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Retail Gets Personal With AI and ML

Posted on: April 19, 2018 | Posted by: Trevor Duguay

Retailers have now universally accepted that connecting their online and offline customer journeys with real-time, personalized engagement will be critical for competing with aggressive threats from online leaders such as Amazon. As this acceptance has taken shape, a further realization is that such capabilities will require significant investments in customer experience technology. In particular, Gartner reports the top three retailer investment priorities in 2018 are CRM/Loyalty, e-Commerce and Personalization. What this report seems to have missed is the widespread adoption of ML and AI capabilities across these three technologies.

Indeed, if you attended Shoptalk in Las Vegas last month, you just couldn’t have missed all the buzz around ML and AI. As an exhibitor or attendee, if you weren’t talking about such capabilities in your product or your tools stack, then you were clearly last season’s fashion.

There’s no way that today’s customers’ expectations can be met with manual processes or decision-making that take days, hours, minutes or even seconds. Today’s retailers need to meet customer expectations instantly or risk losing to a plethora of alternatives. That means being able to track individual customer events in truly real-time, 24/7/365, across all digital and physical channels and responding in milliseconds with highly personalized, contextual communication. This complete loop of contextual insight to contextual action while the customer is still on the channel is the key to holding customer attention and offering a truly delightful experience that will win not only their business but also their loyalty.

To achieve this level and speed of personalized responses, there needs to be a conscious shift toward a new generation of systems that can respond in milliseconds at-scale. Additionally, they must do so while learning and adapting these responses to the evolving conversation with the customer and in-the-moment context changes. So, we can see that ML and Deep Learning applications are central to this emerging wave of hyper-personalized, in-the-moment, customer engagement – across both stores and digital channels.

That’s exactly what ZineOne’s Customer Engagement Hub for Real-Time Personalization was built to do at scale. Our all-in-one platform brings together individual customer contextacross the retailer’s systems of customer engagements (website, mobile apps, stores, etc.) and systems of record (existing data, campaigns, and / or CRM), in real-time and at scale. The platform uses Machine Learning techniques to understand customers’ preferences, needs and intent at an individual level to enables the retailer to interact with every customer in a relevant and personalized manner. The platform can support a variety of online and in-store use cases, such as:

  • Use store visits to convert “stalled” online purchases or to recover abandoned carts
  • Intelligent in-store assistance
  • Smart in-store and online product recommendations and personalized messaging
  • Improved in-store and online customer experience

To read more about these use cases and to learn how retailers can achieve such contextual relevance and real-time personalization, download our whitepaper on Harnessing AI to Power Event-Driven Contextual Engagement.