Get More Contextual With a Switch to Adjusted Bounce Rates with Time Intervals

Bounce Rate is generally accepted as a vital heartbeat measure of site performance. It is also perceptually flawed. There tends to be a general misconception among many business stakeholders across verticals that bounce rate must come from qualified bounces tied directly to users’ length of time onsite. What many fail to realize is that the variable for time is not a core dependency for the bounce rate equation. Instead, bounce rate depends on a ratio of single accesses divided by entries (SA/E) for a particular target page, regardless of the user duration. The assumption that bounces are only qualified if the bounces occur under a certain amount of time (such as a 30 or 60 second interval) is not only a fallacy, but also one that causes inaccuracies in judgement and reactive tactics. That’s not to say that correlating bounces to time intervals is bad. Quite the opposite in fact.

While the equation for bounce rate does not require on-page persistence over a length of time as a factor, there is significant value in an adjusted bounce rate broken out by time intervals. With the adjusted bounce rate by time intervals ratios in hand, one is able to better interpret more granularly defined impact on conversions, or the lack thereof. However, there is still the gap of whether the majority of user bounces occurred in under or over 30 seconds, or whichever threshold is mandated by various organizations in their performance goals. If a majority of bounces occurred in under 30 seconds, then page-level engagement runs the risk of being impeded by misaligned marketing messaging between search level meta descriptions or advertising promotional campaigns compared to on-page content. Likewise, if a majority of the bounces occurred in over 30 seconds, then engagement may be restricted by a user’s perceived inability to safely explore the content on the page via hyperlink click through events.

How does one go about creating an association between bounce rate and granular time intervals representing when particular bounces occurred?

Adobe Site Catalyst accounts for bounce rate by dividing total bounces for a particular target page by the total visits to that same page. If you need to conduct an evaluation against a particular campaign, then create a custom segment first to isolate traffic by a particular campaign ID. Isolate the first target page in the conversion funnel by conducting a Pages Report for a specific time period. For the selected metrics, include Bounces and Visits. Export this data into a custom table.

Next, run a Time Spent on Site report and filter down to your selected target page. Export your time interval data to a new table. For each time interval factor, such as “less than 15 seconds”, or “15 to 30 seconds”, divide total bounces by the partitioned traffic metric provided in the report. Once your table is propagated with this data by each time interval factor, you may color code the results to visually depict relational outcomes. Once this cycle is completed, start over with the next target page in the conversion path until all target pages in the funnel are accounted for with granular results. Tie the results for all targeted pages together into a final color coded matrix.

With a codified matrix completed, you should now have the ability to tell an enriching data-driven story about when users are engaging in the conversion flow. Such a story may be augmented by an additional color coded click map by onsite link position for the targeted pages to help provide additional context on user behavior tied to the conversion process. Spice this up with correlations to user geo-locations and device types to help with marketing campaign retargeting efforts. More importantly, this level of granularity could be used to identify optimization opportunities which could further be performance evaluated through Taguchi multivariate A/B/n testing. There are a number of directions to take conversion optimization and user behavioral evaluations once adjusted bounce rates by time intervals are determined and reported on. The best part is that the process is not only easy, but compelling and impactful as well.

Related reading

amazon google market share for ecommerce, data
guide to call tracking
Converting custom Using analytics to optimize sales funnels for new and returning customers
set up google analytics annotations for google updates