“Customer DNA™” and its Impact on Customer Engagement

By Team ZineOne July 2, 2020

In today’s hyper-connected world, customers expect businesses to cater to their needs in real time, all the time, no matter which channel they happen to be using. In order to make this level of engagement a reality, however, companies must secure a new means of understanding their customers; one that is built for the requirements of a new engagement paradigm.

To this end, businesses not only need to access and leverage “Customer DNATM”—the constant clickstream of every customer’s activity data on their digital properties—but they must also ensure that this data is optimized to provide in-the-moment understanding about the ever-changing context of individual customers. Only when enterprises achieve these deep insights into customer readiness and receptivity will they be prepared to delight customers with real-time, personalized engagement.

Personalized Engagement: It’s In Our DNA

For businesses, what makes up the Customer DNATM?

At its core, our patented real-time event analysis with Customer DNATM is clickstream data from the activity pattern of an individual customer as it manifests across a multitude of channels.

Customer DNATM is clickstream data from the activity pattern of an individual customer as it manifests across a multitude of channels. This clickstream data has traditionally been aggregated into channel-specific silos, which reside within software packages such as Omniture or Google Analytics. These platforms allow the data to be leveraged for the purpose of establishing collective insights about customer behavior in aggregate; but could clickstream data also be evaluated at an individual level in order to understand the intent of unique customers in a given session, to then appropriately personalize their user experience?

ZineOne has done just that! Our Intelligent Customer Engagement (ICE) platform uses a real-time event store to capture customer activity into individualized sequences, called Customer DNATM. Just like biological DNA, Customer DNATM is unique to each customer or site visitor. It encodes the full event stream of their short- and long-term behavior data, augmented by environmental insights, across all channels. It forms the framework for continuous customer intelligence and—when analyzed with AI and ML models embedded in the ICE platform that determine exactly when, where, and how a particular message should be deployed to a specific customer
for maximum impact.

Essentially, it allows businesses to meaningfully react to user activity as it occurs, based on what the models predict for each visitor.

Entering the Age of Influence

Furthermore, the platform collects, assimilates, and examines clickstream data for individual customers across channels (web, app, m.com, t.com) to read a customer’s intent in-the-moment and then react in any given session based on what our machine learning (ML) models deem as the “influence zone” of that particular customer.

The notion of “influence zones” adds a layer of immediate situational awareness to the concept of personalization. In the past, personalization was largely based on an individual’s propensity score towards a certain offer or product and their eligibility for that same offer or product. This speaks to the “What?” element of the personalized engagement equation.  Influence zones expand this notion of personalization in order to establish the “When?” element. It considers additional factors such as customer readiness and receptivity. These considerations take into account the customer’s in-the-moment activities and situation to determine the exact point at which he or she will be most amenable to a message. How has ZineOne gained an understanding of customer readiness and receptivity? The answer lies in our machine learning (ML) algorithms, which examine incoming event patterns while they are still in-memory (not yet stored). This empowers us to understand customers’ immediate context and, therefore, adjust engagement based on their “influence zone” in that same session. We have seen significant results with this approach—in fact, our algorithms are able to predict whether a customer will complete a transaction in any given session with 75% accuracy. When a customer is not likely to complete a transaction, the ZineOne platform incentivizes them by using its machine learning models to deliver the right message at the right time based on their current receptivity and readiness.

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