There are lessons to be learned from the brick and mortar or pure-play digital retailers that have been successful in the Covid-19 chaos. As the pandemic’s stress test of e-commerce, in-store insights, supply chain visibility, and fulfillment capabilities have revealed shortcomings, and long-lasting consumer experiences— it has also allowed many companies to pivot to very successful strategies built on enterprise data and the digitization efforts that accompany it.

Since the bulk of the retail season is upon us, I wanted to reflect on the four basic pillars of retail that we see successful companies embody. These pillars are based upon personalized interactions, customer-centric merchandising, supply chain agility, and reimagining stores. This blog is a first in a series that will examine the pillars and the successful customer case studies that have resulted. As people are central to retail, we will start with insights founded on accelerating customer insight and relevance through personalized interactions. 

Personalized Interactions Driven by Data

Successful retailers are leveraging customer profiles that produce higher customer engagement results and reduced marketing costs by delivering targeted, relevant, contextual content and recommendations. Leaders are moving to “segments of one,” defined as tracking and understanding individual behaviors across all touchpoints using the data to customize offers, products, or services to the individual customer. To fully execute personalized interactions, retailers are accessing both structured and unstructured data from website click-streams, email and SMS opens and responses, in-store point of sale systems and past purchase behavior. This data-centric approach is delivering the following business capabilities:

  • Customer Identification—Develop processes to identify customers and visitors across channels (store, web, mobile, social, paid search, etc.)
  • Real-time Personalization—Improve relevance and conversion with real-time product, content, and offer personalization
  • Site Layout/Navigation—Incorporate customer insights into product layout, search capabilities, like items and real-time personalized recommendations and checkout options
  • Customer Insights—Develop a deep understanding of customers and their behavior to enable operational improvements and inform interactions
  • Integrated Customer Data—Identify customers and build a robust view of customers across channels and touchpoints
  • Personalized Interactions—Increase conversion and customer engagement with deeper personalization and targeting
  • Marketing Attribution & Spend Effectiveness—Tag interactions that drive desired behaviors while evaluating media spend to allocate dollars to most productive efforts

To enable these business capabilities an enterprise data platform is necessary to process streaming data at high volume and high scale, to manage and monitor diverse edge applications, and provide data scientists with tools to build, test, refine and deploy predictive machine learning models.

How a leading global drug store improved precision and timeliness

This pillar is providing lasting benefits to a leading global drug store chain specializing in filling prescriptions, health and wellness products, health information, and photo services seeking to improve their digital personalization capabilities with their customers. Considering that they fill 900M prescriptions, and process over 1.2B customer digital interactions annually, this was truly a ‘big data’ opportunity.

With a wealth of opportunity in digitally interacting with their customers, they set out to improve their precision of personalized targeted email offers and improve the timeliness (desired to move the email sent closer to the last interaction with the business). They were experiencing a lag of 3 to 5 days between the last interaction and attempting to deliver personalized offers due to sluggish analytic workload performance and high costs associated with their legacy data platform. In order to improve business capabilities and customer relevance, they turned to Cloudera to establish a modern data architecture capable of handling massive customer segmentation model workloads.

This new platform leveraged 365 days of customer purchase history, including in-store POS and digital data across all loyalty customers that produced category affinity scores, individual customer preferences and reduced the time of insight (and action) from 3 to 5 days to less than 3 hours.

The Cloudera Data Platform allowed integration of all data across the enterprise from a variety of data inputs, rapid analytic model iteration updates with built-in governance and security—producing value to customers with relevant and timely content, recommendations, and offers. By enabling the digital marketing team with rapid time to market, with a scalable, performant, and cost-effective data platform— these insights were also propagated across all lines of business. In addition, the modernization program (anchored by Cloudera) attracted new IT talent, refreshing and upskilling for future innovation and growth. 

Stay tuned to my next blog in the retail space that will take the data challenge indoors and discuss the benefits of leveraging big data in customer-centered merchandising. 

Additional retail content can be found at our retail resource kit

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