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 #1
Site Feature Usage
One of the foundational features of Discover 3.0 is the ability to build and analyze site segments or personas of interest in concert: purchasers, shopping cart abandoners, product merchandise interactions, and new versus loyal customers are a few key examples.
Discover is equally adept at providing perspective on overarching site feature usage based on the custom characteristics of a given business. As the business and site functionality evolve over time; Discover provides a consistent window into site feature performance trends and metric outcomes.
The next few examples showcase leveraging Discover to provide perception into which site feature experiences are most impactful for visitors over the course of the purchase consideration process. A caveat, as always in working with segments, is to keep in mind the potential for overlap across segments; it is important to either account for such factors in the segment build criteria or appropriately communicate when relaying out the end data and analysis points.
Compare & Contrast Major Site Features
One simple example of site feature usage reports in Discover is to compare and contrast major site features against each other. In the example below we have built two defined segments: ‘Internal Search Site Feature Visits’, which is built around a visits segment that contained at least one instance of an internal search, and ‘Store Locator Site Feature Visits’, which is a visits segment that contained at least one instance of a store locator search on site.
We can review the trends over time by week granularity to discern volume and conversion attribution. The insights in this report in the context of site feature improvement prioritization and design decisions can be powerful.
For instance, if internal searches as a portion of visits are trending down over time, that could lead to an examination of whether overall user or isolated user segments’ site behavior has shifted to more of a browse mentality.
Likewise, taking the data within the store locator visits report in the context of retail store rollout initiative produces a valuable view that can be crossed with geographic and customer type data.
Actions within a Specific Site Feature
A second example explores how even within a single specific site feature, Discover users can break apart a clear data view across relevant dimensions.
For instance, we can build several segments around various ‘Product Reviews’ actions, such as visits where product reviews interactions are only read, visits where product reviews interactions are only written, and visits where product reviews are both read and written.
The three mutually exclusive segments then can be brought into the same single workspace and crossed with the Time Spent Per Visit dimension to inform the share of site feature occurrences for a given time period.
Note these segments across the top of the image below. A review of the data shows that product review read occurrences and product review write occurrences in the period exhibit similar frequencies. The low occurrence rate of visits where product reviews are both read and written within the same visit shows a dividing line in user intent that informs site personalization efforts. The view allows us to quickly assess what the current time windows of opportunity we have to engage users with product reviews.
Frequency of Use of Site Feature
A third example circles around exploring if certain types of site visitors tend to experience a given site feature with higher frequency.
For instance, looking at purchasers and non-purchasers at the visitor container segment level, we can see the impact of internal search success and failure.
The report below provides an assessment of visitor volume per segment, variations across internal search experiences, and the share of site visitors that do not experience any internal search results. The business can use the reporting on these trends to track improvements in site search and the relative impact of basic, advanced, or SKU searches. Site feature reports and analysis at this level of detail in Discover are quick and efficient in identifying key trends and potential problems.
In my time at Adobe Consulting I have often seen Discover as a linchpin in client optimization efforts and evaluation of site performance trends. I like to think of Discover as an analyst’s open canvas or tool kit that is deeply rooted in exploratory work around better understanding site visitors.
Discover in its best use challenges an analyst’s intuition, creativity in approach, and problem-solving skills to split apart complex site trends into defined reports with actionable findings. As illustrated in the prior examples, marketers can drill down to specific custom segments to optimize and emphasize site features countless ways.
Brian Au is a consultant in Adobe Consulting, focused on digital strategy, analytics & optimization for retail & travel clients. He tweets at @BrianAu.
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.