Real-Time Cross-Channel Customer Engagement (part 2) – the Streaming Analytics Engine

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) and supported with accurately predicted individual customer preferences. We also gave an overview of relevant technologies that not only have to solve individual disparate problems, but also have to come together and act in unison in order to address the complex business case.

In this second part, let us venture into details of the Streaming Analytics engine, which is leveraged for Behavioral Analytics on the dynamic tracking data to obtain the insights about the individual customer activity in real-time.

There have been various advancements in technologies for retailers to deploy to detect customers in the vicinity of a store location and then track their movements within the store. Some of these technologies include Wi-Fi or Bluetooth based sensors for geo-positioning, embedding/integrating RFID chips inside customer loyalty cards, and so on. Similarly, tracking and recording the activity of online customers across various Webpages has been in vogue via Clickstream technologies. The discussion of these technologies is beyond the scope here, since the focus is around the technologies that help in real-time monitoring of each individual customer to realize an instantaneous engagement, as well as in discovering the pattern of customer activity in a zone. We start with the assumption that regardless of the sensor technology deployed, data from such sources will be available for ingestion to generate a continuous streaming event data.

Now, in order to build a model for real-time monitoring of activity, one approach is to first create an Activity Flow Diagram for all of the possible scenarios in which the activity can be defined when a customer is in a particular zone. For example, say  a zone is divided into five sub-zones: Z0 (in vicinity outside the store), Z1 (in the entrance/checkout area), Z2 (clothing and apparel aisles), Z3 (electronics aisles), and Z4 (food and grocery aisles). See Figure 1.

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Figure 1: A possible zone layout for a retail store

One can construct the Activity Flow Diagram across these various states (Z0, Z1, Z2, Z3, Z4), by leveraging the Activity Tracker application within the Streaming Analytics engine; the model will enable the system to monitor the customer movement from zone to zone in real-time (see Figure 2). The Activity Tracker application can also be leveraged to monitor in real-time various KPIs, such as the duration spent by the customer in each zone (see Figure 3). The Duration KPI for each zone can be assigned some threshold (e.g., poor, fair, good) to trigger operational actions, such as texting promotional messages; for example, the smaller the duration consumer spends in Zone 0 and Zone 1 the better, and the larger the duration consumer spends in Zone 2 or Zone 3 or Zone 4 the better. The other KPIs can be the total number of customers in each zone in a given time window, the number of customers that move from Za to Zb as against Za to Zc, and so on.

For the Streaming Analytics engine to monitor the customer flow and provide the necessary insights, a source connector or a stream processing component can first ingest the data coming from various sources (e.g., sensors); the output of the ingestion can become the source stream to the Streaming Analytics engine. Using an event-driven architecture (EDA), the stream processing engine tackles large volumes of raw events in real-time to uncover valuable insights. It does this by correlating events from diverse data sources and by aggregating low-level events into business-level events so as to detect meaningful patterns and trends.

Figure 2: Customer activity model built in Activity Tracker component of Vitria OI platform

Figure 3: Activity Analytics – Duration KPI with defined thresholds for each zone

As the real-time monitoring of a customer in a zone is set in place, the second piece of the puzzle is the predictive analytics for recommendations/messages, personalized at each individual customer level to be delivered when the customer is in the zone. This we will discuss in the next part. Stay tuned.

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