Leverage a new class of affinity-based audiences for hyper-targeted experiences

Leverage a new class of affinity-based audiences for hyper-targeted experiences

Create experiences the user is more likely to engage with now, and as their preferences change over time using affinty-based audiences.

Every user interaction with a site or app provides valuable signals about their preferences and intent. If a user adds an item to their cart, we can surmise an interest in some or all of the attributes associated with the particular product at hand. And combined with the significance and recency of the engagement, it can allow companies to both create experiences the user is more likely to engage with and better recognize changes in user preferences over time.

However, much of the rich browsing data described above has gone completely unused. Too complex to untangle and action as visitors become exposed to a wider range of brands, colors, styles, prices, and more, important correlations between the product a user is interacting with and it’s many attributes end up being missed.

To solve for this, Dynamic Yield now automatically collects, analyzes, and calculates this data to build unique affinity profiles for each individual in real-time. In turn, marketers can utilize these sophisticated new user segments across web, mobile, app, and email as well as in external third-party platforms like DMPs, BI tools, DSPs, and more.

Dynamic Yield’s approach to affinity

We’ve been in the game for a while now, and our affinity-based personalization capabilities are growing to support new and emerging applications as the process of affinity modeling evolves.

Our recommendation engine taps into direct engagement with product feed attributes, using data aggregated through rich affinity profiles to serve the most appropriate experience variations and products to each visitor. We also provide multiple methods and algorithms to power how these recommendations are personalized, allowing teams to employ deep learning and neural networks as well as continuously test and optimize a mix of affinity and popularity scores to best fit their needs.

And the affinity scores powering these recommendations don’t live behind gated walls. With our Unified Customer Profile API, this history of online activity can be fed through Dynamic Yield, or into any other system, such as a chatbot, smart TV, clienteling app, and more for the creation of other highly-tailored experiences. 

For example, using our Affinity Profile tool, a new user who lands on a site for the very first time would have an empty affinity state.

An affinity score populating as the user interacts with different products

Over the course of the session, the user’s affinity score would begin to populate, with important actions and behaviors like visiting a product page or adding an item to the cart contributing to the calculation. Top values are then bucketed according to the various signals of intent for a high-level view depicting which interactions held the greatest significance. 

Top attribute values ranked by significance

Furthermore, marketers can choose to drill into the user’s full profile, analyzing each of the values and attributes captured to better understand the nature of interactions made over time. For instance, what an individual’s score looks like for women’s products versus those for the home.

Diving into a user's affinity scores

And because affinity profiling is tied to a product feed, we make it easy to explore a brand’s catalog, offering helpful suggestions for recommendations based on either a personalized algorithm a popularity-based approach. 

Exploring recommendation strategies based on affinity vs popularity

And the ability to action these valuable user attributes doesn’t end there. 

Making affinity targetable

Up until now, if a marketer wanted to target an audience of people who had a high affinity to, say, the category ‘running,’ they would have to arbitrarily define the makeup of that segment through a series of targeting conditions. Based on historical interactions, this might be visitors who viewed 3 items or running products in the past week and who’s average order value (AOV) was above $75.

With Affinity-based conditions, visitors with preferences to specific brands, colors, categories, and other attributes from the product catalog can be segmented and automatically targeted without having to build the audience manually. 

Targeting users by affinity with Dynamic Yield

A few use cases of how teams leverage affinity profiles for the creation of highly-targeted experiences include:

  • Promoting relevant products in the homepage banner for users with an affinity to brown boots
  • Reordering menu items within the navigation based on the user’s affinity to Men’s pants
  • Moving product rows up on the shoe category page to surface Nike before any other brand
  • Serving targeted discount notifications to users with an affinity to women and/or accessories.

Additionally, you can breakdown any campaign report based on affinity segments, and have Dynamic Yield’s Predictive Targeting solution continuously analyze and identify opportunities to serve the most relevant content for each affinity audience segment.

Turning complexity into actionability 

Affinity-based personalization opens up an entirely new world of potential, giving brands the power to capitalize on what they know about their customers but weren’t previously able to. We’re excited about what the future holds and hope you’ll stay tuned as we continue to expand our offering in this area.

If you want to learn more about our affinity-based personalization capabilities, contact us here

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