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Using Algorithmic Retailing to Drive Competitive Advantage

by Robert Hetu  |  September 21, 2016  |  3 Comments

Gartner 2016

Gartner 2016

New Gartner research explores how retailers gain competitive advantage through the application of algorithms that reduce costs and grow top-line revenue. CIOs can use this research to identify use cases that will improve business performance in the unified commerce retail marketplace.

Gartner describes algorithmic business as the “enablement of business value through the action of algorithms on data” and regards algorithms themselves as a way to encapsulate and produce intellectual property, knowledge and insight in a reusable form. Algorithms are a set of rules for solving a problem in a finite number of steps, as for finding the greatest common divisor. New technologies create opportunities to advance algorithms, incorporating many more data inputs and steps and even decision-making capability.

Algorithmic retailing is the application of big data through advanced analytics across an increasingly complex and detailed retail structure to deliver an efficient and flexible, yet unified, customer experience. Complex algorithms were once the domain of scientists and academics, but with the advent of digital technologies, the IoT and smart machines, retailers will be able to use algorithms to improve business results. Smart machines are an emerging “super class” of technologies that can perform a wide variety of work and add great value to business processes. Algorithmic retailing supports the evolution of unified retail through smart data discovery that paves the way toward analytically driving every decision and leveraging smart machines for productivity and detailed understanding. Algorithms are required for execution of opportunities identified from big data.

Some Key Findings:

  • Big data abounds, but its true impact on business results has thus far been less than was anticipated, reinforcing the need for an algorithmic approach.
  • A foundational framework is required to maximize algorithms.
  • Algorithmic use cases are emerging, yielding results for aggressive retailers.
  • While many use cases are focused on a single process area, the benefits of algorithms will cross silos.

Use cases are explored, featuring a large selection of vendors and retail scenarios to help crystallize the vast opportunities provided by the applications of algorithms to big data.

To read the entire research click here:

Using Algorithmic Retailing to Drive Competitive Advantage

Additional Resources

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

Tags: algorithms  analytics  bi  customer-analytics  infocentricity  omni-channel  retail  trends  

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 Using Algorithmic Retailing to Drive Competitive Advantage

  1. Tom Deutsch says:

    Hi Robert – good post (thanks) but I’d cooperatively suggest that it isn’t big data or an algorithmic approach, they are not exclusive of each other. In fact, big data can and should be used to enrich the data sets we use to drive and improve the algorithmic outputs.

  2. Kevin Sterneckert says:

    Nicely written Bob! I agree with your foundational positions. I would suggest a refinement….while algorithms are a key, the highest level of maturity is self-learning or as many are marketing – machine learning algorithms. A key challenge of algorithmic retailing is which algorithm should be used – this is best achieved when combined with an engine that can determine the best algorithmic form today and for the future.

    Today’s all-channel hyper-competitve transparent always on retail environment is more complex than any single decision maker can possibly manage without predictive and prescriptive recommendations delivered through advanced machine learning systems, period!

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