How AI Algorithms Analyze Store-Level Data to Optimize Assortments

How AI Algorithms Analyze Store-Level Data to Optimize Assortments

April 21, 2023 | By HIVERY

Harness AI to Optimize Your Assortments at the Store Level: Make a Big Difference in Your Retail Strategy

In the world of retail, it's crucial for both retailers and Consumer Packaged Goods (CPG) manufacturers to make informed decisions about what products to stock, how much, and where to stock them. This is where assortment optimization comes into play, and AI has emerged as a game-changer in this field.

By leveraging store-level shopper data, retailers and CPG manufacturers can gain visibility into shopper behavior and preferences. This allows them to make informed decisions and deliver locally relevant, effectively merchandised, and operationally efficient plans with retailer-supplier transparency and collaboration at scale. With AI algorithms as their co-pilot, they can enhance their decision-making, qualify any category strategy, and iterate faster.

Proper assortment optimization leads to higher sales, increased margins, and improved shopper satisfaction, making it critical to a retailer's success. AI has made this process easier, providing retailers with a powerful tool to help them optimize their assortments. So, let's explore the implementation of AI in retail assortment optimization and how store-level data is the key to superior assortment optimization plans.

AI for Assortment Optimization

AI can help retailers optimize their product assortments in retail by analyzing store-level sales data, product and store attributes, and historical planograms. By leveraging AI, retailers can develop a better understanding of their shoppers, which can lead to more effective product assortments.

One of the most significant benefits of using AI for assortment optimization is that it can help retailers predict which products will likely sell well by analyzing historical sales data, product and store attributes, and historical planograms; AI algorithms can identify patterns and trends that indicate which products will likely be popular. This information can help suppliers and retailers decide which products to stock and how much to order.

AI can also help retailers optimize their product assortments by identifying complementary products. For example, by looking at shopper data at the store level and determining what products should be in those specific stores. Optimizing locally relevant assortment plans, effectively merchandised, and operationally efficient in stores. By recommending complementary products, retailers can increase the likelihood of additional sales.

AI algorithms can also help retailers optimize their product assortments by analyzing market trends. By analyzing data at the store level, AI algorithms can identify trends in shopper preferences and behavior. This information can help retailers decide which products to stock and how much to stock.

Implementing AI for Assortment Optimization

To implement AI for assortment optimization, retailers must first gather and organize the data they need to train the AI algorithms.

Next, retailers need to design the right AI algorithms for their needs. There are many types of AI algorithms, each with strengths and weaknesses. Retailers must choose the right algorithms based on their data, goals, and budget.

Once the AI algorithms are chosen, they must be trained on the data into a "model". This involves feeding the algorithms the data and allowing them to analyze it to identify patterns and trends. The AI model needs to be trained on a large dataset to ensure that it can accurately predict future trends.

Finally, suppliers and retailers must discover insights from the AI model to run different assortment optimization strategies. This may involve adjusting, looking at assortment goals, merchandise rules, and constraints such as merchandise styles or store fixtures, and considering each product expiry, new product innovation, and changing their pricing strategies. The monitoring output is continuous to ensure you are achieving your goals.

Challenges of AI for Assortment Optimization

Of course, there are also challenges associated with the implementation of AI. One of the biggest challenges is the need for high-quality data. AI algorithms need large amounts of high-quality data to be effective. Retailers may struggle to collect and organize the data they need to train their AI algorithms.

Another challenge is the need for expertise in AI. Not all retailers have the in-house expertise to implement AI algorithms for assortment optimization. Retailers may need to hire outside consultants or work with technology partners to implement AI effectively.

Finally, there is the risk of overreliance on AI. AI is useful for many functions but is not a substitute for human judgment. Retailers and CPG suppliers need to use AI as one tool in their assortment optimization strategies, not as a replacement for human decision-making. At HIVERY, we often say AI will help augment your decisions and act as an AI co-pilot. Further, AI can prepare the analysis so that this assortment is space-aware assortment optimization.

Simultaneously optimize space and assortment at store level regarding assortment plan and optimization. Further, it can provide assortments that.

The significance of store-level data and AI

We strongly believe that Data Has A Better Idea™ and that data can provide better insights, and we utilize shopper data from individual stores to demonstrate this idea. Through this, retailers and consumer packaged goods manufacturers can see how shoppers make purchasing decisions and use this information to create locally relevant and efficient plans that promote collaboration and transparency. With the assistance of artificial intelligence, decision-making is made easier by validating category strategies and speeding up the iteration process. Shoppers vote with their wallets in stores, and AI makes this visible to category management and shopper insights teams. This enables them to make decisions and deliver locally relevant, effectively merchandised, and operationally efficient assortment plans.


AI offers retailers a powerful tool for optimizing their product assortments. However, implementing AI for assortment optimization requires high-quality data, expertise in AI, and a balanced approach that combines AI insights with human judgment. As AI continues to evolve, retailers that leverage its power will be positioned for success in the highly competitive retail landscape.

Are you interested in AI-assisted assortment planning? HIVERY offers next-generation AI solutions to help the retail and CPG industries prepare assortment and category plans grounded by store-level insights and space-aware assortments.

Our solution acts as an AI co-pilot. By leveraging store-level data and AI-powered technology, you can quickly and efficiently qualify your strategy with space-aware assortment plans that are locally relevant, effectively merchandised, and operationally efficient in stores.

Related content you might be interested in:

Subscribe to HIVERY updates