Segments in 5 Minutes [Analysis with Insight]
It’s quite common that a customer starting to use Omniture Insight will tell us that one of the key business reasons for adding Insight to their tool set is for its power of Segmentation.
In Omniture Insight, Segments are simply user-defined, re-usable selections of data. They’re quick to use and powerful in analysis when you want to segment and explore a group of visitors, customers, transactions, visits, or other similar level of data.
Let’s imagine that I work for a small group of retail stores and use Omniture Insight, loaded with my website data, but also with in-store transaction data and customer demographic data.
I have a Key Business Requirement (KBR) this year of increasing overall revenue per transaction.
As a strategic step toward that KBR, I am eager to understand female customers who purchase casual womens’ clothing and who have higher household incomes. Tactically, I’d like to develop ways to increase the number of items (and the associated revenue related to those items) in each of their purchases.
I’ll start my Insight analysis by opening a table to display Transactions, Customers, and Avg. Dollars per Transaction by Department. Sorting by Transactions by Department, I see that my top departments (for all customers, and all of their transactions) are Womens’ Casual, Kitchen and Accessories, Girls’, etc.:
Next, I’ll add a Segment visualization. Since I’m eager to analyze customers who fit my selection, I want to create a customer-level segment.
I’ll select transactions that included Womens’ Casual. While I’m at it, I’m also going to add tables for Gender (and select Females) and Income Descriptions (and select the upper income segments.)
In my customer segment visualization, I simply select “Add Segment”, then deselect in the other tables. For convenience, I rename the customer segment to “Upper Income Females, Casual.”
Then, I simply select the customer segment that I created. As a result, what I’ll see in my analysis workspace will be the data related to this selection of customers (female customers in upper income ranges who purchased casual womens’ shoes.)
Looking at the Departments that appear in the most transactions for these customers, I discover that their transactions frequently include kitchen and accessories items, womens’ dress clothes, beauty and fragrance items, etc.
Sorting the Departments for these customers by Average Dollars per Transaction, though, I discover somewhat surprising results: the top departments by dollars per transaction include boys’ items, mens’ athletic items, womens’ athletic items, and childrens’ athletic items. Perhaps there’s an opportunity to try to do something I haven’t done before and cross-promote womens’ casual items with childrens’ athletic or boys’ items.
I can use this segment to continue to dive deeper into other information — specific items, average time between purchases, discounts or coupons most frequently used, etc.
Closing the loop from this analysis back to my tactical objectives (and ultimately my KBR of increasing overall revenue per transaction), I might decide to select a group of these customers (that live a certain distance from a store, that have children in the household, that meet other factors that I discover through analysis) and send them a specific coupon offer inviting them to a special discount when they purchase casual womens’ items and kids’ items in the same transaction.
The best part of this whole exercise: With my data in Omniture Insight, I completed this entire analysis well within 5 minutes, and I’m on to my next hypothesis, my next analysis, and my next discovery to impact my business.
Have a question about anything related to Omniture Insight? Do you have any tips or best practices related to Omniture Insight you want to share? If so, please leave a comment here or send me an e-mail at mhalbrook [at] omniture [dot] com and I will do my best to answer it here on the blog so everyone can learn. (If you prefer, I won’t use your name or company name.) You can also follow me on Twitter @MichaelHalbrook.
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