Comment: Analysing the benefits of analytics

Retailers are increasingly using insight gained from analytics as they strive to improve business performance. In particular, analytics has become essential for the buying and merchandising team, which must try to deliver against the consumer's demand for the right product, in the right place, at the right time. Meeting this demand is no easy task when challenged by complex seasonal, channel and location-specific variations and tight financial targets. Merchandising analytics can help the business make better commercial decisions around how it plans, buys and sells its products.

Many retailers are rethinking their entire operating model to keep up with, and anticipate the demands of, their omnichannel customer. Whilst this has created an appetite to implement complex systems across the business, these undertakings need not be the only way to reap tangible benefits. Analytics can be used in a variety of ways that can help deliver results for the business now. It can provide valuable insight around pricing, stock allocation and assortment, replenishing products, and distributing store space for each product category.


To earn quick-wins from merchandise analytics, retailers should look to first determine how best to sell to their customers and provide a competitive price. The connected consumer of today benefits from higher levels of pre-transaction research and evaluation; for example, many browse on their tablet before a shopping trip or compare products on their smartphone whilst in store. This means retailers have to be accurate with their pricing strategy and execution. Getting the right entry price for products is one challenge, but it is equally important to understand when to offer promotions or sales and at what markdown. Reducing prices by too much or too little is all too common.

An external awareness of competitor pricing is also important to understand "what's hot" and "what's not" in the market. This allows retailers to maintain a flexible approach to pricing execution throughout a season. Analytics provides key statistics and the ability to test different scenarios, helping retailers to make informed decisions around pricing.

Stock allocation and product assortment

Buying the right item at the right location is also vital to maximise category sales and margin. A customer who can't find their specific product size can result in a lost sale for a retailer and gives the competition an opportunity to get it right instead. It is up to the category buyer and merchandiser to minimise these instances. A data driven approach to deciding the quantity and assortment of products for each outlet can ensure retailers provide the right mix of products for each specific store. For a global clothing retailer for example, the sizes required for Thailand's customers will be much different to the UK customer.

In addition, insight can be gained through analysing customer purchasing behaviour across different buying occasions. For example, how often a customer continues to buy a product across different seasons. The analysis can help to demonstrate which products have high loyalty and drive repeat purchase, and therefore, should be core to the range.

Replenishing products

Analytics can also help to determine what proportion of stock to allocate to stores versus what to retain in the warehouse for replenishment. Getting this balance right will vary considerably by product category. For example, you would need to use different logic when predicting how often to replenish a dress, compared to the logic required when forecasting the replenishment of a microwave. Therefore, retailers will need different strategies to address forecasting and inventory concerns. To handle a range of product groups, multiple sets of rules and logic need to be defined depending on their characteristics including:

Enhancing allocation and replenishment processes will improve margins, reducing a retailer's risk of being out of stock or having to markdown products.

Distributing space to different products

In addition to helping retailers to more accurately buy and sell their goods and services, analytics can also help them to allocate space by category or by department, for every store or store grouping, to better match local demand. This is known as macro space optimisation. The analysis can help identify categories that might have performed well in the past but are not necessarily the most profitable use of space for today. In extreme cases, a category may need to be dropped from a store altogether. Analytics aids fact-based decisions, taking any emotional gut-instinct out of the equation.

At a more detailed level, micro space optimisation will inform allocation of product space on shelf, providing insight around the optimum number of facings and positioning of products. Doing this work up front can help drive performance across categories and minimise inconsistent use of space across similar stores.

Retailers are facing challenges to their margins across their business, but where there are challenges, there are opportunities. Developing a merchandising analytics capability can be a powerful approach to delivering the right product for the customer. Ultimately, it can help to significantly improve a retailer's bottom line.

Deloitte's Toby Paxton writes a monthly supply chain & logistics column for Essential Retail, detailing what retailers must consider in order to build the right supply chain architecture.