Friday, June 14

Store Clustering In today’s competitive and dynamic retail environment, retailers need to distinguish themselves to gain the cutting edge that is essential to capture the right customers and increase market share. Most retailers have one brand, one marketing strategy and one assortment plan. The complexity is that inventory, store arrangements and promotion plan in such retail shops are based on sales rate and not on consumers’ differing lifestyles and buying characteristics. Such a general strategy does not suit individual stores with consumers’ different buying patterns, as a result of which a single retail offering becomes a competitive drawback. Some of the decisions such as merchandising and assortment are made at store level. Nevertheless differentiating brands, products, and promotion campaigns for several stores is either expensive or difficult for many retailers. Hence, there arises a need to categorize stores with similar characteristics into clusters (buckets), so that stores with similar characteristics can be specifically targeted.

Store clustering is a popular analytic technique that serves the purpose of categorizing stores. It is a process of grouping stores which are “similar” in one single cluster and are “dissimilar” to the stores belonging to other clusters. It is employed to build appropriate store segments which are homogenous in certain behavioral aspects such as similar performance, shopper segments, compatible functioning characteristics, common store size / type and demographic characteristics so that similar audience can be targeted using the same marketing scheme.

Store clustering approach – how it works:

Organize stores into clusters by considering various factors for clustering. There are 2 types of store clusters: performance based and non-performance based.

1.Performance based cluster: Stores with similar sales performance are grouped together. For example, a small store with high sales will be merchandised in a different way from a small store with low sales

2.Non-performance based cluster: Stores are grouped according to

•Characteristics such as:
Store size
Store type
Store location types (mall, independent store, locality)

•Customer demographics such as:
Age group
Initially, for both performance and non-performance based clusters, review existing clusters to define any problems and issues. Form new clusters on the basis of this analysis.

To understand and implement store clustering retailers need to scrutinize each cluster on the following basis.

1) Profile the shoppers in the clusters from the data to obtain the following information
• Customer purchase behavior
• Spending pattern
• Lifestyle characteristics
• Products preferred
• Brands preferred
• Occasional shopping pattern (i.e. when does customer shop more, during festivals, holidays)

2) Identify heavy shoppers of a category and brand.
POS data on category sales within specific stores is the key information, however this information do not disclose certain shoppers in each cluster. There are few consumers who demonstrate the greatest likelihood to buy the products leading to unseen sales. These unseen sales opportunities within each cluster can be spotted by gathering household panel data. This data is used to draw a demographic and lifestyle profile of the heavy users of these products.

3) Identify the prospect for each product type and brand within the category in each cluster.
• Analyze how each product as well as brand is performing within the category
• How much each product type and brand is contributing towards the total revenue

4) i) Recognize the media characteristics of loyal customers. The way through which a customer is introduced to the product or means of advertisement are newspapers, magazines, ads on tv, radio, hoardings (banners), ads sent through mobile and flyers
ii) Formulate promotion strategy for loyal customers.
• Promote the product/brand through appropriate channel
• Target the right customers with right promotions (i.e. formulate promotion strategies to stimulate customer interest in product and hence produce profitable results)

Depending on the above analysis, modify medium of advertisement and promotion campaign according to heavy shopping behavior for particular customer within cluster.

5) Allocate shelf space as per opportunity (prospect)
• How much inventory to stock on shelf as well as store as buffer
• How much aisle space to allocate to each product & brand
• How much to reorder (replenish)

To bring all this information together, store clustering team comprises of store planners, analysts, merchandise planners and space allocators.

Advantages of store clustering – Impact on retailer’s business model:

1. Scheduling & planning: Useful for store planning, marketing, cross-promotions and merchandising

2. Assortment: Category and sub-category levels decide the best assortment

3. Allocation of space: Helps in macro & micro space allocation for category/product/brand

4. Inventory management: Optimize stock holding, improving availability, replenishment planning

5. Revenue management:
• Promotion tailored to cluster-specific requirements
• Increasing sales by identifying sales opportunities

6. Enormous product range:
• Provides customers with vast range of choice
• Provides the foundation to plan a diverse multi-location environment in an effective and timely manner

7. Assigning / reassigning stores to cluster: New stores can be easily assigned to a cluster thus helping them to establish and grow quickly. Older stores can be reassigned for aligning the store as per changed characteristics like sales patterns, market changes.

Techniques used for store clustering: Store clustering relies on statistical and non-statistical methods to group together observations (stores) that are alike across certain selected variables. Techniques like neural networks, K-means clustering and self-organizing maps are popular for store clustering.

Optimization and data mining techniques can be utilized for defining effective clusters.

SAS Intelligent Clustering for Retail solution helps retailers to increase sales, profit and customer contentment by providing the optimal set of store clusters for assortment, planning and category management.

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