In the intricate world of retail, success often hinges on the art of clustering. It's a strategic approach that involves grouping similar stores to help with efficiency around planning and execution while enhancing customer experiences. Store clustering helps simplify operations. For example, if you have 500 stores, preparing those individuals' store planograms and executing them in 500 stores would be a challenging and daunting feat both operationally and impact the shopper experience.

Clusters help with this - draw fewer planograms, less shelf restocking, re-shuffling, less labor) however, it also means less sales and volume as you essentially take an "averages-of-averages" approach. But as we cover in this blog post, you can find stores that best go store-specific, and the rest go cluster.

HIVERY Curate offers valuable insights for CPG and retailers when they approach clustering by identifying where to go store-specific and where to use clusters.

Rapid cluster analysis

In the chart below, we used HIVERY Curate to re-optimize their current clusters; our client identified new cluster strategies that provide higher revenue with fewer planograms to draw. By conducting store-level analysis and re-optimization, we developed new cluster strategies. We found that we can increase revenue by 4.5% with a minimal increase in the number of planograms required while also improving DOS performance by 20%. We also showed that if they opted for a store-specific approach, it yielded the highest value with an 8.2% revenue change.

We optimized their existing clusters by leveraging store-level data.  This analysis took minutes to run and deliver new precision insight. This analysis did not take months, nor did it rely on many people with special skills in business intelligence (BI), analytics BI, or the data science team to generate.   

The critical point was that our client could double revenue and reduce planogram drawing while keeping DOS stable for just eight more clusters.

Keep reading for more insight like this.

Cracking the Code of Clustering

Clustering stores based on similarities is a concept that has been introduced previously in retail. It's a tried-and-true method for tailoring assortments to meet the specific needs of different customer groups. However, the challenge lies in doing it effectively, especially in an era of hyper-localization and ever-changing consumer preferences.

Cluster Precision with HIVERY Curate - Here's how:

HIVERY Curate leverages cutting-edge AI and machine learning to take clustering to the next level. Here's how it plays a pivotal role in retail strategy:

1. Hyper-Local Insights - One of the critical strengths of HIVERY Curate is its ability to look at store-level data and gain hyper-local, store-specific insights. It goes beyond broad strokes and dives deep into the nuances of individual stores. This level of granularity allows retailers to tailor assortments with unprecedented precision.
2. Real-World Constraints - Retailers often face real-world constraints, from DC (Distribution Centre) contraints, store-level merchandising styles, limited shelf space, and fixture constraints. HIVERY Curate doesn't just offer theoretical insights; it considers these constraints. It helps retailers make decisions that are not only data-driven but also practical and actionable.
3. Simulating Strategies - The power of HIVERY Curate lies in its capacity to simulate different clustering strategies. Retailers can ask questions like, "How can I maximize the productivity of existing space?" or "What is the impact of demand transfer across the portfolio?" and receive qualified predictions. It's like having a crystal ball for retail strategy.
4. Cluster Optimization - HIVERY Curate aids in optimizing clusters by identifying over-assorted sub-categories and suggesting optimal clustering strategies. It's about finding the perfect balance between offering variety and avoiding excessive inventory.
5. Personalized Shopper Experiences - Personalization is key in today's retail landscape. HIVERY Curate enables retailers to provide locally relevant, effectively merchandised assortments that resonate with shoppers. It's the secret sauce for enhancing customer experiences.
6. Dynamic Approach: HIVERY's AI model can refine and update your clustering strategy as new data flows in. This ensures you always have access to the latest store-level insights. This dynamic approach lets you stay ahead of the curve, making informed decisions more often and easily.
7. Maximizing ROI - Ultimately, it's all about the bottom line and ensuring clusters are operationally efficient while giving the shoppers at the local level what they want. HIVERY Curate helps retailers make decisions that maximize return on investment (ROI). Whether rationalizing SKUs or optimizing planograms, the tool ensures that every move contributes to profitability.

Unlocking the Potential of Clustering

The art of clustering is no longer a static, one-size-fits-all approach. With HIVERY Curate, retailers can unlock the full clustering potential, turning it into a dynamic and highly profitable strategy. It's about understanding each store's unique identity, customers, and market dynamics.

HIVERY Curate is not just a tool; it's a partner in the art of clustering. It empowers retailers to navigate the complexities of clustering with confidence, precision, and profitability. As the retail landscape continues to evolve, mastering the art of clustering with HIVERY Curate is a strategic move that sets retailers apart in a crowded market.

Related resources you might be interested in:

• Navigating Common Pitfalls in Product Line Reviews (PLR): A Guide to Success with HIVERY Curate
• Discovering 'Sleeper Stores': How AI is Redefining Retail Insights and Shaping Buying Behaviors
• Interactive Assortment Planning: Using AI-Driven Visual Insights to Make Difficult Decisions
• Can you find individual stores that are worth going to store-specific assortments? AI can