Business Analytics Blog

Business Analytics Blog

Opinions expressed on this blog reflect the writer’s views and not the position of the Capgemini Group

Fancy a coffee?

Category : Food and Drink

It seems you cannot take a stroll down our UK high streets without being greeted by tempting aromas emanating from a trendy coffee shop. According to the British Coffee Association, it is estimated that we in the UK drink approximately 70 million cups of the steaming hot beverage a day. Taking an approximate UK population of 62 million, if we remove under 16’s at approximately 19% (they haven’t got money for coffee!) that leaves us with 50.2 million people consuming on average 1.3 cups each. Hhhmm...not a lot on the face of it. If, however, the United Kingdom Tea Council is correct and we apply a 2:1 ratio of tea to coffee drinkers, we pull that figure up to between 4-5 cups a day. Judging by my caffeine intake on a rainy summer’s day in London, I would not argue that.

With coffee consumption reportedly on the rise, we can expect to see more and more coffee shops emerging to feed the thirst. As I passed yet another Starbucks on a corner on my way to Cannon Street, I started to think about the decision processes that these businesses adopt to work out where their next outlet will be located. What are the main factors they take into account? Customer information? Likely competition? Potential footfall?

Customers are ultimately the lifeblood of a business and so it follows that the more you know about them, the better - this is the role of customer analytics. Some information may be obvious. For example, making sure the new outlet is located correctly to focus on the targeted customer demographic and ensuring that the products to be sold will appeal to potential customer taste (and wallet). However, there are more subtle factors which, when taken in account, can help to achieve a successful decision. For example, understanding your potential customers at such a level where behavioural trends and spending patterns are not always consciously known by the customers themselves through analysis of attitudinal and behaviour data. Data such as this can enrich long term strategy and allow a business not only to understand who potential customers will be but also when and where they will suddenly get the urge to satisfy their caffeine fix.

With that, I present a small game that will turn you, dear reader, from analytical theorist to a practitioner in the process of location planning from a customer analytics perspective...

Colin Nero owns Costalot Coffee, a range of trendy coffee shops around the UK.

Business is sporadic; great in some places and poor in others. Colin would like to open up a new store somewhere in the UK and believes he has captured all the information needed to make a decision. Being a lover of chess, Colin divided up the UK into a chess board grid and gathered information on each chess board square including details of available sites.

He intends to share the information with Capgemini so they can advise which square/site not already occupied by an existing store would provide the best location for his new store.

Colin counted the following types of location within each of the 64 squares:





 

Based on this information where should he locate and why? X, Y or Z?

 

So how do you tackle this type of problem? Let’s think though the logic. Firstly, what are the UNDERLYING TRENDS if any? It seems that:

1. Most sites with a consultancy near them perform well. Site F1 is an exception. • Site X with its existing market of coffee shops, tearooms and footfall, • Site Y with a number of tourist attractions and consultancies nearby, or

2. Most sites close to a tearoom perform poorly. Site G3 is one exception to this finding. • Could locating near a tearoom in the fiercely competitive Tea Vs Coffee war be costly? • Do all sites with tearooms near perform badly and therefore is site G3 enjoying boosted sales thanks to the nearby consultancies and attractions.

3. Sites which have tourist attractions seem to perform well; however, they can also perform poorly. • Are there further reasons for why sites with tourist attractions can perform well or poorly? • On analysis, there is a possibility that when tearooms and tourist attractions are closely located, they perform well, whereas coffee shops suffer.

4. More coffee shops close to a site seems to increase revenue • This may point to the fact that even though there is more direct completion about a site, there is also greater footfall from potential customers • Is locating where there is an existing market a safer bet?

These trends can help to form a location decision. So which site have you chosen? • Site X with its existing market of coffee shops, tearooms and footfall, • Site Y with a number of tourist attractions and consultancies nearby, or • Site Z which also has an existing market on which to feed, however no consultancies close by full of ripe customers and tourist attraction which seem to exist better with tearooms?

By gut feel, you may have reached the decision that site Y is the best place to locate. It clearly has the advantage of an existing customer base, tourist attractions to exploit and no competition. Also there are no tea rooms to compete with. This may seem a logical choice, however, when we apply some simple mathematics to the problem, the choice looks less certain.

By analysing the sites of the high performing outlets, we can work out that the best average mix of businesses to have in close proximity:



By applying this mix to the sites X, Y and Z and plotting the results on an index comparison style chart, we find that: • The assumption that site X is the worst choice is confirmed by the long blue bars. There are too many tea rooms and not enough attractions or consultancies • Site Y looks better but in terms of shape, there are not enough coffee shops (existing markets) in order to maximise on revenue • Surprisingly, site Z performs the best in terms of the mix of close businesses. True it has too many tea rooms and not enough consultancies, but it has the right mix of competing coffee shops to ensure adequate footfall and also has the best shape of the three sites overall. This would be my choice.

This is an extremely simple example in the application of analytics which can be undertaken in minutes. When extra complexities such as deeper demographic information, site costs, strategic implications and local workforce are added to the decision making process, arriving at a sensible result is a lot more difficult. However, whichever site you decided, you are now one step closer to structuring problems in an OR way - congratulations!

About the author

Nigel Lewis
Nigel Lewis
Nigel leads the Capgemini Consulting’s 35 strong Business Analytics team, which delivers analytical, operational and strategic modelling solutions to clients. He has 18 years consultancy experience as well as 8 years experience in the UK gas industry. Nigel has successfully managed complex projects in both the public and private sector, including capacity modelling, simulating supply chain operations, strategic business modelling to support future policy decisions, and implementing complex demand forecasting systems. Nigel is currently focussing on the development of Capgemini’s customer analytics and analytics advisory services.

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