Your store may be selling the best quality products at rates that strike the perfect balance between affordability and profitability. Your store premises may boast of the most exquisitely designed interiors and myriad types of merchandising displays. But how do you move further from here and understand whether your strategy is successful?

Without a concrete evaluation of the current scenario, sales merchandisers may not gauge the way ahead for their businesses. A data-driven approach to merchandise helps retailers assess their current standing and pick from types of merchandising strategies commonly leveraged in the global markets. Data provides retailers with concrete evidence on the performance of their businesses and helps them determine the further course of action. Different types of merchandisers across industries and business sizes leverage a data-driven analytical strategy to reap the benefits of increased efficiency and accuracy. Let’s take a look at the three broad types of merchandise data that can help retailers analyze their sales and requirements:

Activity Data

As the name suggests, activity data refers to information on the actions being performed in the store, including the frequency at which customers visit the store, time spent by each customer in the store for each visit, the section of the store where customers spend the maximum time duration etc.

Such metrics help retailers understand consumer psychology and preferences and devise in-store merchandising and promotional strategies accordingly.

Monitoring and analyzing consumer activities in the store premises also enables retailers to determine the types of merchandise products to incorporate and arrange the product displays in the retail store.

It also helps merchandisers incorporate promotional merchandise based on the type of products with which their target groups resonate the most.

Observational Data

Observational data provides retailers with information on the commodities and supplies available in the store.
Such information includes product stalk and facing, labelling, accessibility, active promotions etc. It helps retailers analyze their store requirements efficiently and accurately and devise merchandise solutions based on their observations.

Based on such data, retailers may design intricate planograms representing the store, the products it entails, and the locations in which these are stacked. Observational data also helps retailers develop sales display and merchandising solutions based on the customer requirements vis-à-vis the product availability in the stores.

A careful analysis of one’s stalks and shelves can also help retailers evaluate the product displays in their retail stores and place them at accessible locations for the customers.

Sales Data

Sales data provides retailers with information on how much each SKU the store has sold within a given timeframe.
Sales data helps merchandisers understand critical aspects of sales, including the percentage of deals to be closed, the number of leads in the vendor’s pipeline, the number of successful conversions etc.

Such information plays a vital role in managing merchandise, studying market requirements, distributing promotional merchandise, coming up with creative ideas for product displays in the retail store, etc.

Conclusion

Today, it is almost impossible to envision the retail sector in general and the on-shelf merchandising process without data analytics. Data science algorithms help merchandisers acquire, maintain and retrieve integral chunks of information instrumental in studying consumer behaviour and preferences and the resources and stalks that the stores possess to cater to these requirements. Premier merchandise and field execution service providers help branded merchandise vendors leverage cutting-edge data analytics solutions that make the data management process efficient, accurate and cost-effective.