Real-Time Cross-Channel Customer Engagement (part 4) – Combining Streaming and Predictive Analytics

In the first part of this 4-part blog series, we presented a business use case wherein retailers are able to engage customers at the right moment armed with insights in real-time (to the second or minute) supported with accurately predicted individual customer preferences.

We also gave an overview of relevant technologies that not only help to solve individual disparate problems, but also have to come together and act in unison. In the second part, we discussed the details of a Streaming Analytics platform, which is leveraged for behavioral analytics on dynamic tracking data to obtain insights about individual customer activity in real-time.

In the third part, we provided insights into the workings of a data science solution for predicting personalized recommendations of products/promotions with cross-channel information of an individual customer.

With an ordered set of predicted products/promotions available and an EPN tool deployed to monitor, in real-time, the activity of an individual consumer in a store, let us see how the above two can be combined to deliver the right message at the right time via the right medium.

The data ingestion layer in a Streaming Analytics platform can ingest and fuse data from multiple sources. For this solution to be successfully deployed, as the EPN tool tracks the consumer in a store, the business process management system (BPMS) performs a few steps to achieve the lock-step (see Figure 1):

  • Consume the output from the EPN tool with information, such as the presence of the consumer in a specific zone, the duration of a consumer in a specific zone, etc.
  • Parse the data with the ordered set of predicted recommendations for the particular consumer
  • Formulate appropriate messages, in context, through various built-in decision rules

For example, when the EPN tool determines that a particular consumer is in Z2 (clothing and apparel), the BPMS tool can extract the relevant recommendation (product or promotion) for the items relevant to that zone.

While the dimensions of “reaVitria Operational Intelligence Platforml-time” and “predictive” are combined through this process, the third dimension of “channel” can also be achieved at the data usage level.

That is, by leveraging the insights about a particular consumer’s affinity to items, gained from online activity information, the right message is delivered to the consumer when in a specific zone inside a physical store.

The inverse can also be achieved with the mix of these technologies. That is, with the help of the insights gained from the activity in various zones in a physical store, a consumer can be engaged with a personalized message via mobile and online channels.

Vitria Operational Intelligence (OI), a fully-integrated streaming analytics software platform, provides not only the ability to detect the consumer presence in a zone through geo-spatial analysis, but also the ability to correlate the moving consumer throughout different store zones (see Figure 2).

Further, Vitria OI has predictive analytics capabilities (through tight integration and deployment of predictive models developed in an offline machine learning environment) that can be leveraged to produce an ordered set of consumer recommendations. As insights about consumer activity across states are extracted by the EPN tool, the predicted recommendations for the consumer can be fused to enact the right message by the BPMS (automated intelligent actions).

VOI-platform Overall, This blog series discussed how a mix of technologies perform individual tasks of a larger business use case and act in lock-step in a Streaming Analytics platform for a consolidated outcome in order to deliver a unified message for an enriched real-time cross-channel customer engagement:

  • Ingest high velocity data from various sensor sources for real-time analysis
  • Glean insights from the real-time monitoring of a consumer in a zone
  • Produce personalized recommendations for consumers based on historically aggregated data from both in-store and online/mobile activity
  • Combine insights from real-time activity with predicted recommendations for more relevant messages, in context
  • Deliver messages to the consumer instantaneously for greater conversion
  • Trigger enhanced engagement (e.g., sending a store representative for interactive engagement) that can dynamically adapt based on the situation at-hand.


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