Retailers are looking to stream processing to personalize offerings in real time, activate dynamic pricing, and more.
No matter the market segment, retail is a hypercompetitive space. Organizations deal with constantly changing customer demand, new upstart competition stealing away market share, or large enterprises suddenly acquiring their way into the industry and propping up those businesses with unique technical capabilities that only some can afford to replicate. Stream processing is already leveling the playing field.
By helping retailers tap into the wealth of information already coming from their websites, shopping/checkout platforms, and multi-channel marketing efforts, stream processing unlocks the kind of real-time responsiveness that feels, to customers, like they’re getting a white glove service.
Marketers and other business users in retail organizations are demanding their IT teams help them leverage stream processing to:
- Personalize offerings in real time: Personalization has gone far beyond “recommended products” showing up in a customer’s shopping cart just before checkout. Retailers capable of delivering even the most complex of upsell/cross-sell products, like a specialized bank account at the time of the customer interaction, can far more effectively create lasting relationships with far more lifetime value.
- Deliver customer support on every channel: A single customer might chat with a customer service bot on Monday, post a frustrated thread to Twitter on Tuesday, and by Friday, call every customer service number asking to speak to management. Retailers perceived to be customer-centric are those that can leverage real-time stream processing pipelines to aggregate and correlate data from individual touchpoints to provide customized, automated, and in-context solutions that feel personalized, as though delivered by a dedicated customer service representative.
- Activate dynamic pricing: Retailers of all sizes, not of Amazon and Walmart scale, can carve out a competitive advantage by changing the prices of their products multiple times in a single day. By aggregating competitor data, live stock status, current customer demand, and other market trends like part shortages, sophisticated omnichannel retailers can endlessly optimize for the best customer experience and profitability.
See also: With DIY and Conversation-as-a-Service, Chatbots Are Here to Stay
Moving beyond batch to stream processing
Many retailers already have strong technical foundations, even if their people haven’t yet experimented with stream processing and how it enables real-time responsiveness. Many of them use an enterprise service bus (ESB) and a service-oriented architecture (SOA) to link data with multiple end-user and back-office applications, all of which are proven for reliability and the business value of easily integrating with customer resource management (CRM) platforms and beyond. Technical leadership at retailers hesitate to rip out any infrastructure and applications they’ve built just to experiment with stream processing.
But they also admit that their current methods of batch analysis, which creates days-long lag times to respond to a changing market trend or a significant customer support issue, no longer suffices against fast-moving competition and global trends that can upturn entire markets overnight.
To build out real-time pipelines that go far beyond real-time data availability, informed retailers are looking for stream processing platforms that are:
Ready for event-driven architectures: If a retailer already runs on an ESB or other publish/subscribe messaging bus, enterprise architects can integrate data pipelines into a stream processing platform to immediately stand up an event-driven architecture. They can respond to events quickly and correlate and aggregate individual events with others to create holistic data entities to be republished on the bus, which then provides marketing teams and other business units with more analytical opportunities down the line.
Offer data enrichment capabilities: Not every customer interaction/event contains all data about who the customer is, such as their full contact details or a list of previous purchases. To provide real-time responsiveness that’s informed with all this context, stream processing platforms can leverage in-memory data stores, which integrate data from their existing data warehouses/lakes, to enrich events with more context, which then triggers the most valuable response rather than the first one created by the stream processing pipeline.
