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30-second summary:

  • ZineOne’s AI-powered personalization platform uses predictive modeling to help businesses understand and respond to in-the-moment customer activity.
  • ZineOne recently published a case study that discusses the challenges a top 10 U.S. department store chain faced with providing contextually relevant in-session user engagement.
  • The retail chain has over 100,000 employees and $15+ billion in revenue across over one thousand stores.
  • The retailer enlisted ZineOne to help them deploy relevant, personalized engagement using AI-based recommendations which incorporated in-session user behavior.
  • 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.

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.

AI-based personalization

Content produced in collaboration with ZineOne.

Cutting through the clutter of retail offers

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.”

Lack of access to in-session customer data was the key challenge

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.

A summary of the challenges, as noted in the case study, are as follows:

  • Access to in-session user behavior and real-time context
  • Inability to connect every customer’s cross- channel context
  • Sub-optimal customer engagement with low offer take rate

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.

AI-driven, real-time personalization was the solution for this retailer

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.

Some details about Customer DNA shopper behavior:

  • Customer DNA is a constantly changing stream of behavioral data for each shopper
  • The data is augmented by cross-platform, environmental insights which provides continuous intelligence about each customer
  • Customer insights are optimized with ML-based models embedded in the ZineOne ICE platform

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.”

The Results

AI-based personalization results

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.

For more detail on this top retailer’s approach to hyper-personalized contextual personalization, download the ZineOne case study: AI-Based Personalization Provides 10%+ Revenue Uplift.

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