What Locations Make Your Retail Format Successful?

Despite constant change and disruption in today’s market, retail location is still an a key success factor. Do you know what type of locations are best for your format?

As retail location remains one of the most important success factors,  understanding what makes one location more successful then another one is key for any successful expansion. Clustering locations helps  to define the optimum blueprint in terms of store size and layout, staffing, assortment/brand mix and merchandise management.

Surprisingly,  many brands still struggle with classifying their retail locations. As location clustering often fails due to different points of view on how to evaluate location criteria, we began to use hard facts as basis for location clusters.

The Classification of Retail Locations

In our most recent projects we used three key metrics to classify retail locations:

  • Traffic per hour
  • CR (Conversion Rate)
  • UPT (Unit per Transaction)

But of course other metrics like ATV (Average Ticket Value) or ASP (Average Sales Price) can be used too.

Traffic per hour is a key metric for rating performance of retail locations. Ultimately, the traffic potential of a location is what you pay your rental fee for.

Unfortunately, not all brand and retail formats work in all locations. Thus, overall traffic numbers might give an indication about the general performance of a location – but it is the relevant traffic, that decides about success or failure of a store in a certain location. What really makes the difference is the share of relevant consumers out of the total traffic.

Purchasing power, shopping preferences, lifestyle, taste, recommendations from friends and many other factors determine whether a consumer will consider a brand or a retail format relevant to them or not.

retail locations relevant consumers

(Graphic: Heike Blank)

Thus, having traffic numbers of a certain street or shopping center might give you an initial idea about a retail location. But what counts in the end it is the number of visitors that actually enter your store. To make your life even more complicated, not all consumers who enter your store are indeed relevant consumers for your brand or retail format.

retail location oxford street

(Screenshot: Google Maps)

In high-traffic locations like Oxford Street in London, Kaufinger Strasse in Munich or Broadway in New York City you will ultimately experience a high number of visitors entering a store but a low share of them will actually end up buying.

We call those locations ‘mainstream locations’, as traffic in those locations is a colorful mixture of consumers, tourists, professionals and commuters. Mainstream locations are ideal locations for mainstream brands and retail formats in a low to medium price segment. Zara, Uniqlo, Next, H&M, Forever 21, Boots and the like perform excellently in that type of location and are able to pay the considerable rental fees. The more distinctive, unique and pricy a brand, however, the lower the share of relevant consumers in those mainstream locations will be.

The Four Types of Retail Location

Yes, store staff really can make a difference. But the dimension of your conversion rate will show whether a particular location generates enough relevant traffic for your brand.

retail location dimensions

(Graphic: Heike Blank)

Taking into account the two dimensions Traffic per Hour and Conversion Rate, we can cluster retail locations into these four types:

  1. Prime Locations = High overall traffic with a high share of relevant consumers, hence high conversion rate
  2. Mainstream Locations = High overall traffic but with a low share of relevant consumers, hence low conversion rate
  3. Niche Locations = Low overall traffic but with a high share of relevant consumers, hence high conversion rate
  4. Flop Locations = Low overall traffic with a low share of relevant consumers, hence low conversion rate

Double checking our assumptions by analyzing UPT and 4-Wall-Contribution (4WC) of stores in each location type confirmed that stores in prime locations generate the highest UPT and 4-WC, followed by niche locations, which generate UPT and 4-WC above portfolio average, whereas mainstream locations generate UPT and 4WC below portfolio average and flop locations generate lowest UPT and 4WC.

retail location clusters

(Graphic: Heike Blank)

The same analysis using ASP by location type, however, show less distinct results. Stores in prime locations also generated the highest ASP among all portfolio stores. And the majority of stores in flop locations performed below average in terms of ASP too. But in mainstream and in niche locations the share of stores performing above and below average in terms of ASP was almost equal with slightly more stores in niche locations performing above average.

Friendly Neighbours: Adjacencies Matter

Taking a closer look into individual locations and their adjacencies we realised that the price segment of neighbouring stores made the difference. Stores with neighbours that sell products in comparable or higher price segment performed significantly better in terms of CR, UPT and ASP. Whereas stores in locations with low price or discount adjacencies performed significantly worse.

Defining your optimal adjacencies is therefore a key success factor for a sustainable and profitable expansion. Understanding retail location dynamics and how your brand and retail format benefit or suffer from it does not only help to select the best retail locations for future expansion. It also helps with tweaking staff planning, structure and capabilities as well as finetuning your assortment in those locations. But that is a story for another article!


About the Author:

Heike Blank has worked for big organizations such as VF Europe and s.Oliver but also for niche brands such as Ecko Unltd. and Zoo York in top executive positions. Her extensive experience with opening and managing own retail, partner stores, concessions and shop-in-shops in 23 countries in Europe, the Middle East and Asia make her an expert in expansion. Read more of her work here and connect with her on LinkedIn.

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