Automated Conversion Metrics
They aren’t usually standardized. Unfortunately, most digital analytics tools don’t come with conversion metrics out of the box. They may have a few, like bounce rate, but once you start customizing your analytics implementation, you are on your own when it comes to creating conversion metrics. This is one of the drawbacks of every organization having its own custom analytics implementation.
They require a lot of resources and time. Each organization has to conceptualize the conversion metrics they need and then define the correct formula for these metrics. This is both cumbersome and inefficient. Over the past twenty years, hundreds of conversion metrics have been created across thousands of implementations. Why can’t all of this experience be leveraged by the entire digital analytics community?
Apollo’s Best Practice, Automated Conversion Metrics
Apollo provides both best practices and automation to help improve your digital analytics implementation. To start, Apollo provides a library of best-practice conversion metrics learned over decades of digital analytics implementations. Having these conversion metrics is powerful because it allows you to start with the end in mind. You can pick the conversion metric you want and then back into the data elements needed to produce it.
As shown here, each conversion metric displays which data elements are needed. The data elements are color-coded to indicate which ones are already part of the implementation and which are not. If new data elements are needed to use a conversion metric, they can be added in a few clicks within Apollo.
The other powerful aspect of conversion metric functionality is that Apollo’s relational capabilities make conversion metrics re-usable for any digital analytics implementation. Typically, if an analytics vendor wanted to share a custom conversion metric with multiple customers, it would be difficult because the definition of the conversion metric would be tied to different variable numbers for each organization.
For example, even if Company A and Company B have Form Starts and Form Completions, the vendor couldn’t share a Form Completion Rate conversion metric because Company A and Company B likely use different variable numbers for Form Starts and Form Completions. But since Apollo uses a relational database to know which data elements are needed for conversion metrics and which variable numbers are being used by each organization, the same conversion metric can be leveraged by hundreds of organizations. Apollo can therefore continuously add more and more conversion metrics to its library, knowing that there are economies of scale that will make them available to all organizations. This means that vendors (and altruistic members of the digital analytics community) can help customers and peers by creating and sharing reusable, best-practice conversion metrics.
In addition to the benefits of shared, best practice conversion metrics, Apollo can also automate the creation of these conversion metrics. Leveraging analytics tool APIs and the Apollo’s relational database, conversion metrics can be easily pushed into the analytics tool.
This automation makes it easy to share conversion metrics with all users and ensure that the correct metrics are being used.