Discover 3.0 Fundamentals: Simultaneous Segmentation with Table Builder
Over the coming weeks, the retail industry experts in Adobe Consulting will share a series of analysis quick wins for retailers, using Adobe Discover 3. For a limited time, Adobe SiteCatalyst 15 clients can inquire with their account team and ask to take part in a free trial of Adobe Discover. We’ve made it easier than ever to try Discover, and we’re showing some great Discover analysis opportunities specific to the retail industry. For more information and to request trial access, contact your account manager or account executive.
Adobe Discover — Retail Quick Win #3
Simultaneous Segmentation with Table Builder
One of our favorite features of Discover 3.0 is Table Builder, which allows users to build highly flexible pivot tables that can incorporate multiple data dimensions, multiple segments, multiple metrics, and your desired temporal granularity all in one analysis or report.
Additionally, even though this feature looks and feels much like a traditional pivot table, the user still has the ability to “drill anywhere” to perform deeper dives or ad-hoc analyses within the defined Table Builder report.
One of the many valuable benefits of the Table Builder feature in Discover is the ability to evaluate high level key metrics (e.g. bounce rate, conversion rate, etc.) or metrics for a particular data dimension(s), such as products purchased, across multiple segments at the same time. In the two examples below, we’ll look at how the use of simultaneous segmentation can provide instant insight into the visit share and performance of multiple visitor segments to your site, as well as the landing page performance of your marketing channels for new vs. return visitors.
Examining Site Traffic by Visitor Status
Whether it’s to inform personalization efforts or just gain a better understanding of who is visiting your site, we can create mutually exclusive visit-based segments with Table Builder and then evaluate what portion of your site traffic each segment represents as well as how they perform against your site’s KPI’s (Key Performance Indicators). Consider these potential segments as a starting point, available to many retail and travel clients using a combination of out-of-the-box variables as well as some common custom variables:
- New visitors that register
- New visitors that do not register
- Return visitors that sign-in and have purchased
- Return visitors that sign-in and have not purchased
- Return visitors that do not sign-in and have purchased
- Return visitors that do not sign-in and have not purchased
The screenshot above shows 3 KPI’s (visits, revenue per visit (RPV), and conversion rate), by day for our 6 segments of interest.
Note the conversion rate of segment #5: Return visitors that are registered on the site but have never made a purchase. While their conversion rate is already 5X the total site conversion rate, we might consider presenting targeted promotions to these visitors at key checkpoints during their visit (e.g. on product details pages or the shopping cart).
Looking at what products these visitors are browsing and buying (by drilling down within these segments – right-click on the segment name) might help us refine such a strategy even more effectively and provides ample opportunity for testing. Additionally, we might use this data as a tracking mechanism to monitor the success of efforts we have in place to encourage visitors to log into the site.
Authenticating helps match visitors to customers which should help improve site personalization efforts. These visitor segments could easily be expanded to include other attributes such as loyalty program membership.
Monitoring Landing Page Bounce Rates by Marketing Channel for New and Return Visitors
Understanding landing page performance is a critical input in planning and evaluating marketing campaigns. One of my favorite analyses in Discover is to look at several key metrics, bounce rate and conversion rate in particular, across 3 dimensions: marketing channel, entry page type, and visitor type (new vs. return). I like to incorporate the visitor type dimension because it helps me to keep in mind customer acquisition as an objective of some of my campaigns. Note that having a solid page type variable in place is crucial for this analysis.
Consider the table below, which I’ve set up to include 4 segments: New Visitors from Natural Search, Return Visitors from Natural Search, New Visitors from Paid Search, Return Visitors from Paid Search.
There’s a lot to take in here but I’ll point out a few things:
- Note the difference in conversion rate between the segments when landing visitors on product details pages (Row 2). Return Paid Search visitors have a 46% higher conversion rate than the next closest segment (yellow square). What makes Discover so powerful is that we can now break down the Product Detail row by another dimension such as Entry Page Category to get deeper insight into which types of products are converting so well for this segment. That knowledge could help us test adjusting the paid search spend on certain categories.
- While relatively few visits from paid and natural search land on a Search Results page, these visits (blue squares) have a relatively low bounce rate and convert very well for visitors familiar with your site or brand (as opposed to new visitors). Again, drilling down into this entry page type across other dimensions will tell us what the product set was for these search landing pages as well as what external search keywords were driving to these landing pages. For this particular dataset, we found that the natural search visitors were trying to find retail store locations.
- With respect to category landing pages (red square, Row 4), you can see that the stickiness and conversion rate of these landing pages differs significantly between paid and natural search. Again, there’s opportunity here to drill down and understand which specific category landing pages are driving the results we want.
SiteCatalyst offers the ability for any user to relate one commerce dimension to another and to apply a segment to any given report. Using Discover offers the power analyst the ability to analyze multiple segments simultaneously within Table Builder while being able to drill down anywhere within the data without limit. From my experience working on the client side and now as a consultant with Adobe, I’ve seen first-hand how analysts quickly adopt Discover as their go-to tool for answering the hard questions and finding those opportunities to drive incremental value within their organizations.
Matt Gilligan is a consultant in Adobe Consulting, focused on digital strategy, analytics & optimization for retail & travel clients. He tweets at @gilliganmatt.
If you’re an online or cross-channel retailer using Adobe SiteCatalyst 15, you should try these Retail Quick Wins in Adobe Discover. We’ve made it easier than ever to experience a free trial of Discover. For more information and to request trial access, contact your account manager or account executive.