See How Personal Product Recommendations Generate 67.4x ROI for IMA
International Military Antiques implemented Listrak’s Personalization Engine in October of 2014. Its onsite recommendations account for 36% of IMA’s e-commerce sales while its email recommendations make up nearly 30% of all email revenue. IMA is seeing a 67.4x ROI from product recommendations alone.
Onsite
Prior to implementing Listrak’s Personalization Engine for onsite product recommendations, IMA was manually selecting four recommendations for each product page on its website. This was very tedious and difficult to merchandise and manage. With Listrak, IMA can now create customized algorithms and use predictive analytics based on 360-degree customer insights to serve up personalized product recommendations relevant to each visitor.
Product Page
IMA started by adding these personal, automated recommendations to its product pages, which is the best place to start if you are new to onsite recommendations. The recommendations are based on the “viewed this, viewed that” algorithm and they have a 38% higher click-through rate than the site’s average.
Checkout Page
Based on this success, IMA added recommendations to the checkout page and the results were even better. The recommendations on this page use the “frequently bought together” algorithm and they have a click-through that is 140% higher than the site’s average. More importantly, with a 21.3% conversion rate, it is evident that customers are using these recommendations to add more merchandise to their transactions.
Together, the onsite recommendations have a 9.9% click-through rate and a 1.8% conversion rate, accounting for 36% of the total e-comm revenue.
Email Recommendations
IMA also added personal product recommendations to some of its email messages as a way to engage shoppers and assist with product discovery. Since Oct. 2014, personal product recommendations in emails have accounted for nearly 30% of the total email revenue.
Triggered Messages
Starting with its triggered messages, IMA added product recommendations to both its shopping cart abandonment messages and its post purchase emails.
Its three-part cart abandonment series averages a 35.2% open rate, a 13.6% click-through rate and a 24.5% conversion rate. This conversion rate increased 15% with the addition of the recommendations, which are based on the algorithm “frequently purchased together”. The recommendations make up 15.8% of the total revenue for the cart abandonment series.
The post purchase message, which use the “purchased this purchased that” algorithm, averages a 52.3% open rate, a 19.7% click-through rate and an 8.7% conversion rate. The recommendations make up 43.3% of the total revenue generated from the post purchase message.
Weekly Messages
IMA also wanted to boost the engagement and relevancy of its weekly email newsletter and found that including personal product recommendations based on inventory considerations really works. These recurring automated messages, which launched April of 2015, have a 7% higher conversion rate than IMA’s manual campaigns that do not include product recommendations. The recommendations generate 84.7% of the total email revenue brought in from these messages.
Personalized product recommendations in email campaigns really work. IMA averages 9.7% click-through rate and 1.3% conversion rate for emails that include the recommendations. These recommendations drive nearly 30% of IMA’s total email revenue.
About IMA
International Military Antiques (IMA) is the world leader of military collectibles and antiques. IMA has supplied the world’s foremost museums such as the National WWII Museum, Natural History Museum of Los Angeles and West Point Museum, film productions such as Saving Private Ryan and Band of Brothers, private collectors and historical reenactors. However, the majority of IMA customers are everyday people that share IMA’s passion for history. Learn more about them at www.ima-usa.com.
Questions about how Listrak’s Personalization Engine can help you achieve these same results? Let us know in the comments section.