Clustering and segmentation may make boosting sales and aligning products with customer preferences and seasonality a walk in the park according to Forbes.
Naturally, there are different types of clustering in response to a range of challenges businesses face and each approach offers unique insights into customer behavior and market trends.
Let's look at what innovative tech tools can do for us to simplify the implementation of product clustering.
Key Takeaways
Clustering products improves layout, promos, and inventory planning.
Groups by price, demand, or affinity.
Helps set modular planograms.
Supports bundling & cross-sales.
Reduces overstock of low clusters.
Refined by frequent analysis.
What is product clustering?
A product cluster is created by grouping products based on shared characteristics, creating flexible categories for retailers. Products can be clustered by various attributes, including type, shape, purpose, material, features, price, seasonality, brand, function, and other features.
This approach enables retailers to analyze and manage their assortment with versatility, supporting precise adjustments to inventory, pricing, and overall performance.
Types of product clustering
Product clustering can be broadly categorized into two models: static and dynamic.
Static clustering
This product clustering model involves creating fixed product groups based on predefined attributes such as size, category, or sales history. Static clusters are established once and do not change frequently. These are ideal for businesses with stable, predictable product lines.
For example, in a supermarket, static clusters might be created for non-perishable items, such as canned goods or household supplies. Similar products are grouped by category (for example, canned vegetables and cleaning supplies) and their historical sales data. The demand for these items tends to be consistent throughout the year, so there is little need for frequent adjustments to the clusters.
The primary advantage of static clustering is its simplicity. However, there's also a limitation in that static product clusters may not account for sudden market shifts, changes in consumer behavior, or emerging trends. Retailers relying solely on static clustering models might miss opportunities by reacting too slowly to these changes, potentially losing out to more agile competitors.
Dynamic clustering
In contrast, the dynamic product cluster model is much more flexible and adaptive. It utilizes real-time category data to create product groups based on current performance metrics, customer behavior, market conditions, and other relevant factors. Dynamic clusters constantly evolve, allowing retailers to react swiftly to changes like shifts in demand, emerging fashion trends, or even economic conditions.
For example, retailers in the grocery industry might use dynamic clustering to adjust product categories daily based on factors like freshness or sudden changes in consumer purchasing behavior (e.g., during holidays or special events).
The ability to optimize product availability and pricing in real time is a major benefit of dynamic clustering
If a retailer notices a spike in demand for organic products in a particular region, dynamic clustering can adjust stock levels in nearby stores to meet this demand.
Leading retailers are now combining both approaches to create a hybrid model that takes advantage of static clusters' stability while maintaining dynamic ones' flexibility for effective product categorization.
Benefits and world best practices in product clustering
A good product clustering strategy offers numerous benefits for retailers, particularly in assortment planning, and managing inventory. Let's look at how global brands use these techniques effectively:
#1. Optimizing inventory levels
Product clustering helps retailers manage their stock efficiently.
Amazon is a prime example of a company using dynamic product clustering to tailor its recommendations and inventory management. During major shopping events like Black Friday, Amazon clusters high-demand products to optimize availability and manage prices dynamically. The dynamic clustering system allows Amazon to maintain efficiency in its inventory and ensure that customers are shown the most relevant products based on real-time demand and purchasing patterns.
#2. Adapting assortment planning
Static clusters manage long-term inventory, such as perishable goods, while dynamic clusters adjust based on regional trends and seasonality to ensure products are in line with local demand.
Walmart uses a combination of static and dynamic clustering in its operations. In its grocery segment, for example, static clusters may group products based on long-term sales patterns (such as perishables vs. non-perishables), but dynamic clusters adjust assortment planning based on regional buying trends or seasonality. Walmart also uses Big Data to predict demand for specific items, ensuring that stock is dynamically replenished to meet customer needs during peak periods, like holiday shopping.
#3. Enhancing customer personalization
Nike uses dynamic clustering to enhance its direct-to-consumer (DTC) sales approach. By analyzing customer behavior through its website and mobile apps, Nike dynamically adjusts product assortments to highlight trending items among specific user segments. If a particular type of running shoe gains popularity in a certain demographic, Nike adjusts its product clusters to promote similar items.
These examples highlight how global brands effectively use product clustering to drive business success. Whether through static or dynamic models, these strategies allow companies to optimize assortment planning and inventory, streamline marketing efforts, and improve overall customer satisfaction.
Challenges in implementing product clustering
Despite its benefits, implementing product clustering can be fraught with challenges.
Data management
One of the most significant hurdles is managing large volumes of data. Retailers need accurate and up-to-date information to ensure their product clusters are relevant and effective. Without proper data management, clusters may become outdated, leading to poor inventory and pricing decisions.
Model selection and adaptability
Another challenge lies in developing and applying the right product clustering model. Retailers must account for various relevant factors such as market conditions, customer demand, and competitive landscape. These factors can change frequently, making it difficult to maintain accurate and useful clusters without advanced tools and AI-driven intelligent systems.
Consistency across locations and categories
Finally, implementing clustering across different locations and categories can lead to inconsistencies in how products are grouped. To overcome that, businesses are turning to artificial intelligence and machine learning to enhance their clustering models, ensuring that they remain flexible and adaptive to market changes.
LEAFIO AI: proven tools for clustering strategies
Luckily there is a solution that addresses these challenges. LEAFIO Assortment Performance has been designed to cluster products optimally and streamline assortment management.
Customizable SKU groups
With the Saved SKU Groups feature, retailers can create custom product clusters for marketing campaigns or performance tracking. This feature allows teams to collaborate effectively by sharing product group files to ensure everyone is on the same page.
Product matrix functionality
The Matrix functionality is another essential tool for category managers. This feature allows retailers to manage SKUs and their importance scores within clusters, making analyzing how different products perform easy. It also enables A/B testing within clusters, helping companies understand how different SKUs perform in similar circumstances. In addition, this functionality supports the rapid implementation of exclusive products in specific stores, ensuring flexibility and responsiveness in a fast-paced retail environment.
LEAFIO Assortment planning software simplifies the complex process of clustering and provides retailers with the tools they need to manage product categories and increase profitability effectively.
To sum up
When combined with innovative tools based on artificial intelligence like LEAFIO AI, product clustering becomes a key method that allows retailers to manage inventory, price, and customer engagement more effectively. At the same time, techniques such as morphological analysis and entity matching help refine product titles and improve the accuracy of clusters. Clustering has simply become such a common practice that it should be a part of the retail routine for any company hoping to optimize its operations.
Curious to see how it could work for your business? Request a demo of LEAFIO AI Assortment Planning Software today!
Have a question?
Have inquiries about retail automation or optimization? Talk to our expert for solutions!
Jack Larson
Retail Optimization Expert
