Several solutions have emerged over the years to get us closer to a viable dataset that will propel category management into the future.
Over the years, category management solution challenges have been the same: reviewing lots of data, analyzing, and tailoring selection to the store and executing at the shelf level. We started considering more data as we progressed, and the store count grew considerably with expansion and acquisition. Suddenly, we added the word “Big” in front of “Data,” and the problem started getting more attention and became overwhelming. We started looking for answers in the wrong places. It doesn’t have to be this way.
The category management processes have proved effective and resilient over several decades. Are there areas where it didn’t reach its potential? Yes, but the cause is different from what you would expect.
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Most systems in retail are transactionally based. Things get complicated when we start introducing spatial data into the mix. What we envision in the digital world must be rolled out to the physical. The challenge is making it execute flawlessly at every store while conforming to structural limitations.
As a longtime friend in the retail space always reminds me, “The closer you are to a perfect planogram by mathematical standards, the further you are from a perfect visual one.” Intelligent systems need to account for the artistic elements of a planogram.
Category reviews based on planogram data versus “real-o-gram” data will be as good as compliance with the plans. This is why computer vision scaling is essential, and adoption is critical, to more intelligent systems.
If we ask retailers whether they do total category reviews, execute to the shelf, perform full compliance and follow the process continuously, many say “yes” – but by my estimates, very few actually do. About 25% pretend to follow it, and the rest build a pretty picture. We are likely to see smaller retailers implement a strategy, use the tools they have and deliver better results.
Why the disparities? The problem is threefold.
We need the right tools built for scale and collaboration. We’re still relying on manual product selection and semiautomated optimization processes. Simplification kicks in as the problem becomes more complex, deadlines approach, fewer hands are available to do the work, or outsourcing options become scarce. The results are pretty pictures, but do they deliver on shoppers’ needs?
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As Dr. Brian Harris, known as the “father of category management,” noted: “Legacy solutions limit the strategic opportunities of category management. They have fundamental limitations in enabling retailers to address deployment needs and fail to incorporate diverse data sources to understand consumer behavior and growth drivers.”
The second part of the problem is the silos at the solution level. They need data, software and platforms to come together and deliver. Anything less than that is short-lived until we encounter a new trend to distract us.
The third part is the discipline to follow the process. As you deviate from the process, things start to fall apart and become challenging to execute and measure efficiently.
Accurate representation of shelf conditions supporting cash register sales has been elusive. It’s the critical component that makes all current solutions in the market half efficient and the key component that will make an artificial-intelligence/machine-learning solution viable.
Several solutions have emerged over the years to get us closer to a viable dataset that will propel category management into the future, making legacy tools obsolete and ushering in new, intelligent tools to deliver better outcomes at the shelf. From handheld devices and robots to fixed cameras, computer vision and sensors are getting closer to providing shelf insights in near real time at scale.
What to Do
Adopting legacy-solution thinking at this time is more busy work and will deliver disappointments at many levels. Retailers need to continue challenging solution providers to provide viable alternatives and not more of the same.
Future of Category Management
The category management processes are still relevant and essential. We prevented them from reaching their full potential by not evolving solutions to address the growing complexities of today’s retail operating conditions. The data analytics and the steps at the foundation of the process stayed true to form. Those that followed it early on saw significant returns. The challenges in scaling the process brought us to this current state. It’s the responsibility of solution providers to address that issue.
Several newcomers have started to show the possibilities. More intelligent tools, data and a genuinely collaborative platform can bring about the future that the new generation would expect to make the competition for space much more effective and deliver a tailored assortment to the store and the shelf. The future is in the making, and we’re experiencing the early lifecycle of the products that will propel us, which will be the foundation for the next decade. The bright light starts to emerge right after the darkest hour. Let’s get ready.