Visitor segments allow us to differentiate between the behaviors and intent of individual visitor groups. They are the fuel that drives our marketing analysis, testing, optimization and personalization efforts towards success. These powerful cohorts allow us to see what and who is driving the end-result, and act accordingly. Without them, we end up looking at meaningless, useless numbers.
Developing a customer centricity approach is the core foundation of a strong marketing strategy, and in order to be able to deliver powerful personalizations, it is crucial to segment the customers. This article explains why discovering the optimal customer segments is such a critical step towards real personalization.
— Dynamic Yield (@DynamicYield) March 27, 2015
Let’s say we’re looking at a report for the last 7 days with the following metrics:
– Unique users: 13,020,403
– Revenue impact: $260,408
– Average revenue per user: $0.02
Can we conclude anything valuable from this? The answer is, obviously, no. This information is absolutely useless because too many pieces of the puzzle are missing. For example, we have no relative context for this data, and we can’t compare these metrics over time to perform trend analysis. In short, there isn’t any real story to tell here (I encourage you to read Why Most Marketers Fail at Web Analytics for a deeper explanation).
Segmenting these aggregate metrics can have a tremendous impact on our marketing activities, eventually leading to better insights, conversion and revenue or average revenue per user. If we take a second, deeper look at the data from a structured, segmented point-of-view, we may define several interesting segments to study and then reveal the real story behind the “what” and “who“. Let’s have a look at an example performance report of the top 5 visitor segments:
When analyzing these pre-defined segments, it becomes clear that the “Tech & Gadget Enthusiasts” yielded the highest revenue impact. That being said, there’s another interesting segment to explore, “Video Enthusiasts”, with an average revenue impact of $0.06. Compared to the overall average revenue impact of $0.02, that’s a 200% lift! This data tells us that we need to focus our marketing efforts on these two most valued segments.
But why stop there?! Realizing that there’s something impactful with the group of video enthusiasts, we can now delve deeper into the stats and create sub-groups of segments, based on the number of videos watched. For example, the following chart reveals that visitors with 3 or more video plays have had the highest revenue impact, with a 93% lift, compared to visits where only a single video was watched. Now that’s interesting!
The process of analyzing segmented visitors can be thrilling, because it’s an opportunity to uncover some of the truths behind the numbers. Nevertheless, it’s an ongoing, time-consuming cycle. You usually start by studying new segments. You continue by (hopefully) discovering interesting groups, then you start analyzing the data and finally, you conclude which actions need to be taken upon those groups.
There are many ways to segment visitors. Standard segments can include dimensions such as geography, traffic sources, devices and page views. If you’re looking for more advanced segments (and you should), here are a few ideas to include in your segmentation toolbox:
Discover how to build actionable segments and deliver optimized and personalized customer experiences that drive higher revenues and engagement.
Advanced Ways to Group Visitors into Segments, According to:
- Revenue impact (especially for discovering high-value customers)
- Engagement or action taken (e.g. video watchers, social media interactions, etc.)
- Type of content consumed (e.g. product pages, landing pages, categories, etc.)
- Visitors with past conversions vs. no conversions
- Session count
- Depth of visit (e.g. visits with 3+ page views vs. 2 or 1 page views)
- Customer intent (e.g. interaction indicators, path of journey, past behavior, etc.)
- Visitors with cart abandonments
- Activities during business hours vs. off hours
- Visitor type (e.g. non-registered vs. registered / non-customer vs. customers)
- Type of readers (e.g. scanners vs. avid readers)
With advanced technologies in hand, like the one we offer at Dynamic Yield, we can automate the process to easily discover new opportunities to maximize engagement and yield and deliver real-time personalized website experiences across the entire customer journey. Contact us now to learn more about our customer segmentation and personalization solutions and schedule a personalized demo.