The Retail Transformation Blog

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The hidden impact of improved retail store location planning.

In an era where many retail sectors are consolidating their store base in the face of dramatic changes to the retail landscape, the planning and insight that informs store investment decisions is arguably more important today than it has ever been.

The cumulative financial effect of taking better decisions to drive the future shape of a store network is huge – it could impact the long-term prospects of your stores business as well as annual profit, cumulative cashflow and ultimately business valuation.

Let’s take a typical example of a fashion retailer trading from stores of 2,500 average net sqft. The difference in sales density between a ‘star’ performing store (generating densities of circa £725/sqft) in this chain and an average store (operating at circa £500/sqft) translates to an annual difference in sales of £563k. But this delta is often exacerbated at the level of operating profits, where the higher density stores in the network can typically deliver a higher operating margin. In this example, a difference in margin of 27.5% for the star performer vs. 22.5% for an average store delivers an annual profit difference of £217k.

Property decisions are long-term investments

However, the key issue surrounding store property decisions is not just a single year’s difference in operating profits between two stores. Leases still typically run from anywhere between 5 and 15 years, so property decisions are long-term investments. So, for each store, a profit differential in a single year can go on to deliver a cumulative effect on the long-term profitability, cashflow and, ultimately, valuation of the business. In the example above, the profit delta across a 15-year lease is a whopping £3.26m.

In addition, by improving the quality of store decisions, retail businesses can consistently improve their chances of opening future star performing stores rather than average performers. By improving 25 location decisions as a result of more informed location planning, the cumulative impact on profits in this fashion business example would be more than £80 million across a 15-year lease.

The underlying store models in your own business/retail sector will undoubtedly look quite different, but these enormous cumulative effects of improved location decisions will be at play even if they are not transparent.

The value of effective location planning is difficult to quantify

Despite this, quantifying the financial impact of effective location planning underpinning improved store decisions is not easy to do. You can only ever truly see the performance of stores you have actually opened, not the ‘opportunity cost’ performance delta of stores you might otherwise have opened.

It is perhaps for this reason that a surprising number of leading retailers still pay lip service to high quality location planning – often eschewing the proven (but often complex) data and modelling approaches that can add real insight and financial value, in favour of more traditional approaches to estimating how a new store might trade.

In the omni-channel era, where the role of stores and their financial contribution to the wider business is under increasing scrutiny, the ability to blend insight driven out of powerful data and analytics will be one of the attributes that defines thriving retail businesses.

The ability to blend data from different sources is increasingly important for several reasons.

1 The interactions that occur to deliver in-store sales and/or footfall have grown increasingly complex; gone are the days when the full contribution of a retail store to the business can be determined by simply looking in the till and counting the takings at the end of the trading day.

In a recent blog – The halo effect – we explored the importance of understanding the interaction of the physical store channel with other customer channels (e.g. online, mobile, contact centres). Experience shows us time and again the synergy that exists across channels whereby the presence of a strong store channel helps to contribute incremental sales to the other customer channels.

The reasons for this (a) brand awareness – the physical store presence in a geographic area helps to increase brand awareness/recall among local consumers (b) pathways to purchase – the store channel plays a key role in customer journeys that may end up transacting through other channels (c) store support functions – functions such as returns, click and collect, and after-sales service help to make a non-store channel purchase more likely in areas where a strong store footprint exists. (For more details of the halo effect, read the full blog here.)

2 The role of the retail store is evolving. The KPIs used to determine whether a store is trading at, above or below “acceptable” levels are growing as the role played by the store evolves. A store might be judged on its own sales and its contribution to other channels (see above), but there might be other customer service level objectives and operational standards that should go together to give a more balanced view of the wider contribution of each store.

3 There are new and insightful external data sets that can shed light on how and why stores perform as they do. In addition to the ‘point in time’, static metrics traditionally used to assess the retail venues and catchment areas that provide the market context in which stores trade, data from mobile devices, credit card spending and social media activities can now be used to give a new perspective on who uses stores and the surrounding venue, when they are there and how they are spending – all angles which can add new insight into how and why stores perform as they do in their local markets.

Software tools, such as Tableau and Alteryx, have evolved fast to facilitate this use of increasingly near real-time data streams in the analysis of stores to inform both store investment and operational efficiency decisions.

Where analytics was once historic – i.e. shedding light on historic data – the balance has now shifted to predictive and even prescriptive modelling around the retail store estate. As this occurs, the analysis which underpins the optimisation of store estates is also changing.

Whilst decisions around investing in stores has traditionally been a long-term commitment, the upsurge in transient retail (from static pop-up stores through to mobile units) and the opportunity to adjust the product offer and staffing base in-store to respond to anticipated peaks of demand creates new opportunities to help stores achieve their true commercial potential.

For further reading on this topic, read Accenture Strategy’s report – Adaptive Retail: Redefining the role of the stores to improve competitiveness.

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