Lab 5: Generate insights

In this lab, you will learn how to:

Generate insights about your segments
View differentiators and overlap between segments
Build suggested segments based on a segment

Exercise 1: Generate insights on your segments

Task 1: Segment differentiators

Often, you will have customers that exist in multiple segments. It can be helpful to understand when you might have overlap between them. Let’s try to find out common customers that belong to both Customers from Texas and Fall close out Promotion segments and also what differentiates both of these segments in terms of Reward points and LifetimeSpend.

Select Insights > Segments from the left navigation menu. Select the Insights (preview) tab and select + New from the command bar, or select the + New insight button.
You will now see two options. Let’s create using Differentiators first to see what distinguishes both of these segments. Select Differentiators.
Choose Fall Closeout Promotion as the primary segment. Select Next.
Choose Customers from Texas as another segment, and then select Next.
Now choose Reward points under Customer fields and LifetimeSpend under Measure fields to see how the above segments differ from each other with respect to Reward points and LifetimeSpend. Clear all other customer and measure fields.
Select Next and name the insight Fall Promotion vs Customers from Texas.
Verify the Output entity name gets set to FallPromotionvsCustomersfromTexas. Select Save.
After the insight finishes refreshing, open the created insight. Select the Attributes or Measures tabs to see how the segments differ from each other with respect to the fields you selected. Observe the Difference score, which signifies the degree of difference. The higher the score, the more different they are.
Select each measure and attribute to see deeper insights.

Task 2: Segment overlap

Now that we have successfully created a Segment insight using Differentiators, let’s create an insight using Overlap.

Make sure you’re still in the Segments > Insights (preview) tab and select + New from the command bar. Choose Overlap.
Select both Fall Closeout Promotion and Customers from Texas segments to find out their shared customers.
Select Next.
Name the Segment Fall Promotion Customers Overlapped with Texas Customers and verify the Output table name is set to FallPromotionCustomersOverlappedwithTexasCustomers. Select Save.
After the insight finishes refreshing, open the created insight to see the venn diagram showing the total and percentage of shared customers between these two segments.

Exercise 2: Segment expansion and suggested segments

Task 1: Segment expansion

Segment Expansion can be used to find similar customers to your segment customer base using Artificial Intelligence. Earlier we created a segment called Fall Promotion Customers, which contains millennial customers with higher than average in-store purchase. Now, let’s expand that segment and find customers that are similar to them for us to market our newly launched Cold Brew Coffee.

Select Insights > Segments from the left navigation menu and select the All segments tab.
Choose the Fall Promotion Customers segment. This will become our source segment.
Select Find similar customers from the command bar.
Name your segment FallPromoExpansion.
Select Add fields in the next step to select attributes and measures that are used to find similar customers. We’ll target customers with similar location and average in-store purchase, so choose PostCode and AverageStorePurchase. Select Apply.
Next you must choose who to consider. For now, select All customers except source segment.
Now the maximum number of customers to include must be selected. Let’s stick with 20%.
If you would like to include members from the source segment as well then check the last option. Otherwise, leave it unchecked. For our purposes, we can leave it unchecked.
Select Run.
After the segment finishes refreshing, open the segment to find the Similarity Scores and explore the Segment members preview.

Task 2: Get suggested segments

Use Suggested Segments to discover interesting segments based on a customer attribute or measure of interest.

Select Insights > Segments from the left navigation menu, and select the Suggestions (preview) tab.
Select Get suggestions to begin the configuration experience.
Choose Improve a measure/metric and select Start.
Under Measure fields, select LifetimeSpend as the target attribute. Select Next.
Next, you will select the attributes that might influence the target attribute (LifetimeSpend) so the AI model can find interesting patterns between the influencing and target attribute. The model will suggest segments based on those patterns. Select Email Subscriber, Income, Loyalty Tier, Occupation and State as the influencing attributes.
Select Run. The AI model will start finding patterns between the selected influencing attributes and target attributes to surface segment suggestions. Please wait for a few minutes for the model to finish its analysis.
Once the model has finished running and if it is able to uncover patterns between the influencing attributes and the target attribute, segment suggestions will be displayed under the Suggestions (preview) tab.
To save the segment, select Save as segment in the Suggested segment details panel. Select Save.
The saved segment can then be viewed under the All segments tab and it can be used for downstream processes like any other dynamic segment. If you wish to look at the rules that the model learned after saving a segment, you can do so by selecting Edit in the All segments tab.