Key Figures +25 % additional revenue generated by recommendations +$0.85 increase in revenue per session, according to Google Analytics It took 3 Google Optimize tests to find a successful version of the recommendations What Was Done and What Were the Results Olant has now unified recommendations in mailings and on the website. We set up the transfer of sales from online and offline to a single CDP. […]
April 22, 2020
Product Recommendations Generate +25% of Revenue for the Olant Mom & Baby Store
The following case study is from Mindbox, the original brand behind Maestra’s technology
In the process, they wanted to settle all the previous issues, including taking offline sales into account in the recommendations and learning to change the composition of the recommendations at their discretion, which should ultimately allow for the efficiency to be analyzed. Sergey Gerdov, Product Manager at Olant, shares his opinion about the work carried out.
+25 %additional revenue generated by recommendations
+$0.85increase in revenue per session, according to Google Analytics
3Google Optimize tests to find a successful version of the recommendations
What Was Done and What Were the Results
We compared the efficiency of the three options for product cards on the website. Option 1: we do not recommend anything. Option 2: we recommend similar and related products. Option 3: we recommend only related products. The second option led the way. Two months of testing showed +25% revenue relative to the control group, which didn’t receive any recommendations. That’s $0.85 more for each session.
We had been using Mindbox for our email marketing for 5 years when we decided that it was time to try website recommendations. With this, we were looking to improve our online store’s KPIs. We were using another software provider at the time, but Mindbox proved to be more client-focused and had better results. What’s more, the cost of the Recommendations module was lower than our subscription fee for the other service. Phil and Igor were great on this project and we’re happy with the results.
In fact, the starting hypothesis used to be the standard accepted truth: product recommendations in the product card produce additional revenue.
What Was Done
- We integrated the online store and retail sales into a single CDP and set up a cross-product identification, so the same product in the online and offline stores now has a single identifier.
- We assembled a block of product recommendations. We created and customized the recommendations, taking into account the wishes of the client.
- We set up a test in Google Optimize and went through three iterations to see the actual result.
We Integrated Online and Offline Sales into a Single CDP
The ERP system unloads orders for the last day via FTP once within a 24-hour period, then Mindbox CDP automatically receives sales data. We set up a cross-product identification in order for CDP to understand that the stroller from the website and the stroller from the brick-and-mortar store are the same product.
Mindbox keeps a complete history of customer purchases both from the online and offline segments. We will never advise a customer a product that they have already bought.
We Assembled a Block of Product Recommendations by Laying It Out and Configuring All the Parameters.
Product Block Layout
Olanta’s product recommendation widget allows the customer to add a product to favorites or to the cart, and to see its availability in brick-and-mortar stores. It took five days to configure the complete layout and iron out all the bugs.
You can pay for your order by cash or card when receiving your order from the courier, or by card on our website
Configuring Recommendation Rules
In the Mindbox interface, we set the rules for displaying recommendations and configured them to Olanta’s liking. For example, it was important for Olanta that the Munchkin brand was advised in the nutrition section first and only then the others.
Related product settings
Similar product settings
Setting Up a Test in Google Optimize
To measure efficiency, we set up a test in Google Optimize. It took three tests to see the actual result.
We began to look into the reasons behind it and found the “add to cart” button wasn’t functioning. Without this button, it is impossible to assess the efficiency of the recommendations. We fixed the button and ran the second test. We learned an important lesson: we shouldn’t wait for the test to be completed, it’s better to check it in the process to see whether everything is going according to plan.
We turned off the second test and changed the parameters that allowed us to determine similar products. We increased the price indicators to advise similarly priced items to customers.
The third iteration of the test showed statistically significant revenue. It’s curious that the option that included only related products brought in a smaller amount of revenue than the option with similar and related products. Each session in the version with related and similar products brings $0.85 morethan without them.