Recommender Systems
Recommender Systems
The way to increase your sales and boost your products
The e-commerce market is facing drastic changes brought on by new technologies. Your stored data has the potential to improve your sales performance. A fruitful way to exploit data based on this perspective is what we call Recommender System.
Detecting interesting purchasing patterns automatically generate suggestions based on a combination of rules. This allows us to automatically predict your customers’ buying behavior, increase your direct and indirect revenues and drastically improve and personalize user experience. In that way, you can recommend the right products to the right shopper at the right time!
Recommendations not only lift conversion rates, they help deliver improved user experience to keep shoppers coming back and can boost the average order value. This can motivate “just-browsing” visitors to purchase an item, help “lost” customers to find their dream items and encourage “big” shoppers to add additional items to their carts.
Product recommendation and our continuous efforts to improve performance, result in an optimization of the product discovery process, better customer experience, higher engagement, conversions, average order value, loyalty, and retention.
Every online shop targets to increase sales especially when there are distinct patterns in the data that suggest that certain products are to be sold.
What is the ideal strategy to do that? What is the optimum way to move forward?
InfiLabs’ added value
There are always products that require extra attention to get them sold that the rest. Such cases may involve seasonal products or products that have a temporary discount etc. Those products are prioritized and occupy important positions in our proposals. In order to boost the products that you want, we create highly targeted proposals, using the appropriate filters and limitations.
Each recommender system algorithm varies according to the nature of the products. However, the basic logic is always the same and is presented below.
Step 1: Analysis of purchasing data.
We analyze data in order to extract useful insights that will lead us to the development of the right strategy. We identify purchase patterns and associations between certain products based on the shopping baskets and customer traits/online behavior.
Step 2: Selection of appropriate filters.
In order to produce accurate recommendations that make sense, we wisely select filters considering prices, recency of purchases and segmentation of products. Having acknowledged the general purchase habits, the recommended products are being selected sensibly by following specific restrictions.
Step 3: Promotion of specific products.
We incorporate products that the eshop wants to promote and boost into the recommendations.
Step 4: Presentation of the recommendations.
There are several ways to communicate the recommendations to your customers. One classic way is to build a real time recommender system on your site. Another effective way is via email. Predicting what customers are likely to buy next and dynamically serving those products into your customer’s lifecycle email campaigns, will boost your top-line. Large companies have wisely chosen to go both ways.
A recommender system will definitely improve your relationship with your customers. Providing them with the right recommendations, their navigating experience will be improved and their loyalty to your site will be strengthened. Retailers who implement new marketing analytics technologies, stay ahead of the competition. Be one of them!
Τrust your data with InfiLab and we have the way to use it for your benefit. Don’t hesitate to contact us.
Yours,
Dimos Beleveslis
- Posted by mpitoglou
- On May 29, 2018
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