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CRM Hack: Smart Recommendation Models for You to Use

Take some examples from Optimove experts for data points you can use to power your product recommendation model

Product recommendation engines have been around for a while but there are some key learnings to leverage.

Firstly, products recommendations are a great way to improve the overall UX whilst simultaneously generating more revenue for your org.

Secondly, customers’ past purchases, web activity, and actions can all be used as predictors of driving new purchases.

But, in order to power smart recommendations, you need access to data.

If you would like to learn more, watch the 2:28 minute mini-workshop or read the transcript below:

Examples of Data to Power Your Model

Here are some data points you can use to predict where/when customers are most likely to purchase and to serve more relevant recommendations:

  • Customer location
  • Demographics
  • Behavioral data (clicks, page views, etc.)
  • Affinity-based data (interests/preferences displayed by users on site)
  • Online/offline purchase data

Use Cases

Once you have your data, there are several recommendation strategies you can use:

  • Bought together – complementary products that are typically purchased together
  • Web activity – your customer’s browsing data can help you recommend the right products. For example, the highest product views or added to cart
  • Recently viewed/ Most popular products / New releases
  • Ratings – these recommendations can be used as part of a hybrid recommendation strategy

These are a few among many recommendation models and strategies your brand can use.

Interested in hearing more? Reach out to us:

Strategic Services Team: services@optimove.com

Sales Team: sales@optimove.com

Don’t miss out on our previous CRM hacks from Optimove experts:

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