AI-based personalization provides 10%+ revenue uplift to this top retailer
AI-based personalization platform provider ZineOne reveals how personalized in-the-moment engagement increased net revenue by over 10% for a top retail chain.
AI-based personalization platform provider ZineOne reveals how personalized in-the-moment engagement increased net revenue by over 10% for a top retail chain.
ZineOne’s award-winning AI-based personalization platform uses predictive modeling to help businesses understand and respond to in-the-moment customer activity.
Dubbed an “intelligent customer engagement platform,” ZineOne’s technology enables retailers to supplement existing stored customer data with third-party and in-session browsing data to provide relevant and personalized in-session experiences via their website, mobile device, kiosk or any other channel.
ZineOne’s most recent case study discusses the challenges that a top 10 U.S. department store chain faced with providing contextually relevant and engaging offers to their website and mobile users.
The case study highlights the retailer’s key obstacles, provides a detailed overview of how ZineOne helped them tackle their challenges using AI and predictive modeling, and presents some truly impressive results.
The case study, AI-Based Personalization Provides 10%+ Revenue Uplift, is available to download from here.
Content produced in collaboration with ZineOne.
The influx of pop-ups, push notifications, emails, and other offers from retailers can be overwhelming for consumers. This overload leads to lower conversion rates and more cart abandonments.
In order to stand out with their customers, a top 10 U.S. department store chain knew they needed a technology that could help them support relevant, contextual customer engagement in real-time.
The retailer partnered with ZineOne, an AI-based personalization platform that provides insights on each individual visitor across digital and physical channels to achieve this goal.
The retailer has over 100,000 employees and $15+ billion in revenue across over one thousand stores.
Writes ZineOne, “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 retailer faced several challenges to implementing a more robust customer engagement strategy—the main one being a lack of access to in-session customer data that could supplement existing stored customer data.
Writes ZineOne, “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.”
The retailer enlisted ZineOne to help them deploy relevant, personalized engagement using AI-based recommendations which incorporated in-session user behavior.
They also integrated customer data from various other platforms, unified data into a single user view across channels, and used machine learning (ML) to analyze data in real time, comparing it against historic data points to get a more accurate prediction (and help influence) in-session purchases.
ZineOne’s Intelligent Customer Engagement (ICE) platform enabled the retailer to automate in-session interventions which were based on continuous, cross-channel customer intelligence.
This was done via the use of a patent-pending “Customer DNA” technology recommends actions to incentivize visitors based on real-time, relevant information such as hyper-personalized offers delivered to visitors while they are shopping.
Per ZineOne, Customer DNA, “Allowed the retailer to meaningfully react to user activity as it occurred, based on what the intelligence layers predicted as most appropriate for each visitor.”
Source: ZineOne
Once ZineOne’s technology was implemented, the retailer saw impressive results with up to 90% accuracy in the predictive models based on in-session user behavior.
The company also saw a 50+% redemption rate and a 12% net revenue lift for personalized offers.