According to Research and Markets, the Assortment and Space Optimization market is expected to grow from US$2.06 billion in 2024 to US$4.92 billion by 2033. This growth is driven by technological advances in ML-based analytical technologies that analyze large amounts of data and enable informed decisions for assortment optimization.
Machine learning algorithms are changing the way companies manage retail operations and plan their product mix. Let's take a look at the ways ML helps retailers and the possible pitfalls of using it.
Key Takeaways
ML tools make assortment more precise and dynamic, driving sales.
Learns from local buying patterns.
Predicts slow vs fast movers.
Cuts dead stock risk.
Helps rationalize SKUs by store.
Adapts to trends in real time.
The Essence of Assortment Optimization with Machine Learning
The implementation of ML technology is not just a retail novelty but an opportunity to gain the necessary competitive advantage. Assortment optimization using ML transcends traditional analytics used to leverage advanced algorithms to sift through and make sense of vast datasets. This technology allows retailers to create the optimal product mix for each and every store while taking into account various factors like consumer preferences, local customer demographics, or shopping behavior.
Ways of Using ML for Better Assortment Planning
Machine learning solutions are used in many areas of retail as part of the assortment optimization process (see below).
SKU rationalization
As part of the SKU rationalization process, ML analyzes each SKU's performance and allows you to understand which SKUs in your portfolio are worth keeping in your assortment and which need to go. Using ML, retailers can identify correlations between products, and create more effective product assortments without low performing SKUs.
Сustomer segmentation
Customer segmentation models use ML to divide a retailer's customer base into distinct groups based on their buying behavior, customer preferences, and demographics. This segmentation allows retailers to identify patterns and tailor their assortment to the specific needs and desires of different customer segments, increasing customer satisfaction.
Enhanced future demand forecasting
Predictive analytics allows retailers to identify trends and forecast future customer demand with a high degree of accuracy based on historical sales data and also external factors such as economic indicators or geography.
Supply chain and inventory management
According to McKinsey, supply chain management driven by artificial intelligence can cut costs by 15%, reduce inventory by 35%, and improve efficiency by 65%. ML optimizes the supply chain at all levels, from inventory management to delivery route planning. By analyzing large amounts of data, algorithms determine optimal replenishment strategies, reduce inventory costs, optimize stock levels, and cut costs.
Optimized pricing strategies
Machine learning models help optimize pricing for individual products, especially for new product launches, by analyzing factors such as customer demand, seasonality, and competitor prices. This approach allows companies to adjust prices in a way that maximizes profits but remains affordable for customers.
4 Key ML Challenges in Product Assortment Optimization
There is no magic pill in retail. Working with ML also poses a number of challenges.
#1 Data Quality
The quality and “purity” of data is crucial. Data fragmentation, inconsistency in data collection, and gaps in data sets are the biggest challenges faced when training a model. It's important to learn how to properly clean the information and train the models to interpret the most likely causes of deviations.
#2 Balancing ML Insights with Expert Intuition
Human intuition plays a crucial role in recognizing patterns and contextual factors that machine learning may not notice. Combining ML predictions with the strategic insights of experienced decision-makers ensures assortment decisions are both data-informed and market-aligned.
#3 Situational influences
Unexpected factors can affect the accuracy of ML-based systems' calculations. For example, a football match broadcast or a change in weather conditions can significantly change the demand for certain products. Therefore, a software developer should train the retailer's staff on how to properly configure the system and efficiently use additional resources and coefficients by adding all possible factors to the model.
#4 Rapid obsolescence of data
The historical data that ML models rely on can quickly become obsolete. For example, during the COVID-19 pandemic, consumer behavior changed in a coordinated manner, rendering previous years' data irrelevant in an instant. Likewise, historical data for the quarantine period became irrelevant again once the pandemic was over.
Improve your product mix with LEAFIO AI Assortment Planning
LEAFIO AI Assortment Planning software is a modern ML-based solution that simplifies the creation of an assortment management strategy and allows you to manage categories and clusters on a daily basis. The software provides convenient tools for SKU optimization. Machine learning analyzes the sales performance of each product in the assortment matrix and provides accurate data to understand whether a particular product is suitable for the audience of a particular store.
The system generates detailed in-depth assortment optimization analytics. The reports implemented within the LEAFIO AI's solution include “Planned-Factual”, SKU sales dynamics, analysis of suppliers and customers, balances, new products, and lost sales. If a category or an SKU has room for improvement, the system will always show you what you need to see.
Conclusion
ML is widely used for better assortment planning. It provides retailers with in-depth insights into SKU optimization, segments customers, and helps to understand their local needs, manage supply chains and inventory, forecast demand, and optimize pricing.
However, to effectively use machine learning solutions, managers must ensure high-quality information input and add as many influencing factors as possible to adjust the model.
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Kristi Miller
Retail optimization expert
