Page Exit Ratio and RJMetrics

 In today’s increasingly digital world, consumers are bombarded with a slew of ads and over-saturated with content every time they go on the internet. Being able to cut through the noise and reach these consumers is getting increasingly difficult, and, as anyone involved in online marketing can testify, getting a visitor to the desired web page is only half the battle. The other half rests on a brand’s ability to engage visitors long enough to successfully convert them.

      Not every page visit turns into a conversion, however. If a visitor leaves a page before the conversion criteria is met, this can be classified as a page exit, with the “number of exits from a page divided by the total number of page views” as the page exit ratio (Web Analytics Association, 2008). While discouraging, page exits prove to be a useful metric because they provide data that can be used to improve a website and ultimately increase the website’s conversion rate. Determining the page exit ratio, or “the number of exits from a page divided by the total number of page views of that page” (Web Analytics Association, 2008), and measuring it against the company’s Key Performance Indicator for this metric is an important step in identifying what specific aspects of the website are keeping visitors from successfully converting.



      An excellent example of page exit ratio in action comes from the company RJMetrics. As a business analytics company, part of RJMetrics’ business strategy is to release benchmark case studies to the public (Sassoon, 2016). Traditionally, the company has done this by asking for visitors’ email addresses in exchange for a PDF download of the report; a tactic that has historically resulted in lead generation for the company. To increase user convenience and replace a seemingly antiquated method of content sharing, RJMetrics recently decided to move away from PDF distribution and instead opted to publish reports directly on the website, “gated only by a modal asking for an email address” (Sassoon, 2016). While the company’s ability to measure macro conversion rates remained unchanged, RJMetrics was now presented with the opportunity to measure user engagement leading up to and within the report itself in a way that PDF distribution had prevented before.

      The logical next step was for RJMetrics to use a web analytics tool, (Snowplow in this instance), to track page exits to measure visitor engagement during the process (Sassoon, 2016). This data was compiled into the chart below. The X axis of the chart measures how long a visitor was on the page, while the Y axis represents how far visitors scrolled into the page. For this particular page, a visit was considered a successful conversion if the visitor entered an email address into the modal before exiting. The blue dots represent visitors who did not provide their email address, the red dots represent visitors who did provide their email address, and the yellow line denotes where the email address modal was located physically on the page, with everything above the yellow line representing activity within the report after it was unlocked by submitting an email address. (The blue dots above the yellow line depict users who viewed the report multiple times and in doing so, were not required to provide the email address again to gain access.)

                                                                   (Sassoon, 2016)

      This chart reveals several insights about how visitors are interacting with this page. The most notable, and obvious, insight is that the conversion of landing on the page and providing an email address to gain access to the entirety of the report is not high. With a conversion rate of just over 16% and a page exit ratio before conversion of 84%, user engagement with the report seems to drastically diminish after visitors are asked to provide an email address. An optimistic takeaway, however, is that within the group of visitors who do enter their email address and gain access to the report, the majority are scrolling through the entire report and taking their time to digest all of the information offered.

      Using these insights, RJMetrics has identified a path it can pursue to increase the conversion rate on the page. The company’s hypothesis, backed by the chart above, is that readers haven’t been able to absorb enough of the report to make it worthwhile for them to provide their email address to learn more. If the email address request is moved further down the page, readers will have the opportunity to read more of the content and subsequently get hooked on the report, leaving them with less hesitation to provide their email address later (Sassoon, 2016).

      Without web metrics, and specifically page exit ratio, RJMetrics would only have the total number of email addresses gathered to determine the effectiveness of switching away from PDFs. With only the final conversion rates available, RJMetrics may have decided to abandon its new approach for the less desirable, but tried-and-true PDF approach. By utilizing page exit ratio, however, the company has both invaluable engagement metrics and the feedback it needs to keep the new approach and make changes to the website layout that could potentially lead to higher conversion rates.

References

RJMetrics. (n.d.). RJMetrics.com. Retrieved from https://rjmetrics.com/

Sassoon, Y. (2016, February 16). How RJMetrics measure content engagement with Snowplow: A case study. Snowplowanalytics.com. [Blog]. Retrieved on January 21, 2017 from
http://snowplowanalytics.com/blog/2016/02/16/rj-metrics-case-study-measuring-content-engagement-with-snowplow-event-tracking/

Snowplow, the event data pipeline. (n.d.). Snowplowanalytics.com. Retrieved from http://snowplowanalytics.com/

Web Analytics Association. (2008, September 22). Web analytics definitions. Retrieved on October 13, 2012, from:
http://www.digitalanalyticsassociation.org/Files/PDF_standards/WebAnalyticsDefinitions.pdf