Product recommenders: How New Product Discovery recommendations can improve personalization
When companies establish growth as an organizational goal, there are several sales and marketing strategies that can help realize this objective. One strategy, personalization, is incredibly effective at driving sales and increasing marketing ROI.
In a nutshell, a personalization strategy focuses on creating targeted experiences by leveraging customer insights such as product, content, and channel preferences.
Product recommendation and personalization
A recent McKinsey survey of senior marketing leaders found that…
“Only 15 percent of CMOs believe their company is on the right track with personalization. But there’s a big incentive to figure it out. Today’s personalization leaders have found proven ways to drive 5 to 15 percent increases in revenue and 10 to 30 percent increases in marketing-spend efficiency—predominantly by deploying product recommendations and triggered communications within singular channels.”
– McKinsey & Company, The Future of Personalization
Product recommendation, a critical piece in a personalization strategy, as noted above, is where a recommendation engine seeks to predict the products, services, or items that a user would be interested in purchasing.
These predictions are embedded throughout the customer journey, such as targeted products on an ecommerce site, or in email campaigns based on data such as customer attributes, browsing behavior, or situational context. Recommendations help provide a personalized customer experience, resulting in an increased share of their wallet.
Types of recommendations
Recommendation engines can aid in the following areas
- New Product Discovery
- Complementary Products
- Repeat Purchases Reminders
In this post, we will tackle New Product Discovery, subsequent post will cover the remaining flavors of recommendation engines.
New Product Discovery
New Product Discovery leverages historical purchase and browsing patterns for specific customer segments to surface up potential recommendations. These can manifest in the “Frequently Bought Together” and “You Might Also Like” sections on the sites of online retailers.
As previously noted, this discovery does not end at checkout. Customers can then receive follow-up communication that continues to suggest new items, potentially bringing them back to the site.
While suggesting new goods to customers is nothing new, New Product Discovery customizes these offers based on an individual’s behavior and shopping habits. This increased level of personalization increases the chance that the consumer ultimately says “yes” to whatever is suggested.
One of the ways that models identify products to suggest is by identifying bundles of related goods. The following diagram is a conceptual view of the steps that can be taken to identify products that have the highest propensity of being bought by the consumer.
New Product Discovery is just one of the many ways to leverage product recommenders in your personalization strategy. Stay tuned for deep dives into other popular “flavors” of recommendation engines including Complementary Product Recommendations and Repeat Purchase Reminders.