Target Uses Guest Intelligence To Drive Marketing Strategies

By analyzing customer-driven data from market basket analysis and sales by category Target Corporation is learning new ways to market and merchandise products within its 1,700+ big-box stores. The fifth largest retailer in the U.S. shared its customer-focused strategies in a standing-room-only Big Ideas session at the National Retail Federation convention in New York in January.

A primary component in the quest for customer-focused merchandising is implementing optimization technology, noted Eric Bibelnieks, group manager for guest data and analytical services at Target Corp. Most recently Target has focused its efforts on Planogram Optimization and Space Optimization provided by SAS.


“We plan from the top down,” said Bibelnieks, “using both macro and micro views of the store” to enable space management by sales patterns, categories and individual products. Target also examines “adjacencies,” he noted, those categories that are merchandised near each other within the store. “By looking at sales patterns,” Bibelnieks said, “we can create cluster-specific planograms.”

Optimization Constraints
During his presentation Bibelnieks shed light on some of the constraints associated with optimization:

  • Presentation. One of Target’s goals is to hone in on minimum and maximum product facings using a block layout
  • Assortment. At Target, the most effective assortments feature complementary items merchandised near each other.
  • Inventory control. The primary constraint in managing inventory throughout more than 1,700 stores is the expense associated with supply chain and store operations.
  • Fixture constraints. The physical limitations of store fixtures come into play as issues such as product weight must be considered.

Challenges and Learnings
Bibelnieks shared nine of his top challenges associated with marketing and merchandising based on guest intelligence. When planning optimization strategies, he suggests focusing on the following:

  1. Analytics. Respect the data and appreciate the resulting analytics.
  2. Data Quality & Standards. For quality control, implement standards associated with how data is handled.
  3. Financial Resources. Create a budget and follow it.
  4. Clearly Defined Roles. Each member of the team should understand his role in order to effectively communicate and deliver on goals.
  5. Training. Be sure team members, from the top down, understand how to use the optimization applications.
  6. Hire Analytical, Reporting and Data Analysis Talent. Build a team that knows what to do with the data once it’s collected.
  7. Prototype, Learn then Develop. You will save valuable time and money if you first prototype an implementation, learn from the prototype, then develop the final product.
  8. Test, Learn then Scale. Take the time to test the implementation on a small scale, learn from any mistakes then scale up to a full chain rollout.
  9. Balance the Art and Science of Retail. Technology helps today’s retailers create more effective, efficient and customer-centric stores but don’t ignore the art of using experience and instinct to enhance the process.

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