Data Challenges for New Age Analytics in Retail

  • John Morrell
  • April 11, 2018

A recent article in WWD highlighted some of the key trends in analytics an industry expert expects to see more of in 2018. This includes:

  • The evolution from predictive to prescriptive analytics
  • Analytics to optimize store operations
  • Increased focus on omni-channel experience
  • Product assortment analytics
  • Using analytics to drive loyalty programs
  • Supply chain data sharing
  • Analytics to drive dynamic pricing

To many, this may not be very surprising. Datameer eBooks — Five Big Data Use Cases for Retail, a Winning Playbook for Measuring the In-Store Customer Journey, and Using Customer Behavior Analytics to Increase Revenue — discuss very similar ways to use these forms of analytics in a retail environment.

While many may understand the need for these new analytics, what is not generally known is the need to create an agile, comprehensive data strategy to make them successful. Each of these new analytics will require some or all of the following:

  • Consumption of more data to get a deeper view of the problem
  • The use of new, unknown data sources
  • The ability to shape the data in unique ways
  • The need to ask questions in rapid succession
  • Proper governance to effectively steward the data

Let’s look at a few of these analytic areas, and examine how a comprehensive data strategy can increase the value of these efforts.

Optimizing Store Operations

Beacon and sensor technology is the new approach being used by a number of retailers to track the interactions of consumers inside of stores. Much like tracking a customer experience on an e-commerce site, this technology can help retailers see locations customers visit inside of a store, gauge purchase intent, and optimize the merchandising to streamline purchases.

Organizing the data for these forms of analysis can be quite challenging. It requires blending multiple data sources beyond the raw sensor data including: merchandising data, product data, special offer data, and product sales transactions. Beyond this, the data needs to be shaped specifically to help analysts understand paths, graphs, and specific time-series aspects of the data.

The last aspect is to identify ways to gain value from the data. An obvious value is to identify the placement of best-selling products in the stores, see what merchandising techniques pay off, and to optimize store layout. Even greater value can come from integrating this data into the overall omni-channel experience view of customers (see next).

Omni-Channel Experience

The notion of a 360-degree view of a customer is not a new one. It has now evolved to measuring the omni-channel experience of a customer. Today’s omni-channel experience has expanded in four critical ways:

  • The explosion of digital data about customers and how the retailer is interacting with them from a marketing, sales, e-commerce, and service standpoint
  • The need to capitalize and act on different events in a customer’s experience and lifecycle within specific time windows
  • The use of behavioral analytics to see the reactions of customers to various interactions and the common attributes they share
  • More advanced modeling of customers with a customer lifetime value model

The Datameer eBook, Using Customer Behavior Analytics to Increase Revenue, digs deep into how companies can use behavior data and analytics to optimize customer experience and interactions.

A data strategy to measure and utilize omni-channel experience requires a comprehensive approach to:

  • Aggregate various sources of experience data including in-store experience, e-commerce experience, marketing campaigns, service interactions, and more.
  • Shape and organize the data in various ways based in the type of analysis at hand, including time-series analysis and identifying patterns to capitalize on the right lifecycle events
  • The ability to map experience data to outcomes, to determine to help drive next best actions based on where the customer is in the lifecycle
  • The ability to interactively explore huge volumes of data to continuously ask new questions and identify patterns and trends hidden deep in the data

Governance and security also play a critical role in the data strategy for omni-channel experience. The penalties for data breaches is growing with each new data privacy regulation necessitating strong security controls in the data strategy. And the advent of regulations such as GDPR is forcing retailers to have a deep understanding and control on how they interact with customers, requiring the ability to audit and track how data is being used.

Loyalty Programs

A large number of retailers have expanded into offering loyalty programs either directly via loyalty cards, or through privately offered credit cards that include loyalty programs. With so many programs available to consumers, retailers need to better use their loyalty programs as an event-driven touch point with customers and to differentiate offers.

Analytics for loyalty programs need to take similar tactics for a data strategy as omni-channel experience. Large volumes of data must be aggregated to determine how customers shop, where they shop, what they buy and more. This helps provide the baseline of data on customer behavior.

Beyond this, loyalty programs need to constantly explore how customers use the loyalty program. This requires combining even more data about various loyalty program offers, the marketing campaigns that deliver these offers, and how customers respond. The end result should be an agile loyalty program that can optimize offers and enable a personalized approach to interacting with the customer.

Dynamic pricing

As many retailers know, pricing is incredibly fluid, influenced by a number of factors from suppliers to markets to the economy.

Creating a program for dynamic pricing requires a data strategy that can support the use of:

  • External data sources on pricing, including competitor pricing, from trusted third-party sources
  • Supplier data with regard to product costs, shipping costs, and the timing of product delivery
  • Customer and transaction data to determine price elasticity by various demographics and attributes

Once aggregated, data needs to be effectively organized to help determine different elasticity models within various micro-channels – online vs. in-store, different geographic regions, different seasons or stages of the product lifecycle. Shaping the data for various forms of time-series analysis and deep interactive exploration of various attributes that determine pricing are critical successful results.

Call to Action

Retailers face an extremely challenging environment, with threats from pure-play online competitors to new boutique shops. The common denominator with these competitors is how they use data to differentiate, whether that is optimizing online interactions and experience, or identifying new unique products and services.

The best approach for retailers is to steal from the playbook of their new competitors and use digital transformation and data to re-make their operations and execution model. Use Datameer eBooks, Five Big Data Use Cases for Retail, a Winning Playbook for Measuring the In-Store Customer Journey, and Using Customer Behavior Analytics to Increase Revenue to help create your own new winning playbook.

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