(A detailed version of this article appeared as a multi-part blog series at part-1, part-2, part-3, part-4).

Happy recovery from the holidays! I am sure you were one of those numerous gleeful shoppers in the holiday season shopping via web, mobile, physical, and other channels, while also wishing that retailers could do more to enhance your shopping experience!

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 personalized) of the engagement simultaneously?

For example, consider the situation when a consumer is in vicinity of a physical store, and then is at store entrance, and then is inside the store shopping through aisles. 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 (a detailed discussion of tracking technologies is beyond the scope here; 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).

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. This article discusses the relevant technologies that not only have to solve individual disparate problems, but also have to come together and act in unison in order to translate this complex business case into a reality.

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 (how this is achieved via the “event processing network – EPN” and data ingestion layer, is discussed below);
2. The data science has to deliver individual consumer’s predicted (personalized) 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;
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.

Dynamic Tracking and Streaming Analytics: 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: Z (in vicinity outside the store), Z (in the entrance/checkout area), Z (clothing and apparel aisles), Z (electronics aisles), and Z (food and grocery aisles). See Figure.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. 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. 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.

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.

Predictive Analytics for recommended promotions/products: Some of the popular approaches for product/item recommendations have been based on collaborative filtering techniques. The fundamental assumption in these techniques, as it is suitably referred to as a “collaborative” technique, is that an individual consumer tends to view/like/purchase the same items that other consumers with similar patterns of views/likes/purchases have also done. Various algorithms/models in the realm of collaborative filtering techniques vary in their level of efficiency in extracting the “product/item similarity” between two consumers. For example, the Matrix Factorization method, particularly the Alternate Least Squares (ALS) method, has been a preferred choice among collaborative filtering techniques. For more detailed discussion on this method, and how cross-channel information of individual consumers can be captured via this method, see here and here.

Regardless of the approach, the recommender system will produce for each individual user an ordered set of recommended items based on the items not yet “touched by the user,” but have been “touched” by other similar users. It is important to note that the collaborative filtering techniques work well for user-item affinity, where the items (consumer products, movies, etc.) have sufficient longevity to be “touched by” a considerable number of users. But, what if the items are promotions or coupons or other similar things with a short life-cycle to map relevant promotions to users? One approach is to leverage the triangulation of “user <=> products” and “promotions <=> products” and thus deriving the “user <=> promotions” affinity.

With the ordered set of predicted products/promotions available ready and the EPN tool deployed to monitor in real-time the activity of an individual consumer in the zone, how do we combine these two to deliver the right message at the right time via the right medium? We will discuss this in the following section.

Combining Streaming Analytics with Predictive Recommendations: 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) within the Operational Intelligence (OI) platform performs a few steps to achieve the lock-step (see Figure for functional architecture for the solution):

• 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 Z (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 “real-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.

SUMMARY: Overall, the article discussed how a mix of technologies perform individual tasks of a complex business use case and also act in lock-step in an OI platform 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.