In July 2015, Barilliance (an e-commerce personalization recommendation engine) produced a data report on different types of product personalization methods and the effectiveness of each of these methods. The data was collected from 1.5 billion shopping sessions over three months in the second quarter of 2015 using data came from ecommerce websites that use Barilliance’s personalized product recommendations engine. The report included data from a variety of different stores in a number of different countries that sell a wide assortment of products.

The report showed that:

“Visitors who viewed this product also viewed”generates the most revenue

-Showing customers products that other similar customers viewed generated 68.4% of all revenue out of the sites surveyed.

-In addition, 57.5% of sites used this type of recommendation, making it the most-used type of personalized product recommendation.

“Visitors who viewed this product ultimately bought” is also successful

-25.1% of all revenue was generated using this type of recommendation.

-However, only 7.3% of sites use this recommendation type.

“You might also like” generates the third-most revenue

-16.1% of revenue was generated using this recommendation.

-However, 42.5% of all sites use this type.

As you can see, some of the highest revenue generators are not the most common types of recommendations in the real world. Conversely, some of the most used methods are not the most effective. Consider “Customers also bought” which is used by 30.9% of sites, but generates only 7.9% of revenue.

These stats show how important it is to choose the right type of personalized product recommendations, not just the most common.

Creating Effective Personalized Product Recommendations

-As you can see from the data presented above, the effectiveness of a product recommendation varies depending on how it is presented. In order to boost sales for your e-commerce business, you’ll want to take the time to make sure that you create effective personalized product recommendations.

Present Relevant Suggestions

-Customers are significantly more likely to purchase relevant products. This is obvious. However, making suggestions relevant involves measuring and reacting to a customer’s behaviour and preferences.

-For example, if a customer was interested in shirts in size large, only shirts that are currently available to order in size large should be shown as recommendations. Suggesting shirts that are only available in size small could frustrate the customer.

Use Engaging Recommendations

-It’s important to make recommendations appealing to the customer. This can be done in a number of ways, including showing high quality images or details of a product on the recommendation page, rather than making the customer click through to a new page to find out this information.

Don’t Distract the Customer

-When and how you make recommendations matters. Recommending other products on a “product details” page or in the customer’s cart page can cause the customer to get distracted or second guess his or her purchase. This could actually make a person less likely to make a purchase at all.

Give Customers Options and Control

-Many people are wary, skeptical or concerned with the amount of personal information that companies have about them.

-Therefore, it’s important to give them privacy options such as the ability to delete their browsing history or a way to leave feedback on the recommendations that they are shown.

Don’t Forget Customer Service

-It can be tempting to set up a robust and effective personalized product recommendation system and then allow this system to be the sole way that you service your customers. However, this isn’t what many customers want.

-It’s crucial that you provide your customers with other ways to get help, such as detailed FAQs, a live chat or a 24/7 phone number that they can call with questions.