Customer Preference Center Overview

Overview

While personalized recommendations built on information gathered with Omnichannel Personalization's instrumentation and based on an individual shopper's behavior can be very powerful, enabling the customer to tell us what he/she actually likes or doesn't like can take personalization one step further. Imagine enabling your shoppers to "Like" or "Dislike" a product, brand or category, or even let us know that they are not interested in something and then using that information to provide recommendations.

This overview is intended to inform retailers interested in using the Customer Preference Center, in concert with the User Profile Service to provide highly personalized shopping experiences to their customers.

Benefits 

Behavioral product recommendations are based on implicit signals from an individual as well as the aggregate of many shopping sessions (Wisdom of the Crowd). The consumers' explicit preferences are a very strong signal that has been absent from personalization. Enhancing the user experience by empowering customers to specify what they like and what they don’t allows you to use those specifications to drive their individual recommendations.

How It Works?

Omnichannel Personalization provides the APIs to capture the consumer’s preferences and integrates this data to enhance personalized recommendations for the consumer. In real-time, we boost "like," de-boost "don’t like," and remove recommended products that the shopper indicates as "not interested."

Consumers can submit their preferences as they shop by interacting with product thumbnails on the website, app or kiosk. Or, the retailer can provide a dedicated preferences page for consumers to view and update their preferences.

Highlights

  • Capture Shopper Preferences - Retailers can define their own user experience to capture shopper preferences and use APIs to enable the capture, view or update of preference information.

  • Preferences Accessible by Retail Sales/Service Teams - Consumer preferences can be accessed and updated by authorized retail employees to deliver more personalized shopping and customer service experiences.

  • Omni-channel accessibility - Preference data is stored on our User Profile Service and can be accessed on through any digital interface - mobile, online, call-center, or in-store via POS, Sales associate device or self-service kiosk.

  • Reporting - Access performance metrics including engagement rates and co-hort analysis through reports to track lift customer lifetime value.

  • A/B and MVT - Test and confirm performance of preference-based strategies with our A/B multivariate testing (MVT) capabilities to determine which strategies and placements perform best for your consumers and your digital storefronts.

Sample Use Cases

These use cases illustrate possible applications for use of preferences. 

Additional development may be required.

“Liked” Items:

  • Shopping list or wishlist that is accessible by the shopper across devices

  • Accessed when the shopper is logged in on any device for real-time personalization

  • Accessible by the Sales associate / Customer Service Rep

  • Accessible by social network if the shopper chooses to share their “likes”

“Liked” Brands/Categories:

  • Alerts and notifications for special events, promotions, or new/updated content

  • Personalized Brand- or Category-based recommendations

Favorite Stores (Requires store and inventory feeds):

  • Used for filtering of inventory/recommendations

  • Used for recommendations/availability/new products/offers by location

  • New Arrivals at your location

  • Upcoming Events at your location

  • What’s hot at your location

Other Preference-based strategies

  • Show more like this

  • People who liked ___ also liked ____

  • People who liked____ purchased _____