What are the common causes and solutions for low match rates in programmatic?

1 Technical issues

One of the main causes of low match rates is technical issues that prevent the proper communication and synchronization between the publisher's ad server and the advertiser's demand-side platform (DSP). These issues can include network latency, server errors, cookie mismatches, and invalid or expired identifiers. To solve these issues, you should monitor and troubleshoot your RTB integrations regularly, and use tools and protocols that can ensure the accuracy and validity of the bid requests and responses. For example, you can use prebid.js, a popular open-source framework that helps publishers implement header bidding, a technique that increases the competition and match rates for ad inventory.

2 Data discrepancies

Another common cause of low match rates is data discrepancies that occur when the publisher and the advertiser have different or conflicting information about the user, the ad inventory, or the bid parameters. These discrepancies can result from different data sources, measurement methods, or reporting systems. They can also result from human errors, such as typos or misconfigurations. To solve these issues, you should align and verify your data with your partners, and use standardized and transparent data formats and protocols. For example, you can use OpenRTB, a widely adopted specification that defines the structure and format of the bid requests and responses.

3 Privacy regulations

A third common cause of low match rates is privacy regulations that limit or restrict the collection and use of user data for targeted advertising. These regulations can vary by region, such as the General Data Protection Regulation (GDPR) in the European Union, or by platform, such as the App Tracking Transparency (ATT) policy in iOS devices. These regulations can affect the availability and quality of user identifiers, such as cookies or device IDs, that are essential for matching users across different platforms and channels. To solve these issues, you should comply with the relevant privacy laws and policies, and use alternative or consent-based identifiers, such as hashed emails or universal IDs, that can respect user privacy and enable cross-device matching.

4 Bidding strategies

A fourth common cause of low match rates is bidding strategies that affect the competitiveness and attractiveness of your bids. These strategies can include your bid price, frequency cap, budget allocation, creative optimization, and targeting criteria. These strategies can determine how often and how well your bids match with the available ad inventory and the user preferences. To solve these issues, you should test and optimize your bidding strategies regularly, and use data and analytics to inform your decisions. For example, you can use machine learning algorithms, such as reinforcement learning or multi-armed bandits, that can dynamically adjust your bids based on the feedback and outcomes of previous bids.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?