Real-Time Cross-Channel Customer Engagement by Coalescing Streaming Analytics and Predictive Analytics

It is the holiday season again. The season with the highest number of consumers shopping via web, mobile, physical, and other channels. In this 4-part blog series, we will explore what more can retailers do to enrich the experience of their customers.

No doubt that retailers continuously strive for innovative solutions that can enrich customer engagement; particularly the engagement that is seamless across channels, engagement with advance knowledge (predictive) about consumer preferences, and engagement to deliver an offer to the customer at the right moment (real-time). Now, how about a solution that can achieve all the three dimensions (i.e., cross-channel, real-time, and predictive) of the engagement simultaneously? For example, consider the situation when a consumer is in the vicinity of a physical store, and then is at the store entrance, and then is inside the store shopping through aisles.

Through application of some recent advancements in tracking technologies, retailer can track the customer within a certain range of the store and analyze for behavioral patterns; added to that, tracking of the customers on the retailer’s web and mobile stores has been in vogue for a while now. Given this, how can retailers combine the insights from real-time analysis of dynamic tracking information with the predicted preferences (based on historical data about customers viewed/liked/added-to-cart/purchased) in order to recommend the right promotions/products in a very short time window before the customer walks out of the store? Or how can retailers influence the consumer in the vicinity to come into the store, through a personalized message, and thus improve the conversion in real-time?

For retailers to be able to engage the consumers at the “right moment” armed with “insights in real-time to the seconds and/or minutes” and supported with “accurately predicted” individual consumer preferences is no longer a pipe dream. Let us discuss in detail the relevant technologies that translate this complex business case into a reality.

For the solution to achieve the desired outcome in the above business scenario, various pieces of technologies not only have to solve individual disparate problems, but also have to come together and act in unison:

  1. The dynamic tracking data is streamed continuously into a streaming analytics engine, which continuously analyzes the location data to obtain the insights about the individual consumer activity in real-time (we will discuss in detail how this is achieved via the “event processing network – EPN” and data ingestion layer, in part 2 of this series);
  2. The data science has to deliver the individual consumer’s predicted purchase preferences as accurately as possible for greater conversion. While the Recommender Systems are not new, more significant is the fact that the data science, while predicting the preferences, have to take into account the cross-channel correlated data from web, mobile, store POS, and other channels (we will delve into details in part 3 of this series);
  3. Most importantly, the comprehensive solution has to ensure that these disparate pieces of technologies (i.e., streaming analytics and predictive analytics) are able to communicate and exchange relevant information, possibly via a Business Process Management System (BPMS), to deliver the right recommendation at the right time via the right channel (we will discuss the details in part 4 of this series).

Through this multi-part blog series, while we discuss in detail the relevant technological pieces for this business case, we intend to demonstrate how we at Vitria have brought together these disparate technological/functional capabilities in our Real-Time Operational Intelligence (OI) platform to deliver the greatest value to the enterprises.

Please stay tuned….

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