What is assortment optimization?
Assortment optimization is the process of selecting the right mix of products to stock on your shelves. It requires the ability to understand the retailer’s processes and objectives as well as what data is available in order to marry a compelling, data-driven “storytelling" assortment strategy and planograms.
However, aligning assortment merchandising strategies with consumer purchase behavior and responding to change in a timely manner has been the holy grain of the category management industry for many years. At the same time, traditional merchandising solutions have remained relatively unchanged since the 1980s, putting more pressure on the existing solutions and human decision-making process. These current "First Generation" (FirstGen) solutions and processes rely on outdated industry conventions and, human constructs or assumptions of "can" and "can not" be done. They also can not handle the ever-growing datasets and merchandising rules. This means the ability to translate outside category trends and insights into tangible, value add business action is extremely difficult. This, in turn, makes assortment trend analysis, category assessment, assortment optimization, planogram development, and optimizing the shopper's experience at retail stores an impossible task. It's why most retailers and CPGs (Consumer Packaged Goods) focus on the averages of averages approach, making assortment plans at the cluster level.
Next Generations (NextGen) merchandising solutions however aim to make category management more effective by leveraging the wealth of datasets that are available today with sophisticated machine learning algorithms to drive merchandising decisions to a level of precision not possible with current FirstGen solutions. They essentially combine the tasks of assortment analysis, category assessment, assortment optimization and planogram development into one so category managers can focus on what humans are good at; being more strategic, rapid and transparent in their assortment decisions with retailers.
These NextGen solutions are essentially augmenting assortment strategies and market actions with transparency. We are moving from an era of merchandising solutions that were good at “planning” and “automation” to one now focused on “augmentation” of human decision making. Providing a new level of competitive advantage. This is where we, as an industry, are heading, and where HIVERY and HIVERY Curate are focused on. We are creating this new "solution space" for the next generation of category management professionals; essentially the ability to maximize assortment profitability at the push of a button!
Embracing these NextGen merchandising solutions can help retailers and CPGs companies move from fixed seasonal merchandising plans to more fluid planning that responds to consumer interests and needs. Not all consumer trends are predictable, and modern merchandising augmentation systems empower retailers to respond to short-term surges or drops in demand for certain products. With these tools, retailers are more in charge of category management than ever before.
What are the benefits of getting assortment optimization right?
Assortment optimization is an ongoing and time-consuming process that can seem impossible to achieve with conventional methods given the volume of data, rules and constraints. But, if you get it right, the rewards are there for the taking. You’ll be able to identify and swap lower profit for higher profit products without a decrease in volume. You’ll be able to group partner products together, increasing the probability of additional sales and that’s just the beginning. Overall you’ll increase sales, reduce costs and significantly add to the bottom line.
How do you do that?
To perfect assortment optimization, it’s essential to analyze historical data along with an array of complex operational rules and business constraints. You need to be sure that a product swap you’re considering will not result in a reduction in sales volume or breach those important rules and constraints. The more data you analyze, the more accurate your analysis will be. For example, while a brand may currently be selling well, you might be able to spot a decline if you extend your horizon to include more historic data over the last few years. This could perhaps indicate a lack of investment from the brand itself or even a general decline in the category. The more data you have to review and the more complex your rules and constraints, the more likely you need to invest in software to help you navigate these decisions. The more you know about your sales trends, the better you can predict and get ahead of changes, before they get ahead of you.
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