By incentivizing visitors with relevant information in real-time, retailer increased the redemption rate of these hyper-personalized offers by more than 50%.
As a result, retailer achieved +22.6% relative lift in revenue per visitor with in-session offers.
From emails to pop-ups to push notifications, today’s consumers are inundated with offers from retailers everywhere. The influx of irrelevant offers only muddies the waters for consumers, leading to lower conversion and rising cart abandonment rates. How can retailers cut through the clutter and stand out to their customers?
A top 10 U.S. retailer sought an answer to this question. With more than 1,000 brick-and-mortar locations and a growing focus on online channels, this major retailer sought to not just bring more visitors to its online storefront, but to meaningfully engage them once they arrived. To support the relevant, contextual customer engagement it envisioned, the retailer knew that it needed a different solution, one that could take advantage of advancements in data science to deepen customer relationships, brand affinity, and loyalty in real-time.
The key challenge this retailer faced is also true for the retail industry, as a whole—a lack of access to in-session customer data that could supplement existing stored customer data, which when combined, enabled relevant engagement in-the-moment. While analysis of stored customer data allows persona and segments creation that lead to basic personalized recommendations, it does not account for customers’ current channel, needs, and mindset. Hence, a brand cannot meaningfully personalize a customer’s in-session experiences to prevent website or cart abandonment.
An additional layer of short-term insight, driven by machine learning-based models were needed to understand the unique goal and medium of each shopper, whether they were browsing the app, visiting a website, or embarking on a buyer journey via another channel.
In order to nudge more potential buyers to not just browse but make a purchase decision, the company enlisted ZineOne to help it achieve the following:
Deploy relevant, personalized engagement using AI-based recommendations that incorporate insession user behavior
Integrate customer data from various enterprise systems to further enrich the customer context
Unify data into a single, user view across channels
Use Machine Learning (ML) to analyze real-time behavior against historic data points to more accurately predict and influence in-session purchases
ZineOne provided the retailer with a new intelligence layer that allowed it to offer the next generation in AI-driven, real-time personalization. ZineOne’s Intelligent Customer Engagement (ICE) platform automated the deployment of in-session intervention based on the continuous, cross-channel customer intelligence collected with its patent-pending Customer DNA technology.
The platform continuously analyzed Customer DNA insights and made Machine Learning-based predictions about the customer’s current journey and the likelihood of purchase. The ICE platform recommended actions to incentivize visitors with relevant information in real-time increasing the redemption rate of these hyper-personalized offers by more than 50%.
Up to 90% accurate predictive models based on in-session user behavior
50+% redemption rate for personalized offers
+22.6% relative lift in revenue per visitor with in-session offers