Anticipate incremented ROI of about 3%, and its detailed statistical analysis, using our Customer-Centric optimized process of Store Clustering.

A promising practice in the retail domain, Store Clustering creates relevant store segments, that resemble each other in certain pre-decided customer behavioral aspects, based on the actual store performance.

Our clustering services ensure the following:

1. Limiting investment by maximizing your revenue.
2. Adding flexibility and reducing complexity to your pre-existing structure without affecting it, while continually improving your approach.

By Preparing the data needs, such as Store details, Local area, Location, and customer details, we Analyze those variables using our clustering services for the development of each cluster in each category.

NeenOpal offers a wide range of approaches based on several aspects such as:

Single assortment :

• This approach will work well with the companies having a concise, focused product offering or with few outlets.
• Each sales outlet ends up receiving the exact same selection of items.

Sales Volume & Store Capacity based :

• Sales outlets are clustered together based on combination of historic or forecasted sales and capacity measure.

Store type based :

• Sales outlets are clustered based on specific requests for salient merchandise depending on the local market.

Competition based :

• Sales outlets are clustered based on presence of relevant competitors present in the market area.

Demographics based :

• Sales outlets are clustered based on statistical data about characteristic of the shopping population, which might include ethnicity, income, average age, and educational level.

Product attribute based :

• Sales outlets are clustered based on sales history of meaningful product attributes.


After implementing store cluster services offered by NeenOpal, you can experience a gross margin improvement of 3%, while reducing cluster count, by improving operational efficiency and productivity.

We give optimal store cluster recommendations using our retail domain expertise with our scientific store clustering methods using:

• Presence of competition.
• Consumer price sensitivity.
• Store-area market data.
• Customer Demography.
• Retailer defined business rules.

The insights obtained from the analysis would provide deliverable services such as:

• Recommended store clusters for respective scenarios.
• Demographic and competitive profiles of recommended cluster groupings.
• Pricing consultation to price optimization configurations.