Implementing Cohort Analysis Modeling

When reporting paid search results, marketers often field a few recurring questions “How does search contribute to retention?” However, the ability to strategically and correctly answer these questions about search campaign effectiveness over time requires both deep reporting capabilities and a strong grasp of your organization attribution model.

Adoption of Cohort analysis as part of paid search reporting can be a powerful means to assess trends, retention and path to purchase. It also allows for greater accuracy when analyzing campaign results over a dedicated window of time based on the time it takes users to move through the funnel. In marketing, the term “cohort” describes segments of users who share specific events or experiences within a specific time frame. Cohorts include purchasers, email subscribers, trial and/or demo downloads or any other conversion action in the funnel.

Shifting to a Cohort model requires diligent up-front assessment and work, it’s crucial to ensure accurate data is being collected. The most important spreadsheet columns in this instance are the date and time stamps, such as “Original created date for the lead” and “Date when the lead transformed into its next stage” and so on. The date allows measurement of the time it takes for users to move through the funnel and application of that knowledge to paid search reporting and insights.

Once the right data is flowing and a statistically significant lookback window of results to review is available, it’s time to analyze the time it takes our users to pass through the sales funnel from paid search. To set up a Cohort analysis with ample data, shoot for a 6 – to – 12 month window of data. It’s vital to have a large enough date range so we don’t misinform paid search contribution to the marketing platform.

The Cohort model can be used to make faster and smarter search optimizations. It’s not practical to wait for 100% of our leads to move through the funnel before making decisions. Choose the right percentile to use instead. For example, taking the 75^th percentile will help determine how many days it takes for the fastest 75% of our paid search leads to move through the funnel. This may significantly reduce the days between stages from previous analysis.

The key to developing accurate reporting is to ensure prospects; opportunities and customers aren’t being reported outside their time windows. This means if a customer window is 30 days, we’re not viewing any customer results unless they’re 30 days old and have had that time to mature. To get an accurate cost per customer in this instance, we also want to exclude spend from the most recent 30 days. We should only view spends in the maturity window for our customers or opportunities.

Cohort Analysis application for Paid Search

Forecasting - Understanding the flow and evolution of paid search cohorts in correlation to pipeline or revenue makes it much easier to forecast the behavior of a new subset of customers.

Retention strategy – Comparing cohorts by day, week or month of acquisition by revenue generated from that group over the next 6 – to – 12 months will shine a light on purchase and engagement habit changes. If repeat purchases don’t increase, it may be best to implement a retention or re-engagement strategy to guide users back to the sales journey.

Seasonality - Assessing date of first customer/purchase against repeat purchase will highlight users who fall off after a holiday or busy season. Using this data can help inform marketers whether they should double down post-season.

Geo-specific purchase behaviors – If employing international or geo-focused paid search initiatives, measuring revenue incurred month over month by location will make it clear where LTV thrives or dives by region.

Analysis models vary greatly, and shifting to a cohort analysis or model can be a big decision. For many marketers, such a move is necessary for working with lead-gen campaigns. Implementing cohort analysis into paid search reporting is often a powerful means of charting true long-term trends for retention, churn and attribution at a more granular level — and more importantly, bringing to light opportunities within paid search programs.