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A leading department store chain partnered with ZineOne to harness real-time trending data to deliver optimal shopping experiences that influence behavior and conversion with social proof strategies. This tactic yielded robust results with experiences that reduced product confusion, instilled price confidence and generated product urgency
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.
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.
To nudge more visitors to go from browsing to making a purchase, the store enlisted ZineOne 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.
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.
After a quick deployment of ZineOne tags on the store’s website, the retailer leveraged in-session marketing to:
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.
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.
ZineOne’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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