Audience amplification or audience segment extension helps transform a publisher’s first party traffic or hashed email addresses into an advertiser’s audience.

If a publisher’s first party data is a treasure chest, then audience amplification is a process that adds new gold coins to the chest and makes the existing coins more valuable by allowing advertisers to reach their visitors not only on their website, but all over the web. Here are some key terms and tips for purchasing and optimizing your audience segment extension solution.

Below are metrics and benchmarks that can be used to evaluate the ability of our (or any) audience segment extension solution to effectively scale:

• Match Rate
• Coverage
• Amplification Rate
• Precision
• Stability
• ID Distribution for Cluster Files

Match rate is the percentage of your cookies that Roqad has observed in the past (aka, percentage of cookies that we’ve already synced with).

For example, if we match 90 out of 100 IDs from a client, we have a 90% match rate. This metric tells you the percentage of your audience that we can scale.

On average, the minimum “good” match rate sits within the 60 – 70% range. However, this number can vary by platform (desktop, mobile web, mobile app, CTV), geographic region, ID type, and input type (log file, flat file, cookie sync, etc). Note that in many cases, not all cookies and devices will be net-new to the segment. Roqad can’t necessarily guarantee that ALL matched IDs and devices in the file will be brand new to segments.

Coverage is the percentage of your data points that Roqad has both observed and can connect to other IDs (vs. match rate, which shows all the IDs that we’ve already identified).

The minimum “good” connectivity rate is around 40%-60%, however this number can vary by platform (desktop, mobile web, mobile app, CTV), geographic region, ID type, input type (log file, flat file, cookie sync, etc), and output cookie space.

Amplification Rate is the total number of unique IDs in the resulting graph, divided by the total number of unique IDs in the data input.

For example, if Roqad matches 90 cookies out of 100 and is able to match those 90 IDs with 180 additional IDs, the new amplified number of IDs is 270, with an amplification rate of 3X. On average, amplification is 5-8x, while the minimum “good” amplification rate is 3X. However, this number can vary by platform, geographic region, ID type, input type, and output cookie space.

Precision is the number of correctly identified positive matches divided by the total number of match predictions made. This KPI includes only the number of matches correctly identified, and does not include the number of non-matches correctly identified. It can be calculated as:

True Positives / (True Positives + False Positives)

For amplification use cases, the minimum “good” precision level is 70%.

Stability is the opposite of churn; it represents the percentage of clusters that persist week-over-week.

For amplification use cases, the minimum stability should be 90%.

It is certainly an interesting time to be in or around the advertising ecosystem. People who make, distribute, and tabulate ad performance are facing new challenges in GDPR and other new privacy regulations, banner blindness and banner burn, IDFA and Google Ad ID deprecation, third-party cookies deprecation in Chrome and other browsers …

It is true that audience extension has been reliant on third-party cookies!

It’s time to try out different, future-proofed methods before the above identifiers are completely extinct. Roqad is building a new identity resolution suite that listens to, stitches together, and outputs clusters tied to the new so-called universal IDs as well as the specific IDs generated by publishers.