PetBox is a fictional eCommerce company that sells monthly subscription boxes containing animal care products. In the accompanying PetBox app, subscribers can track and customize their monthly boxes, view their box history, and purchase specific products they liked from their boxes. In addition, non-subscribers have access to features such as PetCam, which allows them to watch their cats through connected webcams.
PetBox wants to cross-sell cat toys to customers who are already purchasing their products but not necessarily in the toys category. They will start with cat food purchasers and create targeted campaigns if these consumers are not already buying cat toys.
Are cat food purchasers also purchasing cat toys?
If the start of the query row reads Total count of instead of Users who performed, you should make that change as well. Your final results should look like this:
Use 04/01/xxxx to Today in your date range selector and run the query. Click on any point and hover over Create User Segment. Be sure to then click on From entire series to get the users from the full date range.
Below is the Create a User Segment modal. Name the segment and provide a description so we can remember what we saved in the future. Select a category or create a new one by typing the name in so you can easily find your user segment in the future. Note the One-time/Daily toggle. In this case, we want the user segment to update with the most recent results (refreshed daily), so we will select Daily.
All of the prep work is done. You have set up a user segment of cat food purchasers and are ready to do your analysis.
Let’s build out our query.
We want to see the number of Users who performed Purchase Product, using Filter Where Product Category is ”Toys” this time, instead of “Food”.
Remember, we want to see if cat food purchasers are buying cat toys so the only thing missing from this query are the cat food purchasers. Click on the new Filter Where selector and find your Cat Food Purchasers segment.
Now run the query and let’s take a look at the results.
We see “not enough data for visualization” which means there is no data available for this query. It looks like Cat Food Purchasers did not purchase cat toys at all since April. At this point, you can analyze further by changing the date range to see if these users purchased any cat toys in the past or start a marketing campaign to target these users for cross-selling.
You can export your users to engage with them outside of Analytics in one of two ways:
Download a CSV and upload it to your marketing tools.
We hope this tutorial gives you ideas on how you can use Analytics to analyze your data and achieve actionable insights from it. If you have any questions or comments, please reach out to support@mparticle.com.
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