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How The OODA Loop Helps Explain Algorithmic Merchandising

by Robert Hetu  |  March 9, 2016  |  1 Comment

Leveraging algorithms is the only way that merchandising can meet the customer centricity challenges of digital business and the digitalization of retail. Retail merchandisers fight a daily battle to garner and retain customers and to meet rigorous sales, margin and inventory targets. In an oddly similar example, a military pilot, when confronted with a battle situation, cannot optimize just one decision. If a fighter pilot focused only on optimizing fuel, ammunition or speed, the results could be catastrophic. The pilot’s goal is the successful completion of the mission, which requires constant observation, decision and action. Algorithmic merchandising leverages big data to make large and small merchandising decisions with greater accuracy and precision.

The OODA loop was originated by U.S. Air Force Colonel John Boyd, who developed it to define a way to understand how fighter pilots, often in inferior aircraft, consistently won air combat engagements against enemy pilots. What Boyd observed was that winning pilots acted more quickly than their less-successful opponents by cycling rapidly through four states:

  • Observe — See the situation and adversary.
  • Orient — Size up the vulnerabilities and opportunities.
  • Decide — Determine which combat maneuver to take.
  • Act — Execute the maneuver.

The OODA loop is similar to the more familiar approach known as Plan-Do-Check-Act; however, the emphasis on observation and orientation is a notable enhancement. With a proliferation of big data, the complexity of multichannel and the speed of change, the loop must spin much faster than the typical merchandising cycle. Algorithmic merchandising represents a transformation enabling the use of big data to drive even simple decisions. As seen in Figure 2, algorithmic merchandising will be comprised by similar loop that will drive automated and manual decision making.

Merchandising OODA

The Merchandising OODA Loop

Gartner clients can read more about algorithmic business in this special report Explore Algorithmic Business to Drive Differentiation and Algorithmic Retailing: Merchandising Leads the Way.

Additional Resources

Predicts 2019: Data and Analytics Strategy

Data and analytics are the key accelerants of digitalization, transformation and “ContinuousNext” efforts. As a result, data and analytics leaders will be counted upon to affect corporate strategy and value, change management, business ethics, and execution performance.

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Category: customer-analytics  data-and-analytics-strategies  retail-trends  

Tags: algorithms  analytics  customer-analytics  customer-centricity  digital  ecommerce  infocentricity  multichannel  omni-channel  personalization  retail  smart-machines  

Robert Hetu
VP, Analyst Retail
7 years at Gartner
29 years IT Industry

Bob Hetu is a Research Director with the Gartner Retail Industry Services team. His responsibilities involve tracking the technology markets and trends impacting the broad-based retail merchandising and planning areas. Mr. Hetu is an expert in the areas of brand, vendor and assortment management, merchandise planning, allocation, and replenishment. Read Full Bio

Thoughts on How The OODA Loop Helps Explain Algorithmic Merchandising

  1. Very interesting article, congratulations. Often decisions are made without a well drawn strategy and if we apply a method we have more chance of success.

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