Why Filtering Traffic Before You Buy It Is Now Critical?

The digital advertising market is evolving rapidly. Just a few years ago, most industry players considered anti-fraud a post-analysis task: first you buy the traffic, then you analyze the statistics, identify fraudulent activity, and request refunds from your partners.

Today, that approach is outdated and expensive. Several factors have driven this shift:

  1. Growth of automated traffic. Bots have become more sophisticated: they click, convert, and mimic real user behavior.
  2. Pressure on margins. Every invalid click directly reduces the available budget, while bid prices continue to rise.
  3. Speed of RTB auctions. A DSP or ad platform has only milliseconds to make a decision, and it is crucial to filter out low-quality requests before bidding.

Against this backdrop, pre-bid filtering “filtering before the purchase” has become an essential tool. At its core are databases that allow instant decision-making: whether to bid on a specific impression or skip it.

This article examines how databases function in the context of RTB, why they are vital for pre-bid filtering, and how Kaminari Click enables ad networks, DSPs, and advertisers to improve campaign efficiency.


RTB in Brief: Where Filtering Fits In

RTB (Real-Time Bidding) is the process of buying and selling ad impressions through real-time auctions.

The process works as follows:

  1. A user opens a website or mobile app with an ad slot.
  2. The SSP (Supply-Side Platform) sends a bid request to multiple DSPs.
  3. Each DSP evaluates the request, taking into account parameters such as geo, browser, device type, audience, and page content.
  4. The DSP decides how much to bid for the impression.
  5. The SSP selects the winning bid and serves the ad.

The entire cycle takes about 100 mc.

The challenge: within this short window, the DSP may receive a request associated with bot traffic or an anomalous source. Without pre-bid filtering, this results in wasted spend, distorted analytics, and lower conversion rates.


What Pre-Bid Filtering Is and How It Works

Pre-bid filtering is the process of checking incoming bid requests and rejecting those deemed invalid before placing a bid.

Difference from post-bid filtering:

Databases as the Core of Pre-Bid Filtering

To make a decision within 50–100 milliseconds, a DSP cannot run deep behavioral analysis during the request.

Instead, it queries a pre-built database that contains:

  • IP addresses and ranges associated with bots, data centers, proxies, and VPNs.
  • ASNs (Autonomous System Numbers) known as sources of low-quality traffic.
  • User-Agent patterns to detect headless browsers, scripted agents, and emulators.
  • Device IDs / fingerprints previously flagged for fraudulent activity.
  • Historical behavioral metrics such as time on site, engagement depth, and conversion frequency.
  • Geographical anomalies like mismatches between IP and declared GEO or sudden region changes.


How Kaminari Click Builds Its Databases

Since 2019, Kaminari Click has processed billions of requests every month. Each traffic check can become an entry in the database, later used for pre-bid filtering.

Primary data sources:

Real-time checks – JS and server-side validation for each visit:

  • Browser behavior and capability checks.
  • Proxy/VPN detection.
  • Emulator and headless browser detection.
  • Analysis of interaction speed and patterns.

Historical logs:

  • IPs/ASNs previously flagged for fraud.
  • Device fingerprints with abnormal activity.
  • Statistical patterns in clicks and conversions.

External integrations:

  • Partner anti-fraud solutions.
  • Open-source and commercial IP datasets.

Internal correlation algorithms:

  • Automatic identification of new fraud patterns.
  • Instant replication into pre-bid databases.


Technical Process of Pre-Bid Filtering with Kaminari Click Databases

Bid request received

The DSP or ad platform gets a request from the SSP containing user, device, GEO, and other parameters.

Database query

The platform sends an API call to Kaminari Click or uses a locally synchronized copy.

Data matching

  • IP: Is it blacklisted?
  • ASN: Is it on the suspicious list?
  • User-Agent: Is it headless or automated?
  • GEO: Does it match the IP?
  • Fingerprint: Has it been flagged as fraudulent before?

Scoring or flagging

  • 0 (clean) — proceed with bidding.
  • 1 (suspicious) — handle according to configured rules.
  • 2 (fraudulent) — reject.

Decision

If clean, the bid is placed; otherwise, the request is skipped.


Why Database-Driven Pre-Bid Filtering Is More Effective


  • Budget efficiency: each blocked fraudulent bid directly saves money.
  • Performance stability: click-through and conversion rates remain more consistent.
  • Reduced post-analysis workload: fewer disputes with advertisers.
  • Scalable protection: effective across millions of bid requests.


Advantages of Kaminari Click Databases


  • Scalability: processes up to 460 million checks per month with instant replication to active databases.
  • Combined intelligence: merges real-time JS detection with historical datasets.
  • Customizable rules: clients can set blocking logic based on their own metrics.
  • RTB-ready performance: API responses in milliseconds; local sync in microseconds.
  • No negative impact on conversion rate: filtering occurs before the click, avoiding legitimate user loss.


Example Use Cases for Pre-Bid Filtering

Case 1 – IP Blacklist

An ad network implemented automated blocking of AWS and OVH data center IPs.

Result: IVT decreased from 17% to 3%, refund requests from clients dropped significantly.

Case 2 – ASN Mobile Proxy Filtering

A DSP excluded ASNs linked to mobile proxies.

Result: Conversion rate increased by 24%, and invalid registration complaints decreased.

Case 3 – User-Agent Control

The pre-bid filter detected headless Chrome and Puppeteer traffic.

Result: CTR decline caused by bot clicks was eliminated, CPM levels stabilized.


How Databases Support Long-Term Campaign Optimization

  • Cleaner analytics: advertisers get an accurate view of real user behavior.
  • Transparent reporting: easy to demonstrate what was filtered.
  • Flexible testing: enables quick A/B experiments to measure the effect of different filter settings on conversion rates.


Conclusion

Pre-bid filtering acts as both a budget safeguard and a brand protection tool. In RTB environments, it is particularly important because decisions are made in milliseconds and errors have immediate financial consequences.

Kaminari Click’s databases allow platforms to:

  • Leverage billions of historical traffic checks.
  • Make rapid, informed bidding decisions.
  • Block fraudulent traffic before it can harm campaign performance.

For platforms engaged in RTB or large-scale media buying, integrating database-driven pre-bid filtering can significantly reduce costs while improving key performance metrics.