Retailers are looking to do streaming processing to personalize offers in real time, enable dynamic pricing and more.
Regardless of market segment, retail is a hypercompetitive space. Organizations are faced with ever-changing customer demand, new competitors stealing market share, or large enterprises suddenly making their way into the industry and backing those companies 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 activities, flow processing unlocks the kind of real-time responsiveness that seems, to customers, as if they were receiving white glove service.
Marketers and other business users in retail organizations are asking their IT teams to help them leverage flow processing to:
- Customize offers in real time: Personalization went far beyond the recommended products displayed in the customers cart just before checkout. Resellers that can provide even the most complex of upsell/cross-sell products, such as a specialized bank account at the point of customer interaction, can much more effectively build lasting relationships with a much higher lifetime value.
- Offer customer support on every channel: An individual customer could chat with a customer service bot on Monday, post a frustrated thread on Twitter on Tuesday, and by Friday call every customer service number asking to speak to management. Resellers perceived as customer-centric are those who can leverage real-time flow processing pipelines to aggregate and correlate data from individual touchpoints to deliver personalized, automated, and contextualized solutions that feel customized, as if they were provided by a dedicated customer service representative.
- Enable dynamic pricing: Retailers of all sizes, not of the Amazon and Walmart scale, can carve out a competitive edge by changing the prices of their products multiple times in a single day. By aggregating competitor data, real-time inventory status, current customer demand, and other market trends like parts 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
Go beyond batch processing for streaming
Many retailers already have a solid technical foundation, even if their employees haven’t yet experienced stream processing and how it enables real-time responsiveness. Many of them use an enterprise service bus (ESB) and service oriented architecture (SOA) to connect data with multiple back-office and end-user applications, all proven for reliability and business value of easy integration with customer asset management (CRM) platforms and beyond. Technical leadership at retailers is hesitant to rip off Anyone infrastructure and applications they created just to experiment with stream processing.
But they also admit that their current batch analysis methods, which create lag times of days to respond to a changing market trend or significant customer service issue, are no longer sufficient against rapidly changing competition and trends. global that can turn entire markets upside down overnight.
To create real-time pipelines that go far beyond the availability of real-time data, knowledgeable retailers are looking for stream processing platforms that are:
Ready for event-driven architectures: If a reseller is already running on an ESB or other publish/subscribe messaging bus, enterprise architects can integrate data pipelines into a stream processing platform to immediately create an event-driven architecture. They can respond quickly to events and correlate and aggregate individual events with others to create holistic data entities to be pushed back to the bus, which then provides marketing teams and other business units with more analytical opportunities down the line.
Offer data enrichment capabilities: Not all customer interactions/events contain all the data about who the customer is, such as full contact details or a list of previous purchases. To provide real-time responsiveness informed with all of 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 activate the most precious response instead of the first one created by the stream processing pipeline.
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