“Customers who bought this item also viewed.”
“Related products that may be of interest.”
“Items you have recently viewed.”
Today, we’ve become accustomed to finding these recommendations whenever we visit a marketplace or online store. Sometimes we pay these little attention, believing that these suggestions are generated randomly, or in an attempt by the seller to pressure us.
The reality is that these recommendations are usually personalized and based on complex algorithms which feed these so-called ecommerce recommendation systems.
What are recommendation systems?
Recommendation systems are created based on software capable of collecting and analyzing a wide range of information regarding visitors to an online store. Based on this huge volume of information, it can then intelligently process the habits of each user, and suggest products, offers and deals best suited to their interests.
In order to achieve this result, a recommendation system’s algorithm will analyze each user’s visits, viewing and purchasing activities carried out, reviews and ratings, and the time spent per page and on which types of products and categories.
Some systems also include language analysis, which allows them to deduce which keywords will have the greatest impact for each type of user when personalizing their shopping experience.
Amazon states that around 35% of its income comes from product recommendations.
This is proof that personalizing customer shopping experiences and encouraging the discovery of new products are key strategies for ecommerce businesses.
Benefits of a recommendation system for online stores
- Between 15% and 45% increase in conversion rate.
- 25% increase in average purchase price.
- Increased customer life cycle.
- Creation of customer loyalty.
- Better sales through upselling and cross-selling.
Types of ecommerce recommendation systems
Filtered based on content
This is the most effective ecommerce recommendation system, offering the fastest results. With just one user, the system can start to analyze their behavior and create personalized suggestions.
The most well-known examples are Netflix and Spotify, platforms based on personalization algorithms that analyze the content consumption of each visitor, in order to suggest films, series and music related to their tastes.
Content-based recommendations analyze all of a visitor’s previous visits and purchases in order to filter the products that best suit their apparent likes and interests.
For example, if a buyer has visited a brand’s store five times to view backpacks, the algorithm will interpret this as their principal search and will suggest related equipment products or better models of backpacks in order to upsell.
The downside of this type of system is that it requires a great deal of continuous data input, and must also analyze huge quantities of information if the store experiences a high volume of traffic. This requires many hours of analysis, as well as repeating and updating predictions every time new buyers visit or if changes are made to the product catalog.
What’s more, to ensure that recommendations are effective and valuable for the buyer, product content must be well prepared and categorized. This way, the algorithm will always be able to find the most precise related products. A PIM (Product Information Management) system is a vital working basis for ensuring that an ecommerce recommendation system offers good results.
Collaborative filtering
This system was made popular by Amazon, and you’ll recognize it with its typical “Other customers also bought” section.
Recommendations are made by offering visitors suggestions of recommended products or items that have been highly rated by similar users. The system acts as a bridge between buyers that it considers to be similar, instead of creating unique suggestions based on each user.
How to use a recommendation system in your ecommerce business
The breakthrough in recommendation software was announced in 2017, when Amazon made its DSSTNE (Deep Scalable Sparse Tensor Network Engine) recommendations system public. Based on this, many developers have been able to work with the software – although various other recommendations systems for online stores are available: such as Nosto, Sentient, Qubit or Certona.
With recommendations software, there’s no need for any complex installation, and the system will take care of combining the database you wish to track (type of customer, location, visit time, purchase history, viewing habits, etc.), analyzing this in order to make personalized suggestions and carry out upselling and cross-selling actions.
These systems can be applied to websites, ecommerce mobile apps, online-store pop ups, personalized emails, a/b tests, and segmentation.
Should you use a recommendation system?
Modern buyers expect a totally personalized shopping experience every time they shop online. An ecommerce recommendation system is the most specialized solution currently available, allowing stores to offer appropriate product and promotional suggestions to each visitor.
These systems are essential for encouraging buyers to make larger purchases and increasing conversions and ecommerce customer loyalty, as customers appreciate the personalized service.
To guarantee success, you should combine your recommendations system with a PIM system that ensures comprehensive and precise product information, through which smart suggestions can be made.
Start today by enjoying a free trial of Sales Layer and see how your ecommerce recommendations improve when your products offer information tailored to your customers’ needs.
