Case Study: up to 233% increase in trigger conversions with the help of recommendations
Subscribers want to see relevant and personalized content in their emails. This is confirmed by marketing research. For example, Harris Interactive and Listrak found that 84% of subscribers find product recommendations in emails useful if the suggested products or categories match their preferences. 69% are happy to share information about their preferences with an online store so that they can get personalized offers.
Such offers can be product recommendations added to promotional emails and triggers. They serve to increase conversions in mailings. In this article we describe how Shafa.ua applies product recommendations.
Shafa.ua is a platform that consists of a website and an app. This is a marketplace where users can sell and buy various branded products, both new and used. There are sections with goods for men, women, children, beauty and home products.
The client had the following tasks for email marketing:
- Increase sales from the email channel.
- Increase retention and repeat sales percentage.
- Automate communications to reduce the time marketers spend writing emails and building email segments.
- Reach the maximum number of site users with trigger mailings
To achieve these goals the team used the “Advanced Segmentation” with trigger chains. They’ve added recommendation blocks to trigger personalized emails for each recipient, based on their interests and actions on Shafa.ua website.
With “Advanced Segmentation”
it’s possible to identify 40% more site visitors from different channels, browsers and devices. The system is able to link this data with a contact from the database, even if the user is not logged in during the session. More than 50 AI algorithms are available to create individual collections, and upon request, it is possible to develop a custom option to solve the individual problems of your business.
Firstly we installed a web tracking script that enabled advanced segmentation, then uploaded a product feed to add recommendations to emails. The script collects information about user behavior and based on this data a personal collection for emails to each specific recipient are generated.
Then we prepared a series of triggers with product recommendations:
- Abandoned viewed;
- Abandoned cart;
- “You may also like”;
- Price reduction for viewed products.
The trigger works for those users who put the product in the cart and did not complete the checkout. The email is sent one hour after the end of the session. It contains up to 3 cart items and up to 3 recommended items.
The subject line of this email is: Oops... You didn't complete your purchase
The abandoned cart chain is omnichannel: it uses mobile push, web push and email. Product recommendations are used only in the emails.
The script for this trigger goes as follows:
Abandoned cart campaign launch condition: emails are sent every hour to a group of users who have not completed their order. But there is a limitation - all these messages can be delivered to one subscriber no more than once every 196 hours.
This trigger is received by those site visitors who viewed the products, but did not perform other targeted actions. It’s sent an hour after the user left the site. Up to 3 last abandoned viewed products and up to 6 recommended ones can be shown in the email.
The theme consists of the names of the items viewed and an emphasis on the recommended items that can be bought together: $!data.get('abandoned_view').get(0).get('name') | and other products that are viewed together.
As for the previous trigger, the workflow is omnichannel. The user is sent a notification in the mobile application, an email and a web push at the same time. The script is as follows:
Abandoned views runs once an hour to a group that gathers contacts who have viewed something on the site. In order not to spam the users, a restriction is set – each unique email can be sent no more than once every 196 hours.
The trigger is used for users who have not visited the site for 30 days. The email can contain up to 6 previously viewed products and up to 6 recommendations. At the same time, in both blocks there should not be more than two products from the same category.
If it is impossible for the user to form a block with recommendations (for example, he was once on the site, but did not go to product cards), the system does not send an email. Message subject: We miss you and picked up these products.
The workflow includes the same channels as in the previous triggers, but the start time is specified - 14.30:
According to the launch conditions, such a trigger is sent every day to the “Reactivation” group. Each of the users can receive reactivation messages no more than once every 90 days.
"You might like it"
This trigger is created on a look-alike basis. Its content is formed from products that are similar to the positions viewed by the user, and then recommendation blocks are added. Up to 3 similar products and up to 6 recommended ones are substituted in the letter.
Subject line: You will love these products…
The script is:
The launch condition is once a day for the “Reducing the price of a viewed product” group and no more than once a week for each user.
Price reduction for viewed products
The trigger is sent when the price of any of the previously viewed products goes down. Recommendations show similar products from the same product category and the same price range as those the user is interested in.
Email content: up to 3 reduced price items from the same category and recommendations for up to 18 items. The number of recommendations that can be added depends on the advanced segmentation connection package.
The subject clearly indicates which item is being discounted. The product name is inserted into the topic automatically: Discount on $!data.get('Reduced_on_viewed').get(0).get('name').
The launch condition is once a day for the “Reducing the price of a viewed product” group and no more than once a week for each user
To evaluate the results, we analyzed the performance of these triggers for the calendar month from January 15 to February 15, 2021.
We compared the average conversion rates of the two main and most widely used triggers across online stores - abandoned carts and abandoned views - with the obtained data.
Increasing the conversion of the “Abandoned Cart” trigger
On average, according to our observations, the conversion of this trigger is 8-11%, depending on the type of business and product category. In the category “Clothes and accessories”, which is the main one for Shafa.ua, this figure is 8%.
Thanks to product recommendations, we achieved a 25% increase in the conversion of this trigger.
Increasing the conversion of the “Abandoned Views” trigger
The conversion of the “Abandoned View” trigger is usually 3-6%. For the “Clothes and Accessories” group, the conversion of this trigger is on average 3%.
The Abandoned Views trigger conversion increased to 7%, which corresponds to a 233% increase in the rate.
Conversion of reactivation emails and additional triggers with recommendations
The conversion rate of reactivation emails is considered good if it reaches 1.5-2%. Sending reactivation emails with product recommendations in this case resulted in a conversion rate of 5%, which is a very high rate. We consider it inappropriate to compare it with other values, since the settings of reactivation campaigns are different for market participants, and this affects the result.
The remaining two triggers based on advanced segmentation and with recommendation blocks were not previously used by Shafa.ua and there are no general market statistics for them. They gave a high conversion, which was 6 and 5%.
How advanced segmentation will help your business
Shafa.ua plans to further develop this integration:
- transfer all products - 15 million positions. At the time of writing 1 million goods is being transferred;
- improve integration with the application.
This case shows that product recommendations in emails can increase the conversion of existing triggers by up to 233%, as well as generate new sales by introducing additional triggers based on advanced segmentation.
To get even more impressive results, use advanced segmentation, learn more about your customers and add products to your recommendations that will definitely interest them.