Today’s consumers are buried under an avalanche of noise. But companies that deliver tailored, personalized experiences are coming out ahead. That’s why e-commerce leaders need to pay close attention to their product recommendation strategies. In addition to analyzing historical purchases, you need to evaluate the many different variables that influence your shoppers’ propensities to buy.

Here are 3 ways to take your product recommendation algorithms from ‘status quo’ to exceptional.

1. Consider individual preferences

Individual consumers have unique shopping preferences and will often consider a range of criteria, including recommendations from friends, brand affinity, and general taste to make their buying decisions. In addition to understanding what these preferences are, e-commerce retailers should study the patterns behind them.

You can systematically capture data on each customer’s shopping patterns, past session behavior, previous purchases, wish lists, and browsing habits. Then based this information, automatically present shoppers with the products or content that they care about most or that will resonate most effectively—which will be different from person to person.

2. Account for popularity 

Predictive analytics are powerful—until your algorithms develop tunnel vision.

While most consumers tend to be  creatures of habit, they also  love exposure to fresh, fun, and new ideas. Retailers are in a great position to make product recommendations but are often hesitant to take ‘leaps of faith’ with items that shoppers may not want to buy.

One way to minimize this risk is to recommend items based on overall shopper popularity while also factoring in specific individual preferences. You should be able to adjust your algorithms to display products dynamically based not only on factors like most viewed and purchased but also on seasonality, geography, stock levels and individual buyer affinities and purchase patterns.

3. Integrate social data

If you’re managing an e-commerce website, you’re likely collecting a wealth of audience data and noticing clear patterns among your target customers. Put this information to use by creating ‘socially enhanced’ recommendations. Here’s what you need to do:

  • Uncover what your shoppers are researching, reviewing, and buying
  • Leverage those insights to identify and document key personas and social affinity groups
  • Allow your visitors to self-select into social groups simply based on their behavior
  • Automatically bring relevant products to the surface throughout their buying journey

By using social data you’ll build upon the best of two worlds: surprise and relevancy. You’ll introduce your audience to new products using intelligent and relevant recommendations.

Your recommendation algorithms should be flexible enough to accommodate a variety of shopper signals. You need to do more than show shoppers items that they’ve previously browsed—or that their friends are browsing, for that matter. Learn what your audiences care about and optimize those opportunities. That’s the key to better product recommendations!