Data-driven insights are the foundation for every decision. Marketers don’t have time to guess what users want. They use personalized data to give shoppers what they want when they want it, no matter the purchase channel or device used to accomplish the task.
That's why product recommendations have become a basic requirement for any e-commerce business that wants to increase customer engagement, drive revenue and grow at scale. With advanced algorithms and powerful recommendation systems, the technology leverages aggregated data from customer interactions from both online and offline channels to prepare personalized offers for each customer with the products and services most likely to interest them.
Product recommendations that are contextual and personalized to each visitor significantly increase customer loyalty, conversion rates, and purchase values. It’s the most powerful tool in your e-commerce arsenal, especially when aligned with the customer journey.
There is no one size fits all approach. But you should make sure that your product recommendations go beyond the general "You May Also Like" product recommendations and are tailored to the specific needs of different users at different stages of the funnel.
Below, we'll share some tips and tricks to help you create engaging recommendations and tactical solutions that you can build on to maximize the effectiveness of your efforts with Synerise.
1. Increase profit margin
Increasing profits is the ultimate goal of business. To achieve specific profit margin targets, marketers must create an effective plan to achieve these goals. One of the ways to increase profits is to recommend higher-margin items than the average margin of items typically purchased by customers while personalizing the results of product recommendations. You can learn how to implement this scenario in your company with this use case we created for you to better understand this topic.
We also encourage you to check out the following use case, which describes how to promote high-margin products in all types of recommendations.
2. Include customer feedback & known preferences
Customers provide the best insights about their tastes and needs in the form of events (which are the result of their interaction with your site). They contain all the necessary information that you can use to better tailor product recommendations to individual customer preferences.
For example, by identifying customers' buying patterns or search patterns, you can determine their favorite color and use this information to reinforce recommendations with products in the customer's favorite color. Such minor reinforcement of recommendations creates additional value for customers and influences their purchasing decisions by allowing them to easily access products with features tailored to their needs.
Check out this use case to learn how to create recommendations with items in customers' favorite colors.
Another product feature that can be used for a great effect is a brand. This very powerful feature plays a significant role in the customer decision-making process. Shoppers attach great importance to brands. Once you give them direct access to products from their favorite brands, you improve their shopping experience and make them more satisfied throughout their shopping journey.
If you'd like to learn how to create recommendations to boost customers' favorite brands, check out this use case that describes the process of creating such a campaign.
The ability to add products to favorites is a smart way to simplify the user experience, allowing users to collect the products they like while browsing. It's a great enhancement that also helps marketers better promote products that customers are interested in, thereby increasing sales.
To learn how to effectively promote customers' favorite products in recommendations, check out this use case that will guide you step-by-step through all the necessary stages of implementing this scenario.
3. Collect customer feedback with recommendations
Recommendations also can be a source of collecting customers’ feedback about your products or services. Collecting such information, processing it properly, analyzing it, then using it in subsequent communications allows you to better tailor your message to the individual customer.
One of the example scenarios that you can implement to collect customers' feedback with the help of recommendations is an in-app campaign with a swiping mechanism that allows customers to choose products they like or dislike. This information can be used in further communication and boost recommendation campaign results with the liked products or filter the campaigns and exclude disliked ones.
We encourage you to check out this use case that describes the process of creating an in-app mechanism presenting bestsellers with the swiping system.
It's a great scenario that's both engaging for customers and, at the same time, very informative and full of insights for marketers, who benefit greatly from the feedback they receive.
4. Include customer size
One way to recommend products is to recommend items in the customer's size. You can store an additional attribute in the customer's profile that contains information about the most frequently purchased product sizes and then use this attribute to build a filter in the recommendations. In this way, you help customers get to items in their size quickly, ultimately avoiding the possible frustrations that can come from constantly encountering products in the wrong size.
Check out this use case that shows how to create a recommendation that serves 4 personalized items in the customer's size.
5. Differentiate recommendations for different segments of customers
It's important to tailor product recommendations to different customer segments with different needs at different stages of the customer lifecycle and have completely different buying patterns. To achieve better results, it's worth using predictions to help you create more comprehensive customer segments that address your specific business needs.
To increase the effectiveness of your recommendations, you can use the Prediction module to set high lifetime value (LV) customers as your target group. With effective customer segmentation, you have a better understanding of customers and can design a recommendation strategy tailored to each group with different prediction scores. To learn how to create recommendations for high Lifetime Value customers, check out this use case.
You can also check another scenario created for customers at the risk of churn. Check this use case and find out how to promote discounted items to customers at risk of churn.
Product recommendations are directly tied to revenue generation, and they are its most effective tool. There are many strategies that can be used, so you should experiment with them in order to maximize profits. By testing different placements and comparing results, you can gradually develop the best strategy that will help improve sales.