Glossary

Accelerated Mobile Pages (AMP) is a Google initiative launched in 2016 to improve web page loading speeds and user experience. By creating a faster version of web pages, AMP aims to enhance content accessibility on mobile devices. However, despite its intentions, AMP faces challenges in widespread adoption due to issues like duplicated content across AMP and standard pages, and complications in monetization and brand traffic.

For content creators, the appeal of AMP lies in its potential for faster loading times, which can improve user satisfaction and visibility on Google, especially for sites with complex or inefficient code. AMPs can be relatively simple to implement, offering significant user experience benefits with Google's infrastructure. Nevertheless, criticisms include traffic redirection away from publishers' sites and difficulties in generating revenue. Despite its advantages in user experience and search visibility, AMP's limitations in brand control and monetization have hindered its adoption as a dominant mobile web technology.

Account takeover refers to the unauthorized access and use of a user's online account, typically achieved through hacking, phishing, or credential stuffing. This can lead to unauthorized transactions, identity theft, and unauthorized access to sensitive information, causing financial loss and damage to the user's reputation.

Account-Based Marketing (ABM) is a personalized, account-specific marketing strategy that focuses on engaging specific high-value accounts rather than individual leads. Originating in the 1990s and popularized in the mid-2000s, ABM aligns marketing and sales efforts to create personalized campaigns targeting key accounts identified through data analysis. This approach has grown in popularity due to its effectiveness in driving high ROI and increased sales by building direct relationships with businesses.

ABM operates through a collaborative strategy involving both marketing and sales teams to identify potential revenue-generating accounts, followed by the creation of personalized engagement campaigns. These can include targeted emails, custom landing pages, and personalized sales tactics like calls or meetings, all designed to nurture relationships and drive sales.

Key benefits of ABM include higher ROI, increased efficiency by focusing on fewer, more relevant accounts, improved customer relationships through personalized engagement, and enhanced collaboration between marketing and sales teams. Examples of ABM tactics include personalized email campaigns, customized landing pages for key accounts, targeted ads, and tailored sales demos.

Best practices for ABM involve clearly defining target accounts, creating personalized campaigns, collaborating closely between marketing and sales, using data-driven insights for decision-making, and continually measuring and optimizing campaign effectiveness.

Tools like Salesforce Pardot, Terminus, and Engagio support ABM strategies by offering features for targeted advertising, campaign personalization, and integration with CRM and marketing automation tools.

ABM differs from inbound marketing by focusing on specific accounts with personalized approaches rather than attracting a broad audience. While traditionally associated with B2B companies, ABM can also be beneficial for B2C scenarios, especially for targeting high-value customer segments. The success of ABM relies on clear target account definition, high levels of personalization, marketing and sales collaboration, data-driven decision-making, and regular campaign analysis and optimization.

Active users refer to the number of unique users who engage with an app or a website during a predefined period of time, and is a metric designed to measure growth, churn, and product stickiness.

What is an active user?

As a marketer looking to encourage growth, or a product manager after maximizing retention, you not only need to know how often users access your app/website, but understand the depth of their use and level of engagement.

What do we mean by engagement?

In the app world, the most common example of an active user's engagement is an account log-in. However, engagement can differ across industries and business models.

An online banking app, for example, could define engagement as making a transfer. An eCommerce app, on the other hand, could define this as adding an item to a cart, whereas a SaaS company could focus on software usage.

Either way, active users are identified through a personal unique identifier, which could be their email, user ID, cookies – when dealing with web users – or a combination of all, in case one fails.

An advertising budget is the total amount of money a company allocates for advertising, playing a vital role in the effectiveness of pay-per-click (PPC) campaigns by determining their scope and reach. Setting an effective ad budget involves considering several factors, such as campaign objectives, target audience size, industry competition, and financial resources.

To determine the right ad budget, its crucial to first identify the campaign goals, whether its increasing brand awareness, driving website traffic, or generating leads. Understanding the target audience and competitive landscape helps in deciding how much to invest in advertising efforts. Starting with a modest budget and adjusting based on performance is often more practical than committing a large sum upfront.

Effective ad budget management includes strategies like bid management to automate keyword and ad placement bidding, ad scheduling to display ads at optimal times, and geotargeting to show ads in specific locations. Regularly measuring the performance of your ad budget through metrics such as cost per click (CPC), click-through rate (CTR), and conversion rate is essential to ensure a good return on investment (ROI).

Ad budget optimization maximizes PPC returns by making data-driven decisions on budget allocation for optimal results. It balances reaching the target audience without overspending on ineffective clicks, improving cost-per-acquisition (CPA) and return on investment (ROI).

Key Steps for Optimizing Your Ad Budget:

1. Set a Realistic Budget. Align your budget with business goals, considering profit margins and marketing objectives.
2. Identify Your Target Audience. Use demographic and psychographic data to fine-tune your targeting settings.
3. Monitor Advertising Data. Utilize analytics to track ad performance, focusing on metrics like click-through rate (CTR), conversion rate, and cost-per-click (CPC) to identify improvement areas.
4. Experiment with Ad Copy and Landing Pages. Test different ads and landing page variations to discover what resonates best with your audience.
5. Implement Invalid Traffic Prevention Tools. Use tools like Kaminari Click to safeguard against click fraud and other invalid traffic, enhancing ROI and campaign effectiveness.

By following these steps, advertisers can enhance campaign performance, increase efficiency, and achieve better results from PPC advertising efforts.

Ad creative, also known as mobile ad creative when related to mobile devices, refers to the visual and textual elements of an advertisement designed to capture the target audience's attention and persuade them to take a specific action. An ad creative plays a crucial role in determining the success of an advertising campaign, as it helps attract more clicks, generating higher engagement rates and ultimately leading to more conversions.

Ad creative may include images, videos, animations, text, and other interactive elements to convey a message and promote a product or service. A great ad creative helps catch the attention of your readers, educates your readers about what you're offering and who you are.

Most importantly, it guides them to taking the next steps to convert from a lead to a customer.

Ad delivery refers to the process of delivering advertisements to the intended audience through various channels, such as websites, social media platforms, or email. In the context of pay-per-click (PPC) advertising, ad delivery refers to the process of serving ads to users who have shown interest in a particular product or service, based on their online search behaviour or other factors.

Types of ad delivery that are commonly used in PPC advertising

Real-time bidding (RTB) is an automated process that enables advertisers to bid on ad impressions in real-time, through an ad exchange. RTB uses algorithms to match the right ads to the right users, based on the user's online behaviour, location, and other factors. RTB allows advertisers to target specific audiences, which can improve the relevance and effectiveness of the ads.

Cost-per-click (CPC) is a pricing model that charges the advertiser every time a user clicks on an ad. In CPC ad delivery, the ads are served to users based on the keywords they have searched for or the websites they have visited. The advertisers set a maximum bid for each keyword, and the ads are displayed to users based on the bid amount and the relevance of the ad to the user's search query.

Cost-per-impression (CPM) is a pricing model that charges the advertiser for every thousand impressions (views) of an ad. In CPM ad delivery, the ads are served to users based on the user's location, demographics, and other factors. The advertisers set a maximum bid for each impression, and the ads are displayed to users based on the bid amount and the relevance of the ad to the user's interests.

An ad exchange is an online platform for buying and selling advertising inventory across websites, mobile sites, and mobile apps. Think of it like a digital marketplace, where the deals take place automatically and in real-time.

Ad exchanges play a key role in the world of programmatic advertising, which automates the bidding process for digital ad space. They connect the supply side — parties with ad space to sell, including publishers (website or app owners) and advertising networks (more on these below) — with the demand side (advertisers or agencies wanting to place ads).

Ad extensions enhance PPC ads by adding extra information like text, links, or media, offering more context and value to users. These extensions, crucial for modern PPC campaigns, can highlight features, offers, contact info, or direct users to specific web pages, improving ad performance and user experience.

Types of Ad Extensions:

• Sitelink Extensions. Add links to specific pages on a website, useful for e-commerce by directing users to product pages or other relevant content.
• Callout Extensions. Short texts highlighting offers or features, helping differentiate ads.
• Structured Snippet Extensions. Lists specific items or services offered, suitable for businesses with a wide range.
• Location Extensions. Display business address and phone number, beneficial for local businesses attracting nearby customers.
• Call Extensions. Include a phone number in ads, allowing direct calls, valuable for service-based businesses.

Benefits:

• Improve Ad Performance. By providing additional details, extensions attract more clicks and enhance ad effectiveness.
• Increase Visibility. Extensions make ads more prominent on search results pages, helping stand out from competitors.
• Enhance User Experience. Offering more information and options improves user satisfaction and action-taking ease.

Best Practices:

• Relevance. Ensure extensions are relevant to the ad and business.
• Specificity. Use focused extensions rather than generic phrases for more qualified clicks.
• Testing and Optimization. Regularly experiment with different extensions to find the most effective combination.

Ad fraud is an attempt to deceive advertising platforms into thinking that fake activity on the network is real user behavior for the purpose of financial gain. Malicious actors typically use bots in order to implement ad fraud, although there are a variety of other methods used to get advertisers and ad networks to pay them for fake activity including some that involve real humans.

In short, ad fraud refers to any attempt to disrupt the proper delivery of ads to real users and the intended audience.

With a predicted $44 billion lost to ad fraud in 2022, online advertising fraud remains a major issue for ad networks and advertisers alike.

An ad group is a set of individual advertisements that are grouped together within a paid search campaign. Ad groups are created to organise and manage a group of ads that share similar themes, targeting, and landing pages.

The primary purpose of ad groups is to organise and structure a PPC campaign in a logical and easy-to-understand way. This allows advertisers to focus their efforts on specific themes, products, or services, and to target their ads to specific groups of users.

Ad impressions indicate the number of times an ad is displayed within an app. They're different from ad clicks, as the number of impressions does not reflect the number of times the ad was clicked.

What is an ad impression?

An ad impression, also known as an ad view, quantifies the number of digital views an advertisement, post, or web page receives. It's a numerical value that tells you how many sets of "eyeballs" have potentially viewed your content.

There has been some debate about the accuracy of ad impressions as a metric, as they don't reflect whether a viewer actually interacted with the content. Regardless, impressions remain one of the most critical digital marketing metrics out there.

Why? Because they tell us how many potential customers have been exposed to a piece of content. And that's priceless information for marketers.

The two types of ad impressions

Served impressions tell you how many times your ad has been delivered to a web page by the ad server. This is the most common way of measuring impressions and is often used as a baseline metric for ad campaigns.

Viewable impressions measure the actual number of times your target audience sees the ad.

Ad inventory is the total amount of advertising space a publisher puts up for sale. The term originally came from print media, but now it primarily refers to internet ad space. Ad publishers can sell inventory to marketers in multiple formats, including desktop websites, mobile websites, mobile apps, and video ads.

Increasingly, app marketers buy ad inventory through advertising exchanges and intermediary markets. This ties in with programmatic advertising, which is when marketers use software to buy ads. Traditionally, you bought ads by manually contacting representatives at a publisher, but now you can buy ads with algorithmic software. As a result, marketers can purchase ad inventory much more quickly as it becomes available.

Ad mediation platforms enable publishers to sell ad inventory efficiently by having ad networks compete for space, ensuring the highest bid wins, which boosts fill rates and eCPMs. In-app header bidding allows real-time bidding by multiple advertisers for ad space, with the highest bidder paying slightly more than the second-highest bid. This method ensures publishers maximize revenue and advertisers pay fair rates.

Waterfall bidding, an older method, sequentially selects ad networks based on performance or preference, often used for quick remnant inventory sales. It involves the ad mediation platform ranking ad networks by performance, then displaying the winning ad via the SDK.

When choosing among popular ad mediation platforms like AdMob by Google, ironSource, MoPub, and Max, consider the supported ad networks, server reliability, campaign optimization features like audience segmentation and testing, and the quality of customer support and data safety protocols. These criteria help select the best platform for maximizing ad revenue and campaign performance.

Ad monetization refers to the process of earning revenue from advertising on a website or mobile app. It involves the integration of ads on a digital property, such as a website, mobile app, or social media channel, and the subsequent revenue earning from these ads.

Ad monetization can be achieved through different methods, such as the cost per click (CPC), cost per impression (CPI), or cost per action (CPA) model. Also, it can include different types of ads, such as display ads, sponsored content, affiliate marketing, and video or audio ads.

Ad networks act as intermediaries in the sale of ad inventory between advertisers and publishers. They match advertisers campaign criteria, including budget and target audience, with the most suitable ad inventory from publishers, facilitating deals.

Types of Ad Networks:

• Horizontal Ad Networks. Cater to a wide range of industries, focusing on broad audience reach, ideal for cross-channel campaigns.
• Vertical Ad Networks. Specialize in specific niches like fashion or sports, targeting advertisers and publishers within those sectors.
• Premium Ad Networks. Offer exclusive access to high-quality inventory from top-tier publishers, ensuring better conversion rates for advertisers.
• Inventory-Specific Networks. Focus on specific ad formats, such as video or mobile ads.

Benefits of Ad Networks:

1. Increased ROI for Advertisers, Instant Revenue for Publishers. Ad networks optimize ad exposure, ensuring ads receive maximum views and clicks for advertisers, while publishers generate revenue from designated ad spaces.
2. Expanded Audience Pool. They expand the selection of publishers for advertisers and increase ad inventory exposure to multiple bidders for publishers, enhancing chances of securing high-paying bids.
3. Efficiency in Time and Cost. The automated system of ad networks saves time and effort by filtering and matching ad inventories, ensuring cost-effective deals for both advertisers and publishers.

Ad optimization refers to the process of maximizing the performance of your ad campaigns. This involves analysing the performance of your ads, making changes to improve their effectiveness, and continually testing and refining your approach. Ad optimization is a critical part of PPC advertising, as it can help you to achieve better results from your campaigns, whether your goal is to drive more traffic to your website, increase conversions, or improve your return on investment (ROI).

Ad optimization is crucial for achieving success with PPC advertising. There are several reasons why this is the case, PPC advertising can be expensive, and you don’t want to waste your budget on poorly performing ads. Ad optimization can help you to identify and eliminate ineffective ads, saving you money in the long run.

Ad optimization can help you to increase your click-through rate (CTR), which is a key metric for measuring the effectiveness of your ads. A higher CTR means that more people are clicking on your ads, which can lead to more traffic and conversions.

An ad server is a software platform that manages the distribution of digital advertising campaigns.

An ad server stores variations of an ad campaign’s creative assets, such as imagery, audio, and video files, and selects which versions to serve which customers. Ad servers also have the ability to collect data, such as clicks and number of impressions, that provides insight into an ad’s performance.

In the milliseconds it takes a page to load, an ad server picks the right ad to shoot in a mobile app or website’s available ad slot by choosing from its reserve of available ads.

Ad servers are data-driven matchmakers that connect ads with audiences based on a set of descriptive tags on geolocation, interests, and behaviors.

Ad spend is simply the amount of money you are spending on advertising campaigns. Depending on how you account for ad spend, you may simply measure actual spending on ad placements, or you may include costs for agencies and ad operations personnel.

Ad spend can be measured in multiple ways, including:

• CPM: Cost per thousand impressions
• CPC: Cost per click
• CPL: Cost per lead
• CPA: Cost per action
• CPS: Cost per sale
• CPI: Cost per install

While cost is a simple concept, aggregating cost across multiple channels is not. Often, ad spend data is fragmented, and even when you manage to pull it together it won’t map properly to your revenue data because each of your ad networks uses different schemas or terminology.

Ad stacking refers to a type of mobile ad fraud in which the fraudsters "stack" or hide ads beneath the primary ad that is displayed to users.

The reason that fraudsters use ad stacking is that while only the top ad will be seen by the user, advertisers that have multiple ads stacked beneath will still have to pay for the fake impressions.

For example, if a user clicks or views the top ad and there are other ads stacked beneath, this means that a click or impression will be reported for every ad in the stack. Ultimately, by stacking ads, fraudsters are stealing ad budgets from advertisers and increasing the ad revenue for publishers that may be involved in the scheme.

An ad tag is a code snippet, either HTML or JavaScript, embedded on a website to request ads from an ad server for display. It contains details about the ads format, size, category, and other specifications, and can be placed in the websites header or an iFrame to keep it separate from the main site script.

Role in Mobile Marketing:

Ad tags activate browsers to display ads and can collect data about viewers, playing a crucial role in enabling modern digital marketing.

Common Types of Ad Tags:

• JavaScript Ad Tags. Used for dynamic ad serving, displaying different ads based on user behavior.
• HTML Ad Tags. Simple tags for static ads like banners or text ads.
• Iframe Ad Tags. Allow ads to be displayed in an iframe, accommodating different sizes or shapes.
• VAST Ad Tags. For video ads, adaptable to various formats and sizes.
• Native Ad Tags. Blend with website content, used for sponsored content.
• Dynamic Ad Tags. Change ads in real-time based on user behavior for targeted advertising.
• Rich Media Ad Tags. For interactive ads, including expandable ads or those with videos/animations.

Lifecycle of an Ad Tag:

1. A publisher generates an ad tag and places it on their website.
2. The ad tag prompts the browser to request an ad from the publishers ad server.
3. Optionally, the ad server might consult a Data Management Platform (DMP) to refine ad targeting with user data.
4. The request is then forwarded to the advertisers ad server, which sends back the selected ad to be displayed in the designated spot on the website.

Ad tags are essential tools for all digital marketing players, facilitating targeted and effective advertising strategies.

An advertising budget is the total amount of money a company allocates for advertising, playing a vital role in the effectiveness of pay-per-click (PPC) campaigns by determining their scope and reach. Setting an effective ad budget involves considering several factors, such as campaign objectives, target audience size, industry competition, and financial resources.

To determine the right ad budget, its crucial to first identify the campaign goals, whether its increasing brand awareness, driving website traffic, or generating leads. Understanding the target audience and competitive landscape helps in deciding how much to invest in advertising efforts. Starting with a modest budget and adjusting based on performance is often more practical than committing a large sum upfront.

Effective ad budget management includes strategies like bid management to automate keyword and ad placement bidding, ad scheduling to display ads at optimal times, and geotargeting to show ads in specific locations. Regularly measuring the performance of your ad budget through metrics such as cost per click (CPC), click-through rate (CTR), and conversion rate is essential to ensure a good return on investment (ROI).

Ad units vary in format, each with unique effectiveness for mobile advertising. Here are key types:

• Native Ads. These blend seamlessly with the app or websites design, appearing as natural content. They can be tailored as image or video ads and are particularly effective in long-form content environments.
• Rewarded Ad Units. Offer users in-app rewards for engaging with content, such as completing surveys, playing games, or watching videos. This format encourages user interaction by providing tangible incentives.

Ad viewability measures the percentage of an ad that users actually see. For an ad to be considered viewable, at least 50% of its pixels must be visible for a minimum of one second for display ads and two seconds for video ads, in a supported format. This metric is crucial in ensuring advertisers receive value, with high viewability rates leading to better ROI through increased clicks and conversions.

Importance in PPC Advertising:

In PPC advertising, where costs are incurred per click or impression, its vital that ads are seen by the target audience. High ad viewability indicates campaign success, reducing the risk of ad fraud and boosting industry integrity.

Measuring Ad Viewability:

Viewability is assessed using standards by the Media Rating Council (MRC) and the Interactive Advertising Bureau (IAB), alongside technology and third-party verification to track user interaction and adherence to these standards.

Maximizing Ad Viewability:

• Select appropriate ad formats. Different formats offer varying viewability rates.
• Optimize ad placement. Position ads where users are most likely to see them.
• Improve page load speed. Ensure ads have the chance to be seen before users navigate away.
• Utilize high-quality creatives. Attractive ads are more likely to capture user attention.
• Target the right audience. Ensure ads reach users most likely to be interested.
• Monitor viewability. Kaminari Click strategies based on ongoing viewability metrics.

AdSense is a pay-per-click advertising platform by Google allowing website owners to earn revenue by displaying targeted ads. Advertisers use Google Ads to manage campaigns, targeting specific keywords and demographics. Website owners earn a share of the revenue from clicks on these ads.

How AdSense Works:

AdSense uses ad tags to display ads related to website content. When users click on these ads, the website owner earns revenue.

Ad Types:

• Display Ads. Banner ads, static or animated.
• Video Ads. Pre-roll or mid-roll video ads.
• Native Ads. Ads matching the websites content style.
• Search Ads. Text ads in search results.
• Link Ads. Text ads displayed as links.

Requirements:

• Ownership of the website by the AdSense account holder.
• Compliance with AdSense policies.
• Sufficient content for ad display.
• Minimum traffic requirements.

Benefits:

• Ease of Use. Minimal setup with simple account signup and ad code placement.
• Flexibility. Various ad formats and customization options.
• Targeting. Ad targeting by keywords and demographics.
• Reporting. Detailed performance tracking and optimization tools.

An advertisement (also known as advertising, ad, or advert) is the practice of promoting a product, service, or idea to a target audience through paid media.

Advertisements are created by an advertiser or advertising agency and are designed to persuade the audience to take a specific action, such as making a purchase or downloading an app. Advertising can be conducted through a variety of media channels, such as television, print, and social media, and is used to increase brand awareness, generate sales, and build customer loyalty.

Mobile ad networks link app advertisers with publishers to fill ad spaces with advertiser demand, playing a crucial role in the mobile advertising sector by enhancing advertisers reach, campaign engagement, and app installs. Advertisers typically utilize around seven ad networks to maximize their campaigns effectiveness, depending on their budget and team size. The selection of ad networks is vital, with reliability and results being key factors. The Trackier Performance Index has been a benchmark for identifying top mobile ad networks since 2016.

Traffic quality is another consideration, distinguishing between direct traffic from ad networks and indirect traffic from affiliates, which may vary in quality and control over ad placements. Direct ad networks offer closer publisher-advertiser relationships, potentially reducing fraud risk and ensuring more stable publisher income.

Mobile ad networks provide diverse ad types—like video, social, and native ads—catering to various advertising strategies and audience segments. Certain networks specialize in video ads, known for their engaging nature and higher conversion rates, while others, including major platforms like Facebook and Google, offer video among various ad formats.

In an affiliate program, advertisers pay commissions to external publishers for product sales or lead generation from their referrals. Affiliate fraud involves any deceitful activities aimed at earning commissions from an affiliate marketing program, breaching its terms and conditions.

Analytics is the practice of analyzing data to gain insights for informed decision-making, utilizing techniques like statistics, data visualization, and machine learning to transform data into useful information for success.

Mobile analytics focuses on analyzing data from mobile devices to enhance mobile app performance. It measures metrics such as user engagement, app functionality, and financial metrics, aiding developers and marketers in optimizing user experiences and making strategic improvements.

For marketers, mobile analytics is crucial due to the increasing prevalence of mobile devices. It helps:

• Optimize user experiences by analyzing user behavior to identify improvement areas.
• Increase conversion rates by identifying and addressing obstacles in user actions.
• Understand customer demographics for tailored marketing strategies.
• Evaluate marketing campaign effectiveness to optimize resource allocation and ROI.

Biased attribution occurs when attribution platforms, also providing traffic and conversions, may favor attributing results to their services over competitors. This conflict of interest can skew accurate performance assessment. While platforms like Google and Facebook offer valuable analytics, using a third-party provider like Singular ensures unbiased ad performance insights, allowing for accurate ROI calculation and optimized ad spend.

Common Biased Attribution Examples:

• Using multiple ad networks. Misattribution occurs when self-attributing networks claim conversions that result from other networks impressions or clicks.
• Correlation-based bias. Incorrectly assuming one event causes another, leading to misattribution.
• In-market bias. Attributing a conversion to an ad when the consumer was already intending to purchase.
• Cheap inventory bias. Misattributing high conversion rates to ad effectiveness when it may be due to lower-priced products.
• Digital signal bias. Favoring online activity in attribution over offline sales, skewing data towards digital.

To counter these biases, marketers employ third-party attribution services for a comprehensive view of ad performance.

How Singular Addresses Biased Attribution:

Singular offers unbiased attribution by measuring across all channels, tracking ROI by integrating cost data, and considering the entire customer lifecycle through advanced tracking techniques. This approach provides marketers with an accurate view of campaign effectiveness, aiding in better decision-making and ROI improvement.

Attribution fraud is a type of mobile ad fraud where criminals attempt to steal credit for app installs, by reporting fake clicks as the last engagement right before the app is first launched by a legitimate user.

Attribution fraud tricks attribution platforms to associate an organic install or an install created by another source, to the fraudster, and by doing so, manipulating the "last-click-attribution" model commonly applied by attribution providers.

Commonly triggered by a form of malware installed on the user’s device, the program "listens" to the user’s activity and is notified once a new app install begins.

The malware will then search for campaigns relating to the relevant app, populating the relevant information into a fake click report, and then registering the last click engagement to win the attribution for an otherwise organic install, or one generated by another media partner.

Attribution modeling is a way of measuring the impact of different marketing efforts across the customer journey, so that advertisers can assess which channels or campaigns are most effective in driving conversions.

As they engage with a brand, users are exposed to various marketing touchpoints — both paid and organic — which can influence them to take a particular action (installing an app or making a purchase, for example). Attribution models analyze interactions with these touchpoints, and work out their contribution to the decision-making process.

Attribution modeling can be single-touch, analyzing the effectiveness of a single click, or multi-touch. Multi-touch attribution models (MTAs) help marketers understand how consumers make decisions across multiple brand engagements over time.

Audience targeting is a technique used to reach specific groups likely interested in a product or service, leveraging demographic, geographic, psychographic, and behavioral data. Its crucial for PPC campaigns, enhancing ad performance by focusing on the most relevant audiences to increase traffic, conversions, and ROI.

Types of Audience Targeting:

• Demographic Targeting. Focuses on age, gender, income, etc.
• Geographic Targeting. Targets users based on location.
• Psychographic Targeting. Targets based on personality, values, and lifestyles.
• Behavioral Targeting. Targets based on online behavior and interactions.

Benefits:

• Improved Ad Relevance. Makes ads more applicable to the audience.
• Cost Savings. Reduces wasted ad spend by targeting relevant users.
• Better Conversion Rates. Increases the likelihood of desired actions.
• Increased Brand Awareness. Reaches relevant audiences unfamiliar with the brand.

Automated requests made to a website, mobile app, or API that are triggered by an automated process (bot) rather than a real human user. In the context of ad fraud, this non-human traffic is designed to mimic real user behavior and inflate audience numbers.

Automated traffic refers to website visits not made by real humans but by various sources like search engine crawlers, uptime checkers, and automated scripts. Often, its driven by bots aiming to boost ad impressions and website traffic artificially.

Automatic bidding is a feature on many online advertising platforms that optimizes bid amounts to improve the chances of winning ad space. Advertisers set a maximum bid, and the Kaminari Click platform bids based on auction competitiveness and conversion likelihood.

How It Works:

Advertisers choose a bid strategy from options like Target CPA (cost-per-acquisition), Target ROAS (return-on-ad-spend), Maximise Conversions, and Enhanced CPC (cost-per-click), each tailored to different campaign goals.

Benefits:

• Saves time. Automates the bidding process, freeing up time for other business activities.
• Reduces costs.
• Improves ad space winning chances.
• Increases conversion rates.

Risks:

• Potential overspending. Without careful monitoring, spending may exceed budget.
• Possible reduced ad effectiveness. Inaccurate conversion predictions can affect ad performance.
• Loss of campaign control. Over-reliance on automatic bidding might reduce hands-on campaign management.

Setting Up Automatic Bidding:

1. Choose a bid strategy. Select one that aligns with your campaign objectives.
2. Set a maximum bid. Determine the highest amount youre willing to pay per conversion.
3. Monitor your campaign. Regularly check performance to ensure it meets your expectations, focusing on costs and conversion rates.

A bid request is a piece of code used to sell display ads and inventory details. It allows visitors to see ads most suited to them, and for multiple advertisers to utilize the same ad spot on a given publisher’s platform.

Computers are extremely fast at processing data and making decisions. With bid requests, the exact ads on the page aren’t determined until visitors reach the app or website. As soon as they do, the bid request is automatically generated to provide information about available impression inventory and consenting visitor demographics (age, gender, location, visited websites, etc.).

This code is used in header bidding, exchange bidding, and real-time bidding (RTB).

A blacklist in the world of mobile fraud describes a database of known fraud signal providers.

Any publisher or source who’s behavior matches known fraud patterns is reported as mobile fraud, and blocked when possible.

Many anti-fraud solutions buy or maintain their own blacklists of Device IDs, bot signatures, and IP addresses known to perpetrate fraud. Once added to a blacklist, all clicks and installs from a blacklisted source are automatically blocked.

Why are blacklists important? It is both cheap and easy for criminals to change both their IP addresses and their Device IDs, evading the real-time protection offered by most blacklists.

Additionally, only data providers with massive, real-time databases and advanced machine learning capabilities, can identify fraudulent devices and IP addresses quickly and efficiently, to deliver meaningful real-time protection.

Similarly, many fraud providers blacklist only a couple of bot signatures, as developing the required database for bot signatures requires massive resources.

Fraud bots in mobile marketing, known as malicious bots, are programmed to simulate false user activities in mobile apps, skewing data for financial gain. These bots impersonate real users, conducting fraudulent installations, ad interactions, and in-app engagements to distort marketing metrics.

How Bots Operate:

Fraudsters employ emulation software to create bots that mimic user behavior, engaging in install fraud, click fraud, and ad stacking fraud. These bots are designed to evade detection and adapt to new user behavior trends, continually updating their databases to avoid being caught.

Types of Fraud Bots:

• Click Fraud Bots. Generate fake clicks on mobile ads by imitating real user interactions.
• Install Fraud Bots. Simulate app installation processes to falsely claim conversion credits.
• App Engagement Fraud Bots. Fake in-app behaviors, such as ad views or in-app purchases.
• SDK Spoofing Bots. Impersonate an app by hacking its SDK, performing false activities and sending fraudulent in-app activity reports.

These bots aim to deplete marketing budgets, wrongly attribute commissions, and provide inaccurate marketing metrics.

Protection Against Bot Frauds:

• Use Closed Source SDKs. Prevent fraudsters from accessing and exploiting the SDK code.
• Challenge-Based Detection. Employ challenges like CAPTCHAs that are easy for humans but difficult for bots.
• Track Bot Signatures. Blacklist IPs and accounts known for malicious activities.
• Monitor for Suspicious Activities. Constant vigilance for anomalies, such as an unusual number of clicks in a short timeframe, to detect fraud.

Marketers must actively protect their campaigns against evolving bot fraud to preserve the integrity of their marketing data and budget.

Brand safety refers to the measures taken to protect a brands reputation during online advertising or digital campaigns. This involves ensuring ads do not appear alongside inappropriate content that could harm the brands image among consumers.

The importance of brand safety cannot be overstated. Many small and medium-sized publishers might not recognize its significance, leading to lost advertising revenue opportunities. Large publishers, such as news and magazine websites, also face challenges in producing content without triggering blacklisted terms, affecting their monetization capabilities. Furthermore, brand safety protects publishers from threats like ads for illegal downloads, fraudulent e-commerce, and invalid traffic, all of which can diminish a publishers brand equity. Therefore, a comprehensive understanding of brand safety is essential for publishers to safeguard their revenue and reputation.

Browsers can load certain content on a website before the user accesses and interacts with it. This is done to speed up fetching the content and provide the user with a seamless experience on a website.

However, this preloading and rendering of content can result in an ad impression that the user never viewed or accessed.

CPA fraud (cost per action fraud) is a type of click fraud where fraudulent actors manipulate the mobile attribution process to earn commissions for actions that real users did not actually complete. This fraud can take many forms, including the use of bots, click injection, and device spoofing. In some cases, fraudsters may use these methods to artificially inflate the app install or in-app event numbers to earn more commissions from advertisers.

One of the most common methods of CPA fraud is through the use of bot traffic. Fraudsters will use a network of bots to simulate real user activity and generate fraudulent conversions. For example, a bot might simulate a user clicking on an ad, completing a form, or making a purchase. The publisher will then be paid for the fraudulent action, even though it was never taken by a real person.

Campaign optimization is the process of refining marketing campaigns to maximize return on investment (ROI).

Types of Optimization:

• Organic Internet Marketing Optimization. Enhances visibility and drives traffic through organic content marketing strategies.
• Email Campaign Optimization. Improves email strategy elements to boost engagement, including subject lines and timing.
• SEO Content Optimization. Increases a websites search engine visibility through content, meta tags, and backlinks.
• Ad Optimization. Focuses on targeting the right audience to increase actions like clicks or conversions, involving campaign budget management and conversion rate optimization (CRO).

How to Optimize a Campaign:

1. Define Objectives. Establish clear goals and KPIs.
2. Establish Attribution. Use tools like Kaminari Click for accurate campaign performance insights.
3. Launch and Diversify Campaigns. Use a mix of paid and organic channels.
4. A/B Testing. Test different marketing elements to identify what works best.
5. Analyze Performance. Regularly review campaign analytics to spot trends and areas for improvement.
6. Iterate. Continually refine your campaign strategy.

Click spamming is a type of online fraud that occurs when spammers send large numbers of fake clicks to ads in order to generate revenue for the advertiser. This type of click fraud is a serious issue for online advertisers, as it can lead to wasted ad spend and decreased ROI.

How click spamming works

Click spamming typically occurs when spammers use automated software to generate fake clicks on ads. These clicks can come from a variety of sources, including bots, infected computers, or even real people who have been paid to click on ads.

Last-click attribution is one of marketing measurement analytics models that advertisers can use to measure performance of their advertising campaigns. Since the customer journey often involves multiple platforms and devices, attribution models are how advertisers determine which keyword, ad platform, or device led to the eventual sale. When marketers are using a last-click attribution model, the credit for a conversion is attributed to the final ad clicked before a sale or goal completion.

Click farms, in the most general sense, refer to any physical location set up with devices to generate clicks, in bulk (and usually not for good purposes).

What are click farms?

Click farms (aka device farms or phone farms) are physical locations stocked with real mobile devices used to perpetrate mobile click fraud.

By repeatedly clicking on mobile ads, click farms drain display-based marketing campaigns.

How do click farms work?

Fraudsters operating these farms own and operate real applications which they use to publish ads. Cheap laborers or machines are used to engage with ads and install advertiser’s apps.

Click farms usually try and tap into campaigns in regions with lucrative payouts by changing their IP addresses using VPN and Proxy software, often hiding their activity behind Limit Ad Tracking and DeviceID Reset Fraud.

There are many types of ad fraud that impact advertisers, although click fraud is one of the most common. Click fraud refers to a type of digital fraud in which bots pretend to be real people and repeatedly click on ads. In some cases with poor attribution technology, they can then seem to be the click that resulted in an app install, a site visit, or a marketing conversion.

Click fraud has long been a problem online. With online pay-per-click (PPC) advertising, website owners are paid based on how many users click on the ads on their site. By using bots to impersonate real user activity and click on ads, the malicious actors are able to both waste advertisers' PPC budget and also generate revenue for website owners. Since these clicks are carried out by bots, click fraud typically occurs on a large scale and each ad is repeatedly clicked in an automated process.

Click fraud is also a problem for mobile app marketers, because fraudsters hope that click spamming can lead to view-through attributions or last-click attributions.

Click injection is an Android-specific form of mobile ad fraud where a click is triggered just before an app is fully installed so that the fraudster will get credit (rather than the real media source or ad network).

What is click injection?

A sophisticated form of click spamming, click injection will use an app located on the user’s device which “listens” to app installation broadcasts.

The fraudsters will be informed when new apps are installed on the device, triggering a click before installation is completed, enabling them to take credit for the install.

How does click injection work?

Fraudsters will often use "junk apps" located on the user’s device, remaining dormant until the installation broadcast "wakes it up" to hijack the user’s device and generate the click, stealing credit for an organic install or non-organic install generated by another network.

Beyond being an advertising budget theft, this method has more severe implications on the advertiser’s future targeting and segmentation of traffic, affecting ad spend planning and distribution, by highlighting a fraudulent source ahead of a legitimate one.

Click redirection is a type of click fraud commonly found on mobile web, where publishers run a script that causes the first click on their site, or the first click on a link on their site, to load a third-party page.

What is click redirection in mobile ad fraud?

Sometimes referred to as "automatic redirection," click redirection is a type of mobile ad fraud where, even if a user didn't click on a certain ad, a script is triggered to redirect them to the ad's landing page.

If the user ends up downloading and installing an app then that conversion event is falsely attributed to the bad actor (as opposed to the legitimate media source where the user originally saw the ad).

Click to install time, or CTIT for short, describes the interval of time between when user clicks on an ad and an app is opened. It’s a very handy metric to look at in detecting and preventing mobile ad fraud perpetrated by bad actors looking to fake last clicks.

What is it?

CTIT is a type of distribution modeling that is used in the detection of install hijacking and click flooding.

While install hijacking CTIT analyses typically looks for a volume of installs during the first 3-10 seconds after the click, click-flooding CTIT analysis looks for nearly flat distribution at scale between hours 2 and 24, as well as between days 2 and 7 after install.

In order for a user to click on an ad, they must be shown the ad before. While this sounds like simple logic, currently, ad partners are free to log in click data without providing the mobile measurement partner (MMP) a verification of matching impressions. Hence, fraudulent ad partners can easily get away with click fraud, and marketers have to waste their ad budget paying the fraudsters. Click validation is a protection tool that prevents this issue, as it requires all ad partners to provide their MMPs with matching impressions to their click data.

Click-through rate (CTR) in mobile marketing metric that measures the effectiveness of a mobile ad in terms of its ability to generate clicks. It can be calculated by dividing the number of clicks an ad generates by the number of impressions it receives, expressed as a percentage.

CTR is a good indicator of the effectiveness of a mobile ad. If an ad has a high CTR, it means that a large percentage of people who saw it were interested enough to click on it. This can be a positive sign that the ad resonates with its target audience. In contrast, a low CTR could indicate that the ad is not relevant or appealing to its intended audience.

Furthermore, CTR is a key factor in the cost-per-click (CPC) calculation. Many mobile marketing campaigns operate on a CPC basis, where the advertiser pays each time someone clicks on their ad. Therefore, a higher CTR means that the advertiser is getting more bang for their buck, as they are paying for fewer impressions but still getting a good number of clicks.

Additionally, CTR can impact the ranking of an ad or search result. In many cases, a higher CTR can lead to a higher ad ranking, as it indicates to the search engine or ad platform that the ad is relevant and useful to users. This can lead to more visibility and more clicks in the future.

Cohort analysis refers to the process of taking a large group of users or customers and breaking them down into smaller segments (aka cohorts) with certain, specified characteristics over a set period.

How do specific types of users engage with your app? Cohort analysis is a highly effective method for achieving deeper insights into how specific groups of mobile users, with similar characteristics, engage with an app over time. Early in a marketing campaign, cohort analysis is typically used to identify, define and refine KPIs (key performance indicators).It is used to optimize user acquisition campaigns, pinpointing which segments may be underperforming, and where corrective action is needed.

App marketers commonly use cohort analysis to improve user retention and lifetime value (LTV), remarket to high-quality users and scale their base.

It also lets marketers compare apples to apples, making it a reliable way to monitor changes over time.

Cohorts are commonly grouped into when and where users installed an app. For example, an advertiser can create different cohorts based on different regions, and compare the average number of sessions per user for each cohort, over the first 30 days of each user’s activity.

A conversion rate is a marketing metric that shows the percentage of times a user took a desired action.

A conversion rate is used to measure the effectiveness of a campaign or piece of content. Specifically, it shows how often viewers took a desired action such as clicking a link, registering for an event, or making a purchase.

A conversion rate is always expressed as a percentage: the higher it is, the more successful your campaign. Average conversion rates vary by industry, but they usually hover in the low single digits. For instance, just 2% of app downloads typically lead to a purchase. This means a small change in your conversion rate can have a big impact.

Why is your conversion rate important?

Your conversion rate is valuable because it shows you how effective a page or piece of content is.

After all, you don't produce content for fun — it all has a purpose as part of your marketing plan, whether that's getting users to buy, sign up, or complete another action. While metrics like page views or impressions are informative, they don't tell you if your content is doing its job of driving users to act.

Conversion tracking is a crucial aspect of pay-per-click (PPC) advertising. It involves measuring and analyzing the effectiveness of PPC campaigns by tracking the actions that users take on a website after clicking on an advertisement. This allows advertisers to determine how well their campaigns are performing and make necessary adjustments to optimise their efforts and increase conversions.

What is a conversion?

A conversion is any desired action that a user takes on a website after clicking on an advertisement. This could be anything from purchasing a product to signing up for a newsletter.

Advertisers set specific goals for their campaigns, such as increasing website traffic or generating leads, and track conversions to see how well they are meeting these goals.

Why is conversion tracking important?

Conversion tracking is important for several reasons. It allows advertisers to measure the return on investment (ROI) of their PPC campaigns. By tracking conversions, advertisers can determine how much revenue is generated from their PPC efforts and compare it to the cost of the campaign. This allows them to see whether their campaigns are profitable and make necessary adjustments to optimise their ROI.

Cost aggregation refers to the process of collecting and summarizing cost data from various sources, such as ad networks, publishers, and ad channels, into a single, unified view.

How can cost aggregation benefit mobile marketers?

Cost aggregation can benefit mobile marketers in several ways:

Improved transparency By aggregating cost data from multiple sources, mobile marketers can get a clear, comprehensive view of their cost structure, including how much they are spending on each campaign and partner. Having a comprehensive view allows them to make informed decisions about their advertising spend and optimize their campaigns for better results.

Better decision-making With all cost data in one place, mobile marketers can compare performance across different campaigns and identify areas where they can optimize their spending to improve performance. Thus, cost aggregation can help marketers make data-driven decisions and maximize their return on investment (ROI).

Cost models in digital advertising are the methods used to calculate the cost of placing advertisements on digital platforms such as websites, social media, and mobile applications. These models consider various factors, such as the target audience, advertising goals, and budget, to determine the most effective way to charge for ad placements. The cost models vary, with some charging for impressions, clicks, or desired actions such as sales or sign-ups. The choice of cost model can significantly impact a digital advertising campaign's success and return on investment (ROI). Therefore, it is important for advertisers to carefully consider and select the most suitable cost model based on the campaign's specific goals and target audience.

What are some examples of cost models, and how can advertisers use them?

Different cost models focus on different metrics and can be more or less appropriate for various types of campaigns. Understanding the varieties cost models available and when and how to use them is essential for maximizing your advertising budget and achieving your goals.

Below are some of the most common cost models used in digital/mobile advertising:

Cost per mille (CPM)

Cost per mille (CPM) is a model where advertisers pay for every thousand impressions of their ad. Impressions can be calculated based on the number of times an ad is displayed, regardless of whether it was clicked or not. This model is best suited for brand awareness campaigns where the focus is on increasing visibility and reach. The main advantage of CPM is that advertisers can guarantee a certain number of impressions, which can be useful for building brand recognition.

Cost per click (CPC)

Cost per click (CPC) is where advertisers pay for the ad clicks. This model is best suited for campaigns aimed at driving traffic to a website or generating leads. The main advantage of CPC is that advertisers only pay for clicks, which is more cost-effective than paying for impressions.

Cost per action (CPA)

Cost per action (CPA) model is where advertisers pay each time a desired action is taken, such as a sale, lead, or sign-up. This model is best suited for campaigns that generate conversions or maximize the return on investment (ROI). The main advantage of CPA is that advertisers only pay for results, which is the most cost-effective pricing model for generating conversions. In addition, advertisers should beware that the CPA model may cost more than cost models focusing on impressions or clicks because the target action is generally more challenging to generate.

Cost per install (CPI)

Cost per install (CPI) is a cost model where advertisers pay each time a mobile app is installed due to their ad. The CPI model is used for mobile app promotion campaigns. The main advantage of CPI is that it provides a clear return on investment, as advertisers only pay for successful installations.

Cost per engagement (CPE)

Using the cost per engagement (CPE) model, advertisers pay for each engagement with their ads, such as likes, shares, or comments. This model is used for social media advertising campaigns. The main advantage of CPE is that it is cost-effective, as advertisers only pay for engagements.

Cost per action (CPA) is a pricing model in which marketers pay ad networks or media sources when a user takes a particular action (such as completing a purchase or registration) inside of an app, after engagement with an ad. It’s calculated by dividing the advertising cost by the number of actions taken.

That action can be any in-app event — a registration, tutorial completion, or purchase, for example — that is driven by a particular source. The price the advertiser pays for an action completion is fixed at the start.

Tracking your CPA is a good way to measure the effectiveness of a given campaign or media source.

Why is CPA important?

CPA has become an increasingly important part of the mobile marketer’s toolkit. Its main appeal is that it’s a pure performance model that lets marketers pay only for what they really want, rather than wasting budget on passive views that don’t convert.

It’s also great for attribution, as you can clearly see which sources lead directly to valuable actions — helping you make more informed campaign decisions down the line.

Cost per click (CPC) measures the average cost an advertiser pays each time a user clicks on their ad. Also known as pay per click (PPC), CPC is one of several cost models in mobile app marketing.

When an advertiser opts for a CPC pricing model, they select a maximum bid for each click, and the advertising platform charges them the maximum bid or the actual cost per click; whichever is lower. Cost per click is often used in search engine advertising, such as Google Ads, where advertisers bid on specific keywords related to their app. The cost of each click can vary depending on the competition for the keyword, the quality of the ad, and the relevance of the landing page.

CPC marketing is popular because advertisers can easily use this pricing model because it is straightforward, measurable, and provides a clear indication of the performance of an ad campaign.

Cost per mille (CPM), sometimes referred to as cost per thousand impressions, is a pricing model where advertisers pay a fixed rate for every one thousand times their ad is displayed to users, regardless of whether the users take any specific action.

CPC vs. CPM

In the realm of digital advertising, there are a number of cost models that marketers can choose from. The cost per click (CPC) and CPM pricing models are oftentimes weighed against one another.

CPC offers a direct link between investment and tangible actions, making it suitable for campaigns aiming to drive specific user interactions. On the other hand, CPM emphasizes broader visibility and brand awareness, making it a preferred choice for campaigns that prioritize reaching a wider audience without necessitating immediate engagement.

Crawlers, automated bots used by search engines, collect data from the internet to index and monitor web pages, playing a key role in PPC advertising accuracy and fraud prevention.

Key Functions:

• They navigate by following web links and analyzing content, helping search engines provide relevant results and detect PPC fraud.
• They use links, sitemaps, and direct submissions to find new URLs, employing algorithms to assess page relevance and quality.

Importance in PPC:

• Essential for accurate search results and effective PPC campaigns.
• Aid in detecting fraud, such as click fraud, safeguarding campaign integrity.
• Collect performance data, aiding in PPC campaign optimization.

Challenges:

• Limited by page accessibility, possibly excluding some from search results.
• May inaccurately interpret web content.
• Resource-intensive, requiring efficiency measures that might affect indexing frequency.
• Raise privacy concerns, though search engines have protective policies.

Managing Crawlers:

• Webmaster Consoles. Tools for URL submission and crawling issues.
• Sitemap Generators. Assist in creating lists of URLs for indexing.
• Fraud Prevention. Tools like Kaminari Click help combat fraudulent activities.

Customer Acquisition Cost (CAC) is a key performance indicator (KPI) that measures the cost of acquiring a new customer. It is calculated by dividing the total cost of sales and marketing efforts by the number of new customers acquired during a specific period of time.

CAC is often used by businesses to determine the efficiency of their sales and marketing efforts and to identify areas for improvement.

CAC is a measure of the efficiency of a company’s sales and marketing efforts in attracting and converting new customers. It takes into account all the costs associated with acquiring new customers, including advertising, marketing, sales, and customer support.

Daily active users (DAU) is a metric that measures the total number of users that log in and engage with your product daily. An “active user” refers to one unique user logging in. Tracking daily active users can provide insight into the number of people who use and value your products or services.

Why is it important to measure DAU?

What’s rather important to keep in mind is that you measure daily active users to understand what your customers are using your product for. You may have an idea as to what you think they should be doing, but in reality, it’s not until you measure and analyze data that you’ll uncover how your product is being used. SaaS brands alike need to understand this metric to uncover areas for improvement and spot when and where churn is about to happen. Doing this will reveal hurdles, drop-off points, and features in your product that may not be necessary, or too complicated. Marketers also use it to properly shape your campaigns in the future towards the most popular aspects of your product among different demographics.

Deep links are a type of link that send users directly to an app instead of a website or a store. They are used to send users straight to specific in-app locations, saving users the time and energy locating a particular page themselves – significantly improving the user experience.

Deep linking does this by specifying a custom URL scheme (iOS Universal Links) or an intent URL (on Android devices) that opens your app if it’s already installed. Deep links can also be set to direct users to specific events or pages, which could tie into campaigns that you may want to run.

Deep links produce a seamless user journey that reduces churn and increases the likelihood of an install. They let you make sophisticated campaigns while providing a better user experience, moving users onto your app in a single click.

Deep links also create the opportunity for easier incentivization. It’s simple to persuade people to try a new experience when a potential prize or offer is sent to them via a retargeting campaign.

A demand-side platform (DSP) is a type of software that allows an advertiser to buy advertising with the help of automation. Because they allow mobile advertisers to buy high quality traffic at scale with minimal friction, DSPs are a powerful marketing automation tool.

There are two important stages to how a demand-side platform works.

First, the advertiser uploads creative, sets up targeting and puts down a budget for their campaigns. This can all be done via the dashboard. Once the campaign creative is uploaded, the DSP scours through its network of publishers for sites and mobile apps that fit the advertiser’s criteria and makes a bid for placement. After this, the DSP resolves the bid, places the ad, and manages payment – all in a matter of milliseconds.

Why are demand-side platforms important? The most obvious way in which DSPs are important to mobile marketing also applies to automation in general: this process allows marketers to avoid spending time and energy on something that can be completed by an automated machine.

Rather than manually contacting hundreds of publishers with offers to advertise, DSPs help advertisers quickly set up campaigns and manage them with ease. This allows user acquisition experts to spend more time working on other valuable areas – such as user base segmentation – to improve performance long-term.

Another reason why DSPs are proving particularly useful to mobile advertisers is that campaign performance can be managed in real time. Instead of having to wait for a campaign to end, mobile advertisers can easily adjust campaigns from DSPs without causing disruption.

A device ID is a unique, anonymized identifier for every smartphone or tablet, used by apps for server communication. With growing privacy concerns, the industry is shifting towards aggregated rather than user-level data. This shift was highlighted by iOS 14.5's launch in April 2021, introducing Apple’s App Tracking Transparency (ATT) framework, requiring developer permission to access a user’s device ID.

Types of Device IDs:

• iOS. Known as the Identifier For Advertisers (IDFA), it comprises a unique alphanumeric sequence. Post-iOS 14.5, users can choose to share their IDFA with apps.
• Android. Known as the Android Advertising ID (AAID), it can be found and reset in the device's settings. Android 12 limits AAID access for users who opt-out of ad personalization, marking them as ‘Limited Ad Tracking’ users.

Importance of Device ID:

Device IDs allow for precise tracking of mobile user behavior and segmentation into cohorts by regions or devices, facilitating detailed behavior analysis and improved ad targeting. For publishers, tracking device IDs helps in measuring ad interactions and user engagement within apps.

Device ID reset fraud is a mobile fraud in which a fraudster resets the device ID of a stolen or compromised mobile device to make it appear as a new device. This allows the fraudster to bypass fraud detection systems based on device ID and use the device for fraudulent activities such as creating fake accounts or making unauthorized purchases. Additionally, since the device ID is used to identify a device for ad clicks, app downloads, and in-app purchases, resetting the device ID can also allow a fraudster to make unauthorized clicks, downloads, and purchases.

How does device ID reset fraud work?

To reset the device ID, a fraudster typically uses a technique called "rooting" or "jailbreaking," which allows them to gain root access to the device's operating system and then use specialized software or apps to change the device ID. This can be done by changing the IMEI (International Mobile Equipment Identity) number, MAC (Media Access Control) address, or other unique identifiers of the device.

Once the device ID has been reset, the fraudster can use the device to create fake or duplicate accounts or use automated scripts to generate fraudulent ad impressions or clicks. Device ID reset fraud can enable fraudsters to bypass fraud detection systems based on device ID and generate fraudulent ad revenue. Additionally, since the device ID is used to identify a device for app downloads and in-app purchases, resetting the device ID can also allow a fraudster to make unauthorized downloads and purchases.

Differential privacy (DP) is a mathematical framework for analyzing data while protecting the privacy of individuals in a dataset. It is used in mobile marketing to protect the privacy of individuals while still allowing for valuable insights to be gathered from their data. DP can be especially important when working with large amounts of personal data collected from mobile devices, such as location, browsing history, and app usage data. By applying differential privacy methods to this data, mobile marketers can gain useful insights into consumer behavior and preferences while still ensuring that individual privacy is protected. For example, the technique can mask sensitive information, such as specific location data, or aggregate data in a way that ensures individual anonymity.

Differential privacy is becoming increasingly important for mobile marketers as the amount of personal data collected from mobile devices continues to grow. With the rise of mobile apps and marketing, mobile marketers have access to a wealth of data on consumer behavior and preferences. However, this data often contains sensitive information such as location data, browsing history, and app usage data, which can be used to identify individual users.

Direct traffic refers to visits to a website initiated by directly entering its URL into the browser, without a referral link or search engine involvement. This type of traffic, detected by analytics tools, poses both benefits and challenges for advertisers and PPC specialists.

Sources of Direct Traffic:

• Manual entry of the website’s URL
• Links from non-web documents like PDFs
• Links from mobile apps and messaging apps

Benefits of Direct Traffic:

• Higher conversion rates due to user interest
• More engaged users leading to increased loyalty
• Increased brand awareness and recall

Challenges of Direct Traffic:

• Difficulty tracking performance without a specific source
• Lower volume compared to other traffic sources
• Increased risk of invalid traffic like bots and fraudsters

Optimizing for Direct Traffic:

• Incorporate clear calls-to-action on the website
• Utilize retargeting campaigns to re-engage past visitors
• Utilize email marketing for targeted outreach
• Leverage social media for targeted ads and engaging content"

Display fraud is a type of fraud where criminals defraud advertisers and networks running CPM (cost per mille) and video view campaigns.

What is mobile display fraud?

To understand display fraud, you first have to understand the concept of display advertising. One of the earliest ways for companies to reach or reengage customers online, display ads were placed on sites for which advertisers paid a set price based on a pre-determined number of impressions. This number was usually set to 1,000, which is where the term cost per mille came into play.

Taking an example, if a website charges a $1.00 CPM and a certain ad got 6,000 impressions, the advertiser would pay $6.00. Because more impressions equals more money, fraudsters found ways to generate false impressions (known as impression fraud) to increase the price for advertisers. Other common methods of display fraud include pop-unders and ad stacking.

In the realm of mobile fraudulent impressions are often sent by apps that appear to be otherwise legitimate, bots, and unsavory publishers.

How to detect and prevent mobile display fraud:

You can often spot this type of ad fraud by looking for a combination of high numbers of impressions, low click rates, and substandard install and retention rates for certain media sources, campaigns, SiteIDs, or even geographies.

However, this is no small feat to do on your own. We recommend looking into a fraud solution or a platform that had a fraud solution as a part of its offering.

These types of solutions will help block fraudulent impressions from IP addresses known to send fraud. Some solutions may even allow you to set up automated alerts to look for SiteIDs or networks with particularly low impression-to-click conversion rates.

Duplicate IPs refer to a mobile fraud tactic where an individual or group creates multiple app installs from the same IP address within a short period of time. This tactic is used to artificially inflate the number of app installs, which can be used to manipulate app rankings or fraudulently generate advertising revenue. Duplicate IP is considered a type of fraud because it creates a false representation of the popularity of an app and can be used to deceive users and advertisers.

What is an IP?

An IP, or Internet Protocol, is a label made of numbers assigned to each device connected to a network using the Internet Protocol for communication. It serves two main functions: identifying the host or network interface and providing the host's location in the network. IP addresses are binary numbers. They are generally displayed in human-readable notations, such as IPv4 (a 32-bit address written as four decimal numbers separated by dots) or IPv6 (a 128-bit address written as eight groups of four hexadecimal digits separated by colons). IP addresses are unique, and they allow devices to communicate with each other through a network, such as the internet.

How do duplicate IPs work?

Duplicate IPs work by using automated software or bots to generate multiple clicks and app installs from the same IP address in a short time. This can be done by using a network of devices or virtual private servers connected to the same IP address. The software or bots can be programmed to simulate a user clicking on an ad or installing an app, which allows fraudsters to generate a large number of app installs from a single IP address. This tactic can also be used to create fake reviews, ratings, and engagement on an app to make it appear more popular than it actually is. Additionally, it can be used to game the ad revenue system by artificially inflating the number of clicks and installs, thus generating more revenue for the fraudsters.

Fake bid requests occur when fraudulent entities generate fictitious auction bid requests in programmatic advertising, attempting to mimic legitimate ones from actual advertisers. These deceptive requests aim to mislead advertisers and demand-side platforms into purchasing non-existent or low-quality ad inventory, consequently draining advertising budgets and reducing the efficacy of campaigns.

Fake data refers to fabricated, altered, or misrepresented information used to deceive ad platforms, advertisers, and networks. In an advertising context, fraudsters may generate fake data such as non-human traffic, counterfeit clicks, false impressions, or bogus conversions to illegitimately gain revenue, mislead campaign analytics, and exhaust advertisers’ budgets.

Fake users are often generated through automated scripts or bots, and these fraudulent entities operate to interrupt marketing campaigns and distort performance data. Fake users do not even have the need to employ real human users or devices as most of the user activities are pre-programmed on a software. Essentially, they are faking the entire user journey and generating activities like installs, clicks, and ad interactions that never actually existed. While it is harder to maintain than other types of marketing fraud, if it remains undetected, fake users can scale limitlessly and be a detriment to the entire conversion funnel.

How do fake users harm marketers?

When fake users generate marketing data such as click or install data, it creates zero value as there is no real engagement. However, they are usually able to mimic a user’s entire user journey process and blend in with other data metrics. This makes it very difficult for marketers to pinpoint fake users and are prone to waste their valuable time analyzing fraudulent activities, thinking they are leads.

Once marketers collect user acquisition data that is mixed with fake users, this data becomes worthless and skewed, and marketers have to exhaust their budget paying for completely nonexistent interactions. From draining their resources to harming their reputation and credibility, fake users can take a business down and cause multiple pain points, all of which can become irrecoverable damage overtime.

Fraudster set up fake sites that are made only to serve ads to bots. It is usually a three step process whereby a fake website is created as a first step. As a second step, cheap bot traffic is purchased and routed to the new website. The ad networks see that this site is getting a lot of traffic and include it in their inventory. The third and final step, advertisers buy ad space on the site and the fraudster gets paid. Ad fraud is that easy.

The fill rate is a vital metric for mobile app marketers, indicating the percentage of ad requests that were successfully filled with ads. To calculate it, divide the total ad impressions by the total ad requests and multiply by 100. While achieving a 100% fill rate is rare due to various factors like ad relevance and technical issues, aiming for a rate above 85% is advisable. To increase the fill rate, consider the following strategies:

1. Combat ad fraud. Use fraud prevention tools like Kaminari Click Fraud Prevention to detect and reject fake clicks and installs, safeguarding your marketing budget.
2. Offer multiple ad formats. Provide ad networks with various ad sizes to accommodate different devices, increasing the chances of your ads being served.
3. Ensure compatibility. Ensure your ads are compatible with different operating systems, channels, and mobile ad types to maximize their visibility.
4. Optimize targeting. Continuously A/B test your ads and optimize targeting parameters like demographics and location to engage audiences effectively.
5. Measure comprehensively. Analyze the entire user journey and campaign performance comprehensively using tools for mobile measurement and analytics suite to optimize campaigns for better ad fill rates and user acquisition.

Geo masking involves fraudsters manipulating IP addresses to disguise low-quality web traffic as high-quality to inflate its market value. By misrepresenting the geographical location of traffic, perpetrators can sell it to advertisers at premium prices, especially targeting higher-value regions. This misleading practice not only deceives advertisers into overpaying for subpar traffic, but also adversely impacts the effectiveness and ROI of their digital advertising campaigns.

Geo-targeting delivers tailored content or ads to users based on their location, using technologies like IP address, GPS, or WiFi. It ensures relevance, efficiency, and improved customer experience. Examples include local advertising and event marketing. Limitations include limited accuracy and privacy concerns. Best practices involve using multiple technologies, respecting user privacy, and testing campaigns for optimization. Geo-targeting can be used for SEO and to target users in multiple locations, including those on mobile devices, but targeting users outside the target market is not advisable due to wasted ad spend.

Header bidding, also known as advanced bidding or pre-bidding, is a programmatic method for selling advertising space in real-time. It allows publishers to auction their inventory to multiple ad exchanges simultaneously, maximizing revenue potential. Unlike traditional methods, where ad space is sold at fixed prices, header bidding enables ad networks to compete in an auction, ensuring higher yields for publishers. Additionally, advertisers benefit from accessing impression data to optimize future campaigns.

Previously, the waterfall method was used, ranking ad exchanges by historic yield and offering inventory sequentially. However, this method had inefficiencies, including overlooking higher-paying ad exchanges and causing latency. In contrast, header bidding conducts real-time auctions, eliminating these inefficiencies and maximizing yield.

Header bidding differs from real-time bidding (RTB) in that it utilizes real-time bidding as an auctioning method. When a user opens a webpage, an auction is triggered, and demand partners conduct rapid auctions to establish their highest bid. In-app bidding is similar to header bidding but occurs within mobile apps.

Open bidding, such as Google's open bidding, takes place server-side rather than publisher-side, simplifying implementation. However, header bidding remains the preferred solution despite its complexity and latency issues.

In summary, header bidding revolutionizes the sale of advertising space, offering publishers increased revenue potential and advertisers access to optimized impression data.

Device ID reset fraud is a mobile fraud in which a fraudster resets the device ID of a stolen or compromised mobile device to make it appear as a new device. This allows the fraudster to bypass fraud detection systems based on device ID and use the device for fraudulent activities such as creating fake accounts or making unauthorized purchases. Additionally, since the device ID is used to identify a device for ad clicks, app downloads, and in-app purchases, resetting the device ID can also allow a fraudster to make unauthorized clicks, downloads, and purchases.

How does device ID reset fraud work?

To reset the device ID, a fraudster typically uses a technique called "rooting" or "jailbreaking," which allows them to gain root access to the device's operating system and then use specialized software or apps to change the device ID. This can be done by changing the IMEI (International Mobile Equipment Identity) number, MAC (Media Access Control) address, or other unique identifiers of the device.

Once the device ID has been reset, the fraudster can use the device to create fake or duplicate accounts or use automated scripts to generate fraudulent ad impressions or clicks. Device ID reset fraud can enable fraudsters to bypass fraud detection systems based on device ID and generate fraudulent ad revenue. Additionally, since the device ID is used to identify a device for app downloads and in-app purchases, resetting the device ID can also allow a fraudster to make unauthorized downloads and purchases.

An impression in advertising occurs when an ad is displayed to a user, serving as a key metric to gauge the reach and effectiveness of an ad campaign. It measures the frequency of ad display, while reach indicates the unique audience size reached by the campaign. Impressions are crucial for estimating audience exposure and ROI, aiding advertisers in campaign optimization and trend analysis to enhance performance over time.

Impression fraud, also known as display fraud, is a type of mobile ad fraud that creates fake ad views. This type of fraud causes severe problems for advertisers and ad networks that run ads based on CPM payment model, as it can lead to them paying for ad space that is not being seen by real users.

Impression fraud can be implemented through bots, automated scripts that mimic the behavior of real users, and artificially inflate ad metrics. For example, a fraudster might use a bot to repeatedly load an ad on a website, creating the appearance that the ad has been viewed by a large number of people.

Fraudsters also use malware to commit impression fraud. This malware is designed to hide on a user's device and automatically load ads in the background, creating the appearance that the ad has been viewed by a real person.

Install fraud refers to the act of artificially boosting the number of installs for a mobile app. The goal of this fraudulent activity is to manipulate app store rankings and deceive advertisers into paying for false user engagement. This leads to a distorted view of an app's popularity and engagement, creating a false representation of its value and usage.

How does install fraud work?

Install frauds work by using various technical methods to artificially inflate the number of installs for a mobile application. One of the most common methods is the use of bots. Bots are computer programs designed to mimic human behavior by clicking on ads and downloading apps. The bots are programmed to interact with the app store and perform many installs in a short time, creating a false representation of an app's popularity and user engagement.

Another technique used in install fraud is the creation of fake accounts. Fraudsters create multiple user profiles and use them to download the same app multiple times, thereby artificially increasing the number of installs. They also use these fake accounts to give fake positive reviews and boost app ratings, further misleading advertisers and investors.

Incentivized downloads are another common method used in install fraud. This involves offering users rewards for downloading an app, such as virtual currency or other incentives. While incentivized downloads can be a legitimate way of increasing an app's visibility and attracting new users, they can also be misused to generate fake installs. For example, fraudsters can pay individuals to download the app and then quickly uninstall it, creating a false representation of an app's popularity and user engagement.‍

An install referrer is an ad tracking identifier for Android devices that tracks the source of app installs. It is a unique string sent to the Google Play Store when a user clicks on an ad for an app. This string contains information about the referral source, such as the website or campaign that led the user to the app store and any referral parameters used. When the app is installed, the install referrer is sent to the attribution partner, which is responsible for matching the source of the referral with the actual app installation. This process is called attribution, and it helps app developers understand which marketing campaigns are driving the most downloads. In addition, the install referrer is used to attribute credit for the app installation to the correct referral source, allowing app developers to track their marketing campaign's effectiveness and optimize their strategy for better results.

Why is an install referrer important to mobile marketers?

One of the main benefits of using install referrers is that they allow marketers to attribute credit for app installs to the correct referral source. Knowing the correct referral source is important because it allows marketers to accurately measure the performance of their marketing campaigns and make decisions about where to allocate their marketing budget. For example, if you are a marketer running a mobile ad campaign on two different channels, channel A and channel B, install referrers will allow you to see which campaign is driving the most app installs. This way, you can optimize your campaign on channel A to drive more installs or shift your budget from channel B to channel A if it is driving more installs.

Additionally, install referrers can be used to track the performance of different ad creatives, ad placements, and targeting strategies. This allows marketers to understand which strategies are working well and which ones need to be tweaked or abandoned.

Invalid traffic pertains to any clicks, impressions, or interactions with an online ad that are not generated from genuine user interest. This encompasses a range of fraudulent activities including, but not limited to, bot-driven traffic, manipulative refreshing of pages, and intentional misdirection of users. Invalid traffic ultimately diminishes the efficacy of advertising campaigns by draining ad spend without providing real user engagement or opportunities for conversion.

A key performance indicator (KPI) is a measurable value which shows how effectively an organization achieves its key business objectives. KPIs should be specific, measurable, attainable, relevant, and time-bound. In other words, a good KPI should be clearly defined, able to be quantified, achievable, aligned with the organization's goals, and measured over a specific period of time.

KPI tracking allows organizations to monitor progress toward their goals, identify areas for improvement, and make data-driven decisions. As a result, it is a crucial tool for evaluating the effectiveness of business strategies and for identifying opportunities for optimization and growth.

Why is a KPI important?

KPI helps organizations set clear goals and objectives:

By identifying specific, measurable, attainable, relevant, and time-bound (SMART) KPIs, organizations can set clear goals and objectives for themselves. This helps to ensure that everyone is working towards the same objectives and that progress can be easily tracked.

KPI helps organizations monitor progress:

By regularly tracking and measuring KPI, organizations can monitor their progress towards their goals and identify areas for improvement. This can help organizations to make timely adjustments to their strategies and tactics to stay on track.

KPI helps organizations make data-driven decisions:

By analyzing KPI data, organizations can make informed, data-driven decisions about their business. This can help organizations to optimize their operations, improve efficiency, and increase profitability.

LTV, or Lifetime Value, predicts the net profit attributed to the entire relationship with a customer. It helps marketers understand user value, make informed decisions on acquisition and retention strategies, and optimize user engagement. Calculating LTV involves subtracting Customer Acquisition Cost (CAC) from the Average Revenue per User (ARPU) multiplied by the Average User Lifetime. Another method involves dividing ARPU by churn rate. Marketers can improve LTV accuracy by tracking user behavior and revenue, monitoring acquisition costs, segmenting users, and leveraging Mobile Measurement Partners (MMPs) for data insights.

The Last-Click attribution model credits the final touchpoint in the buyer’s journey as the influencing factor that converted a lead into a customer. What is last-click attribution? Last-click attribution measures which touchpoint, such as an ad, blog, video, or web page, the customer last clicked on before making a purchase. Example: Under the last-click attribution model, you’ll give all the credit for the conversion to the PPC ad and ignore other touchpoints (Facebook ad, organic search).

Limit ad tracking (LAT) is a feature available on iOS devices that allows users to choose whether they would allow advertisers to receive the data generated from their activities and interests. This data is called IDFA (Identifier for Advertisers), and advertisers use it to create personalized ad campaigns. By turning on LAT, the user's device will not have IDFA and will no longer send user data to app developers, advertisers, and other third parties. As a result, the user who turned on LAT will no longer receive personalized ads.

Why is limit ad tracking important?

For users, LAT can be a method that can help to protect their privacy by preventing their data from being collected and shared without their knowledge or consent.

From the perspective of mobile marketers, LAT is an important feature to consider for several reasons.

It can affect the effectiveness of marketing campaigns: When users turn on LAT, their devices will no longer send information about their activities and interests, which means that marketers will have less data to use for targeting their ads, which may lower their campaigns' effectiveness.

Location fraud refers to the deceptive practice of manipulating or falsifying the geographical location data of ad impressions, clicks, or transactions. Fraudsters employ various techniques like IP spoofing or utilizing false GPS data to misrepresent the actual location of a user, thereby making the ad traffic appear more valuable or relevant than it actually is. This fraudulent activity misleads advertisers into believing that their ads are being viewed by users in specific, often more lucrative, geographic areas.

Marketing automation streamlines marketing processes through software, automating tasks like email marketing, social media posting, and ad management. It improves efficiency, segmentation, data integration, and campaign management, leading to revenue growth. Common types include CRM automation, email marketing automation, social media marketing automation, advertising automation, lead nurturing automation, mobile marketing automation, and omni-channel automation. It benefits both B2B and B2C businesses by enhancing lead generation, customer segmentation, lifecycle management, personalized content delivery, customer journey automation, and engagement. To start, define goals, identify the target audience, choose suitable tools, set up campaigns, and continuously test and optimize. Marketing automation is suitable for businesses of all sizes, offering benefits like increased leads, sales, customer loyalty, improved segmentation, and efficient resource utilization. However, it cannot replace the human element of marketing.

Marketing automation is the use of software to automate marketing processes such as customer segmentation, customer data integration, and campaign management. The goal of marketing automation is to streamline, automate, and measure marketing tasks and workflows so you can increase operational efficiency and grow revenue faster.

How marketing automation works

Marketing automation software is typically used to automate repetitive marketing tasks, such as email marketing, social media posting, and ad management. With marketing automation, you can do things like:

• Automate repetitive tasks, like email marketing, social media posting, and ad management.
• Segment your customers and prospects for more personalised marketing.
• Integrate customer data from multiple sources for a complete view of each customer.
• Measure the success of your marketing campaigns.
• Improve operational efficiency and grow revenue faster.

Mobile fraud detection involves utilizing various techniques and technologies to identify fraudulent actions, such as fake impressions, clicks, and installs, within the mobile marketing ecosystem. It encompasses a range of solutions and tools that aid marketers in detecting and preventing fraud. While mobile fraud protection actively blocks fraudulent activities in real-time using rules and signals, certain sophisticated fraud types necessitate advanced detection methods.

Fraud detection solutions leverage big data, real-time machine learning, and artificial intelligence to detect complex mobile fraud schemes, including click redirection, forced clicks, click flooding, click hijacking, install hijacking, install fraud from new devices, fraudulent installs hiding behind Limit Ad Tracking, and mislabeled clicks.

Advanced fraud detection is essential for combating these fraudulent activities effectively and protecting mobile marketing campaigns from financial losses and reputational damage.

Native advertising is a type of advertisement that is designed to blend in with the surrounding content, making it appear as if it is a part of the publisher's website or app. As such, native advertising can take many forms, including sponsored content, sponsored videos, and in-feed ads. The goal of native advertising is to provide a more seamless and natural experience for the user while allowing brands to reach their target audience. The key difference between native advertising and traditional forms of advertising is that native ads are designed to blend in with the content, while traditional ads are designed to stand out.

Why is native advertising important?

As the use of mobile apps continues to rise, mobile marketing has become an increasingly important aspect of any advertising strategy. One of the most well-known and effective ways to reach mobile users is through native advertising, which can provide several benefits for marketers.

First, native ads are designed to blend in with the surrounding content, making them less intrusive than traditional ads. Thus, native ads can lead to higher engagement rates and a better user experience. In addition, native ads are often placed in-feed, where users are already actively scrolling through content. This can increase the chances that users will see and interact with the ad.

Another benefit of native advertising for mobile marketers is that it can be highly targeted. Native ad platforms often allow advertisers to target specific demographics, interests, and behaviors. This means that mobile marketers can reach the right audience at the right time, increasing the chances of a successful campaign.

Native advertising can also be a cost-effective option for mobile marketers. Unlike traditional mobile ads, which can be expensive to produce, native ads can be created quickly and at a lower cost. In addition, many native ad platforms offer a pay-per-click or pay-per-impression pricing model, which can be more budget-friendly for mobile marketers.

Finally, native advertising can also be a great way to drive app downloads. By placing native ads within mobile apps, mobile marketers can reach users already using similar apps and increase the chances of getting them to download their own apps.

OpenRTB is the technology that facilitates real-time bidding (RTB). RTB is the buying and selling of ad impressions via instantaneous programmatic auction.

What is OpenRTB?

Many of the video and display ads you see online result from companies using real-time bidding (RTB) to display their ad to you. RTB — which happens in less than a second — is an integral part of programmatic advertising, an industry that's expected to surpass $200 billion in the US in the near future.

OpenRTB standards supply the protocol for RTB transactions. They include consistent, standardized ways to provide essential information about an ad impression. For example, through OpenRTB, marketers can see publisher information, the sizes and standards for the ad creative, and other information needed before bidding.

The purpose of OpenRTB is to encourage growth in RTB marketplaces by supplying an open industry standard for interoperability and communication between buyers and sellers of digital advertising. It's been around since 2010, when a large group of demand-side platform and supply-side platform (SSP) came together to create the open-source protocol for OpenRTB.

Organic installs are app downloads that occur without direct marketing efforts through paid or owned media campaigns. These downloads result from users discovering the app independently through word-of-mouth or keyword searches in app stores. Unlike non-organic installs, which involve various tactics to persuade users to download an app, organic installs occur naturally.

In terms of measurement, organic installs occur when no specific marketing effort can be attributed to the user journey. Additionally, if a user interacts with an advertisement but downloads the app after the attribution window expires, the install is considered organic. This ambiguity arises because it's challenging to determine whether the download resulted from the ad interaction once the attributable time frame has passed.

Organic installs are significant for ROI and LTV (lifetime value) metrics, as organic users are often considered high-quality. However, achieving organic growth at scale has become increasingly challenging due to the saturation of app stores with millions of apps. Consequently, the organic multiplier, which measures the ability of non-organic campaigns to drive organic installs, has decreased significantly.

Despite these challenges, partnering with a reliable attribution and analytics provider can help optimize spending, maximize ROI, and enhance the quality of non-organic users. This data-driven approach enables app marketers to scale non-organic traffic effectively.

A postback refers to an exchange of data between servers that is used to attribute and report on a user's action and behavior in a website, app, or network. A postback contains the information needed by attribution providers in order to accurately determine what actions a user took on a website or app. Also referred to as a callback, a postback is a type of attribution that notifies an ad network that a user has taken an in-action or performed a conversion event such as an install.

There are two main types of postbacks:

Install postback This type of postback tells the media source that led to an install that it was attributed for the conversion.

In-app postback event This is used to inform the original media source of actions that users take in-app.

Aside from these two main postback mechanism kinds, it's important to note that Apple's SKAdnetwork works differently due to the AppTrackingTransparency update. However, SKAN does also send postbacks with different data payloads, depending on privacy thresholds.

How do postbacks work?

Postbacks send data between platforms involved in the ad delivery process, including ad networks, advertisers, and MMPs, depending on which parties are involved. Below are the steps to activating postbacks and communicating user activity.

• An ad network presents a digital ad to the user.
• The user views the ad.
• If the user is interested, they will engage with the ad. Let's say it is promoting a new gaming app. The user clicks on the ad, which redirects them to the app store install page.
• In the app store, the user installs the app.
• Post-installation, the user opens the app and engages in in-app events like in-app purchases.
• User activity data is sent to the corresponding attribution provider like MMPs that will analyze the data for assigning attributions.
• The MMP detects the ad responsible for triggering an install or in-app event. It sends an install postback or in-app event postback to the ad network that published the respective ad, notifying them of the attribution.

Predicted Lifetime Value (pLTV) is the anticipated or potential worth of a customer, derived from a combination of historical insights and current metrics. It empowers marketers to craft and refine campaigns based on projections of their audience's anticipated consumer behaviors.

Traditionally, Lifetime Value (LTV) represents an estimation of the average revenue a customer is expected to generate throughout their engagement with an app or service. However, with evolving data privacy regulations, measuring LTV becomes challenging due to restricted access to detailed and long-term performance data.

To address this challenge, pLTV utilizes behavioral characteristic clusters to segment the audience. Rather than relying on individual identities, segmentation is based on early-stage interactions with the user funnel. This approach offers insights into the future potential of users to contribute significant value to the business.

The importance of pLTV lies in its ability to provide marketers with predictive analytics that guide campaign strategies. By understanding the potential value of customers based on their behavioral patterns, marketers can tailor campaigns to effectively engage and retain high-value users, even in the face of data privacy constraints.

Privacy preserving technologies (PPTs) are methods and techniques used to protect the privacy of individuals or organizations while still allowing for data collection, storage, and analysis. PPTs aim to protect sensitive information from unauthorized access, use, or disclosure while still enabling the data to be used for legitimate purposes such as research or analysis.

Programmatic advertising is a type of online advertising that uses complex algorithms to automatically buy and place ads.

How programmatic advertising works

Programmatic advertising platforms use algorithms to buy and place ads. These algorithms are based on a set of criteria that the advertiser provide. For example, you can target people who live in a certain area, who have visited your website in the past, or who are interested in a certain type of product. When someone meets your targeting criteria, the programmatic ad platform will automatically place your ad on their screen. This process happens in real-time and is often referred to as "programmatic bidding."

The benefits of programmatic advertising

Programmatic advertising can be a very effective way to reach your target audience. Programmatic ad platforms use algorithms to automatically buy and place ads. This means that you can save time and effort on the manual process of buying and placing ads.

A publisher is a company or individual that owns and operates a website, blog, or app and allows other businesses to advertise on their platform.

There are several types of publishers in the digital advertising industry.

Content publishers include websites or blogs that publish articles, videos, or other types of content. Content publishers often monetize their platforms through advertising, either by selling ad space directly or through an ad network.

App publishers are companies or individuals that develop and distribute mobile apps and monetize them through in-app advertising.

Social media also works as publishers that allow businesses to advertise to their users through sponsored posts, ads, and other types of sponsored content.

Purchase fraud in the context of mobile apps refers to deceptive practices aimed at exploiting in-app purchase mechanisms for financial gain. It encompasses various illicit activities such as utilizing stolen credit cards, exploiting loopholes in return policies, or fabricating fake purchase events.

Similar to fraudulent activities encountered in traditional brick-and-mortar stores and online retail platforms, fraudsters target the point in a user's journey where monetary transactions occur, both offline and in-app.

In mobile marketing, purchase fraud specifically pertains to fraudulent activities related to in-app purchases, which are integral events within the app ecosystem. Fraudsters engage in these practices to capitalize on the potential for substantial payouts from advertisers offering promotions based on lifetime value (LTV) incentives and in-app event rewards.

The types of purchase fraud can manifest in various forms, including the use of stolen credit cards, exploitation of vulnerabilities in return policies, and the creation of fabricated purchase events.

Push notifications are messages that appear on mobile devices, even when users are not actively using an app. They are commonly used by businesses to provide updates, discounts, or time-sensitive information. Push notifications can increase customer engagement, boost retention, and improve conversion rates. Types include transactional, promotional, location-based, behavioral, time-based, re-engagement, and in-app notifications. They help mobile marketers engage and retain customers effectively.

Real-time bidding (RTB) is a form of media buying in programmatic advertising where ads are bought and sold in an instant auction during the loading of a web page or app. It involves demand-side platforms (DSPs) and sell-side platforms (SSPs) facilitating auctions between ad buyers and sellers. Advertisers bid in real-time to display their ads to specific users based on their demographics and preferences. RTB increases efficiency for advertisers by automating the process of accessing ad inventory and allows publishers to maximize revenue by reaching a broader audience. Singular, a leader in marketing analytics, helps advertisers optimize their RTB campaigns by providing accurate ROI insights and cost aggregation tools.

Remarketing, or retargeting, reconnects with users who have interacted with a product, service, or app. It leverages ad views, subscriptions, cart additions, or purchases to engage users. Targeting current or past users aims to maintain brand awareness and increase conversions. With the mobile app market expanding rapidly, remarketing is preferred for its effectiveness in engaging existing users over acquiring new ones.

Mobile remarketing strategies include:

1. Deep linking: Redirects users to specific in-app pages, increasing engagement.
2. Ad frequency caps: Avoids overwhelming users with repetitive ads to maintain a positive relationship.
3. Marketing automation + analytics tools: Streamlines tasks like email marketing and provides insights for effective remarketing efforts.

Benefits of remarketing include:

1. Brand awareness: Increases brand visibility among users, educating them about app offerings.
2. Cost-effectiveness: Targets users already interested in the brand, reducing acquisition costs compared to acquiring new users.

Retention rate is the percentage of users who continue to use an app after a certain number of days post-install. It helps gauge app performance and user engagement over time, with higher rates indicating better user retention and monetization opportunities. Understanding retention rates helps identify areas for improvement and extend user lifetime value (LTV). Improving retention rates involves personalizing user experiences, optimizing messaging, identifying and addressing bottlenecks, A/B testing features, and prioritizing onboarding.

An SDK (Software Development Kit) is a set of tools for developers to create applications for a specific platform. It includes libraries, documentation, sample code, and testing tools. SDKs simplify development, enhance functionality, ensure compatibility, and reduce costs.

Components:

• Libraries: Pre-written code for adding features.
• Documentation: Instructions and examples.
• Sample code: Demonstrations of SDK features.
• Tools: Utilities for building, testing, and debugging.
• Platform-specific components: Additional resources for the platform.

SDKs are important because they streamline development, unlock platform features, ensure compatibility, reduce costs, and facilitate updates.

SDK spoofing is a form of mobile ad fraud where fake installs are generated using data from real devices, without users actually installing the app. Perpetrators manipulate app behavior or SDK code, often through malware, to create fraudulent installs and deplete advertisers' budgets without delivering genuine value.

To prevent SDK spoofing, robust authentication and access controls should be implemented for SDKs, including secure keys and multi-factor authentication. Regular updates and patches to SDKs are essential to address vulnerabilities, and organizations should enforce security protocols, such as employee training and security assessments, to identify and mitigate risks effectively.

SKAdNetwork (SKAN) is a framework for measuring mobile app installs and attribution on iOS 14+. It allows marketers to assess ad campaign effectiveness while safeguarding user privacy. SKAN was introduced by Apple to balance measurement needs with privacy concerns by using unique identifiers and randomized reporting delays.

Key Players:

• Target apps. Developers integrate SKAdNetwork into their apps to send install confirmations to ad networks.
• Publishing apps. Display ads with unique SKAdNetwork IDs assigned to each ad.
• Ad networks. Intermediaries handling ad delivery and receiving install confirmations, attributing installs to ads.
• Mobile Measurement Partners (MMPs). Third-party tools for advertisers to track campaign effectiveness.

Conversion Measurement:

• Publisher assigns SKAdNetwork ID to ads; ad network receives ID when user clicks.
• Install confirmation sent from target app to ad network, including SKAdNetwork ID.
• Ad network attributes install to appropriate ad campaign.
• SKAdNetwork also measures in-app events, limited to 10 events per app.
• Attribution window can be up to 30 days, but postbacks have randomized delays.

Challenges:

• Limited granular data due to inability to collect personal data like user ID or device ID.
• SKAdNetwork conversion value limited to 64 values, limiting post-install activity tracking.
• Randomized reporting delays hinder real-time campaign tracking.

Self-attributing networks (SANs) are ad networks that advertise on their own platform, acting as both publishers and networks. Unlike standard ad networks, SANs do not proactively share data with attribution partners. Instead, they wait for the attribution partner to send the advertising ID obtained via an SDK embedded in the client’s app. SANs then confirm whether they have recorded engagements matching that advertising ID. The attribution flow for SANs involves a user seeing the ad, clicking through, downloading the app, and opening it for the first time.

Sessions represent periods of user interaction with an app or website, encompassing activities like app launches, screen navigation, and actions performed within the app. They allow apps to personalize user experiences, store preferences, and enhance security by tracking user actions. For marketers, session data provides insights into user engagement, helping them gauge app performance, identify areas for improvement, and create targeted marketing campaigns. Understanding session length and frequency aids in optimizing marketing efforts, increasing user retention, and enhancing overall app engagement.

Smart bidding is a form of automated bidding that uses machine learning algorithms to automatically set bids for your Google Ads campaigns. The aim of smart bidding strategies is to help you get more conversions (sales, leads, etc.) at the target return on investment (ROI) you set.

How smart bidding works

Smart bidding works by using data signals and machine learning algorithms to automatically place bids that are designed to get you more conversions at your desired ROI. The algorithms take into account a variety of factors, including but not limited to:

• Your campaign's historical performance.
• The device that searchers are using.
• The time of day that searchers are searching.
• The searcher's location.
• Search queries.
• Ad relevance.
• Your competition.

There are a number of benefits that come with using automated bidding and because the algorithms are constantly learning and adjust bids based on a variety of factors, they can often get you more conversions than if you were manually setting bids.

Smart campaigns are advanced paid advertising strategies that use algorithms and machine learning to optimize campaign efficiency. They're user-friendly and accessible to small businesses. They're often used in PPC advertising, targeting specific keywords and demographics. Key features include automated bidding, precise targeting, ad creative optimization, and detailed reporting.

Advantages:

1. Efficiency. Saves time and effort through automation.
2. Scalability. Easily adjustable to business needs in real-time.
3. Accessibility. User-friendly for businesses of all sizes.

Limitations:

1. Limited control. Less control over targeting and bid adjustments.
2. Less flexibility. May not offer the same flexibility as traditional PPC campaigns.
3. Less granular data. Data may not be as detailed as traditional campaigns.

Best Practices:

1. Set clear goals.
2. Monitor and optimize regularly.
3. Utilize available tools for management and optimization.

Sophisticated Invalid Traffic (SIVT) is a type of fraud that generates fake traffic or clicks on online ads, harming businesses and advertisers. SIVT is distinct from General Invalid Traffic (GIVT), which is non-human activity like bot traffic. SIVT mimics genuine user behavior, making it harder to detect. Types of SIVT include scraping bots, clickjacking, click fraud, click farms, domain spoofing, cookie manipulation, competitor clicks, and self-clicking.

Signs of SIVT on a site include high clicks/views from single IP addresses, unusual locations, odd hours, and atypical browsers. Consequences include loss of advertisers, site suspension, financial loss, and damage to reputation.

Split testing, also known as A/B testing or multivariate testing, is a method used in pay-per-click (PPC) advertising to determine the most effective version of an advertisement or website element by comparing two or more variations.

This technique allows marketers to make data-driven decisions about which version of an ad or website element will perform best with a target audience.

How split testing works

Split testing works by randomly dividing a target audience into two or more groups and showing each group a different version of the ad or website element being tested. The performance of each variation is then measured and compared to determine which version is more effective.

For example, a marketer might want to test the effectiveness of two different versions of a landing page for a PPC campaign. One version of the landing page might have a strong call to action, while the other version might have a more subtle call to action.

The marketer would then split the target audience into two groups and show each group one of the two versions of the landing page. The marketer could then measure the performance of each version by looking at metrics such as conversion rate and cost per conversion.

Split testing can be used to test a wide range of elements, including ad copy, images, call to action buttons, and more. It is a valuable tool for PPC marketers because it allows them to make informed decisions about which elements are most effective at driving conversions.

SSP is a technology platform or software that manages a publisher’s ad impression inventory across multiple ad exchanges. SSPs are effective marketing tools because they automate and optimize the sales of a publisher’s media space, helping to fill it with ads and generate revenue.

SSPs help publishers automate the selling, managing, and optimizing of their ad inventory on the web and on mobile devices.

Before SSPs, publishers needed to manually manage and sell their ad space; however, this left them unable to scale their selling processes and guarantee that ad spaces were filled.

At first, publishers used supply-side platforms to fill leftover inventory at lower prices. However, today SSPs are responsible for the programmatic selling of all ad inventory.

Targeting in mobile marketing refers to identifying and reaching specific groups of consumers who are likely to be interested in your app. This is done by using various data sources, such as location, demographics, app history, and previous purchasing behavior, to create segments of consumers with similar characteristics and are likely to respond to a specific marketing message. Targeting in mobile marketing allows marketers to reach their desired audience more efficiently and effectively while reducing the likelihood of wasting resources on unqualified leads. Marketers can use targeting data for marketing activities such as targeted ads.

Why is targeting important in mobile advertising?

One of the main benefits of targeting in mobile marketing is the ability to reach a highly-qualified audience. By analyzing data from various sources, such as location, demographics, app history, and previous purchasing behavior, mobile marketers can create segments of consumers with similar characteristics and are likely to respond to a specific marketing message. This allows marketers to deliver ads and promotions tailored to their target audience's interests and needs, which can lead to higher engagement and a greater likelihood of conversion.

Another benefit of targeting in mobile marketing is the ability to reduce wasted impressions. When ads are not targeted, they are often seen by a broad audience that may not be interested or qualified to purchase the product or service being offered. Therefore, non-targeted ads can lead to a significant portion of the ad budget being wasted on unqualified leads. However, by using targeting, mobile marketers can ensure that their ads are seen by the right people, which can lead to more efficient use of resources and a higher ROI.

Targeting in mobile marketing can also lead to greater personalization and relevance. With the ability to segment audiences and tailor messages, mobile marketers can create highly personalized ads that resonate with their target audience's specific needs and interests. By doing so, marketers can improve engagement and strengthen emotional connection between the brand and the audience, which can ultimately lead to greater customer loyalty and long-term success.

Trackers are campaign tracking links used in mobile measurement, providing granular data on campaign performance such as clicks, impressions, and user conversions. They help advertisers assess advertising effectiveness and optimize marketing strategies. Trackers are crucial for analyzing user activity throughout the mobile marketing funnel and identifying valuable users.

A tracking link, also known as a tracking URL, monitors user activity by using customized web addresses that are unique to a marketing activity.

Tracking links are created by appending customized codes or IDs to a URL which allows marketers to distinguish each traffic source or user journey. By doing so, marketers are also able to keep track of attribution points and identify sources that led to conversions.

UTM, short for "Urchin Tracking Module," is a code snippet appended to a URL to track the performance of marketing campaigns, channels, or content. Originating from Google's Urchin Tracker, UTMs consist of parameters and tracking variables added after a "?" in the URL. There are five standard UTM parameters: source, campaign, medium, content, and term, each helping to identify different aspects of the traffic source. Accurate measurement requires a standardized naming convention for UTMs.

User acquisition refers to the process of acquiring new users to a mobile app through various marketing activities.

App businesses will typically generate new installs through a combination of paid advertising, organic traffic, and app store optimization (ASO). Ultimately, the goal of user acquisition is to acquire users at a cost that is less than a typical users lifetime value (LTV) or the average revenue per user (ARPU).

For example, if paid advertising is the primary method of user acquisition, this means mobile marketers are looking to generate a positive return on ad spend (ROAS) for their app installs.

Web attribution is the process, and supporting technology, of understanding and attributing the touchpoints of a consumer journey across the web, whether it’s on a desktop or mobile web browser. In the past, web attribution was fragmented with multiple browsers and sessions using different protocols and tools for structure and communication. However, because the consumer journey is also fragmented overall, involving multiple devices (smartphones, tablets, personal computers, etc.), platforms (mobile vs desktop web), and channels (paid, email, social, etc.), web attribution has evolved over the last decade and is being paired with mobile attribution to form holistic attribution solutions.

The number of unique users that engage with your app within a 7-day window. WAU is generally used by businesses where users are expected to interact with the app at a weekly frequency (e.g. analytics tools).

Active users are individuals who engage with your app over a given time period. This could be making a transfer (online banking), adding to cart (ecommerce), downloading or logging into the app (SaaS) — or really any activity that meets your company’s definition of "active" user activity.

WAU is the metric used to track weekly user engagement, including new and existing users.

You can identify your app’s weekly active users through a personal unique identifier like email addresses, IDFA (for consenting iOS 14+ users), and user IDs. In fact, app marketers often use a combination of these as backup in case one fails.

A white-label DSP is a customizable demand-side platform that advertisers and marketers use to manage ad placements. It allows for greater control over ad traffic and customization without relying on third parties. By integrating with multiple ad exchanges and supply-side platforms, white-label DSPs offer access to diverse audience segments. Benefits include full transparency and control over ad traffic, reducing costs in the long run compared to other DSP options. Factors to consider before choosing a white-label DSP include ad spend and platform integration needs. Alternatives to white-label DSPs include full-service DSPs, offering convenience but less control, and self-serve DSPs, offering similar control but with limited customization. Understanding your budget and integration requirements is crucial in optimizing the benefits of a white-label DSP.