Enrich your eComm data set to include item quantity information that can give you insight into high/low volume purchasers, allowing for proper customer segmentation and hyper specific campaign targeting.
Step 1: Identify where on your website’s checkout process the item quantity field(s) are located and add Snapshots
This snapshot will likely be associated with a View Checkout Page event since order item totals are usually listed on the order summary page of a checkout flow.
Note, you can also create Snapshots that captures the name of each individual item(s) in addition to the quantity, but for this play we’ll just be focusing on capturing and analyzing the quantity of items purchased.
For details on creating your snapshots, you can check out Heap’s Creating & Managing Snapshots documentation.
Note: Snapshots Are NOT Retroactive
Unlike Heap’s core autocapture technology, Snapshots are not retroactive. That means the Snapshot does not start collecting data until it’s been created. We recommend keeping this in mind, and setting up a Snapshot as soon as you know you’d like to start capturing this data.
Step 2: Identify high quantity and low quantity purchaser benchmarks
| Query #1 | Item Quantity Average |
|---|
| Graph | – Average: “purchase event” using the ”item quantity” snapshot as the property – Date range “over the past year” grouped by “Entire range” to get average |
What does this tell you?
Knowing the average allows you to determine the high and low volume rates for your customers.
Note
Holidays might spike the average, so depending on how long you have been a customer of heap, be mindful of this. Remember, snapshots are not retroactive.
Step 3: Create appropriate Segments
Using the benchmark from Step 2, create a “high volume”purchaser segment and a “low volume” segment. For details on the importance of segments or how to make a segment, check out this article.
Tip: Feel free to change the segment’s time range from “at any time” to a time-bounded window. If completing an order once with more items permanently changes the way you think about a user’s lifetime value, then it may make sense to define this segment as “at any time.”
However, if completing an order with many items, or submitting multiple orders has a time-limited effect on a user (e.g. may increase their likelihood to purchase within a month) then change the time range accordingly.
Step 4: Target your Segments!
| Query #1 | Identify your customers in each segment so you can properly target them |
|---|
| Users | – Add Filter: “users who are in segment” High Volume Purchaser |
Export this query to have a comprehensive list of all of the users who are in each segment.
Step 5: Interpret your results and take action
You now have a list of customers that purchase both an above average number of items and a below average number of items.
Share these lists with your Marketing team so they can send out campaigns to each customer segment.
Examples include:
- Launching a VIP membership campaign for high volume purchasers
- Rewarding a high volume purchaser with a special offer to thank them for their loyalty
- Launching a loyalty or program for high volume purchasers
- Target low volume purchasers with an incentive to entice them to increase their purchases
Conclusion
In order to personalize your customer’s experience, you need to properly segment them in order to correctly target users based on behavior and push them towards a successful conversion.
Use this play to enrich your eComm data set and segment out your customers for personalization and specific marketing targeting.