Major department store chain boosts revenue with next-gen social proof strategies


What is social proof?

Social proof—such as reviews, likes on social channels or the website, and personal recommendations—has become a powerful tool to drive sales. The concept succeeds by showing that other consumers are also interested in a product, hence the product must be good. According to The Psychology Behind Trust Signals report from Trustpilot, 66% of customers said the presence of social proof increased their likelihood to purchase.

The challenge

Most businesses are familiar with some common social proof mechanisms where site visitors are notified if the items they are viewing are dwindling in stock, or the concept of those who bought X, also bought Y. At the same time, retailers have mastered the art of analyzing stored customer data to create personas and segments that fuel basic personalized recommendations; yet many retailers struggle to account for visitors’ changing needs, intent, channel, and location. To achieve its goals, the department store needed a solution to augment the data from its enterprise systems with in-session intelligence, enrich it with third-party data, and surface these insights while the visitor is still browsing the website.

The solution

To nudge more visitors to go from browsing to making a purchase, the store enlisted Session AI for our in-session marketing capabilities. Powered by our patented machine learning (ML) models, this retail-focused template captures real-time trending data on the number of views for a product, purchases, and inventory. Additionally, it allows for the real-time display of this information on the product detail page (PDP) while the visitor is viewing the product.

Some examples of this information is shown include:

  • Availability of the item
  • The number of people that have this item in their cart
  • The number of people that have recently purchased this item
  • Identification of items that are trending overall and in a particular region
  • The list of items that are being viewed by others in their area
Social proof for product confidence

The result

This tactic yielded robust results for the department store since it applied to a large cross-section of its target customer base. The company recorded $52 million in incremental revenue by proactively showing information on high-demand products.

How did it happen?

After a quick deployment of Session AI tags on the store’s website, the retailer leveraged in-session marketing to:

  • Ingest real-time activity data to identify a visitor’s interest in a product and determine if they showed intent to purchase during the session.
  • Capture real-time trending data on other visitors’ interest in that product
  • Tap into first-party data for historical insights
  • Perform real-time inventory and cart checks for a particular item
  • Tabulate consumer interest in the product based on the visitor’s location
  • Optimize the site experience by surfacing the most relevant information on PDP as social proof to influence behavior and conversion in real-time.

For a site visitor viewing a PDP, the department store automated the display of a badge indicating the number of other shoppers viewing or purchasing the same item at that moment. The marketing team customized this experience by specifying the maximum number of products, the minimum number of viewers, and/or the minimum purchases required to trigger the display of the badge, as appropriate for each PDP.

Additionally, the team tailored the social proof to the visitors’ zip codes—their local communities. Using this intelligence, the team tapped into communal feelings of excitement and anticipation to display what’s trending in that area. For instance, if an unexpected cold wave and snowfall swept over Lake Tahoe, CA, it informed the residents that ski gear is selling out fast. Or when a particularly exciting 49ers football game was coming up, those in the San Francisco Bay Area were shown information about their favorite team jersey’s availability.

Social proof by location

In summary, the department store created highly individualized experiences that capitalized on the wisdom of crowds to reduce product confusion and instill price confidence and product urgency. The store generated $52 million in incremental revenue by proactively surfacing social proof or peer validation to enhance its site visitors’ digital shopping experience.

Session AI’s patented ML models run on AWS’s highly resilient architecture using EC2, S3, WAF, CloudFront, Config, and CloudTrail to deliver a significant increase in conversion rates for ecommerce sites.

Last Updated: August 5, 2022

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