Recommendation vs. Personalization in Retail2 min read
In the retail economy, customer loyalty means the difference between life and death. Personalization is a key currency.
In a world driven by algorithms and preferences, using data to power these experiences is important because it shows investment in the customer.
Brands and retailers are faced with fierce competition for acquiring new customers and retaining those they have. The most successful retailers will move beyond simple targeting and segmentation, and beyond product and content recommendations based solely on what happened in the past or what others, like the shopper, have in common.
The real power comes when artificial intelligence and machine learning are used to target shoppers with recommendations that can accurately predict hidden interests:
- Find patterns between shoppers’ viewed products, and show similar products to drive bigger basket sizes and repeat purchases.
- Look at a shopper’s purchase behavior and recommend complimentary items. Interested shoppers will find inspiration in seeing what other customers have also bought along with their current product. This automates the cross selling across all channels.
- Highlight new products for customers that are more interested in new arrivals, and find different products for those less interested.
- Recommend similar items to the ones that the shopper recently browsed. It is a powerful way to drive engagement and conversions.
- Sometimes shoppers don’t find what they are searching for. Include semantic analysis in your product search engine to improve results.
Interested in going beyond static recommendations to discover what your individual customers really want? Check out our white paper, “What if Retail Isn’t Dead, It’s Just Evolving?”