Finding Big Square’s Next Outlet Location

Originally published in October 2017

The City of Nairobi has 1,695 restaurants — compared to major cities in the world, Nairobi is underserved by restaurants (see chart below). Disparities in per capita income which stands at $1,081 in Nairobi and $54,373 in New York City explain the difference in the number of restaurants per person. However, an assessment of MPESA’s role in the economic lives of Kenyans revealed access to M-PESA had increased consumption levels. This enabled up to 2 per cent of Kenyan households to move out of poverty.

Economic mobility coupled with aspirational behaviour creates the perfect mindset to indulge in conspicuous consumption. Java House Africa has tapped into this economic behaviour and set up 55 outlets within the city of Nairobi. Other food entities are fast learning from Java House’s success and are embracing the same business model. One such company is Big Square — a casual dining restaurant serving burgers, fried chicken, BBQ ribs and accompaniments.

With 9 outlets opened in the last five years, the restaurant chain is on a growth path with two new outlets rolled out in Shell Petrol Stations. This is an aggressive expansion approach given Shell Petrol stations were a preserve for Java House outlets. Like all brick-and-mortar businesses, location is key. So, where should Big Square open its next outlet to maximize profits?

Finding Home

There is an interesting theory known as the Central Place Theory that explains the conditions necessary to create Malls. These are; Threshold — the minimum population and income required to sustain a market, and Range — the maximum distance consumers are prepared to travel to acquire goods. Given these two factors, Big Square’s assured market was in Karen, where they opened their first outlet in 2012. A similar approach was used to set up the next outlet in Lavington the following year. The diagram below shows the current dispersion of their outlets.

The Magic Sauce

Using the Central Place Theory, we develop a methodology that interests and success of a given location. The underlying assumption is that when a new outlet is set up and it becomes profitable, the general area becomes of greater interest to the company thus setting up another outlet close by to serve more customers.

To achieve this objective, we rely on a method known as a Voronoi diagram. This algorithm partitions a region into areas of influence as shown in the diagram below. Initially, we have one region centred in Karen, when the next outlet Lavington is opened, the Karen region has to be split to give territory to Lavington. The same process is repeated when other outlets are opened, i.e. Gigiri is split from Lavington, and Oval is split from both Gigiri and Lavington.

The interlocking regions form a Steiner Minimal Tree which visually shows us where areas of interest as emerging. However, we are interested in a probability score on the viability of an area. To that end, we track which region was split to form another and produced the dataset shown below. A ‘1’ indicates a split and a ‘0’ no split.

Given we have a numeric representation of the hierarchical relationships of the Voronoi diagram, we can apply quantitative algorithms to the data. The algorithm that fits this problem is Bayesian Inference, a concept anchored on conditional probability which seeks to evaluate the probability of an event happening given a previous event has happened. In our scenario, that would be evaluating the probability of an outlet being opened given another one has been opened in the same region. The diagram below shows the prior and posterior probabilities.

We compute the prior probability by calculating the probability of setting up a store in an area. Then we utilized conditional probability to calculate the posterior probability which indicates the probability of setting up an outlet given another has been set up in the same region. An amazing output emerges, Oval region has a posterior probability of 1, which means it is certain that a new outlet set up here will be profitable. Also worth noting is the high posterior probabilities of the Karen and Lavington regions. But this tells us half the story.

The Other Sauce
To get a different perspective, we utilize positional probability — a concept in statistical mechanics that measures the randomness of molecular structures in substances. In our case, positional probability will measure “randomness” by the different type of restaurants that exists in each region. To that end, we access restaurant distribution data from eatout.co.ke and map them to their respective region.

Positional probability relies on entropy, which is the natural logarithm of the probability of an event. The events in our data are the type of restaurant i.e. Mexican, Indian, Coffee House, Fine Dining et cetera. High entropy means there are many different types of restaurants in an area, and low entropy means the same type of restaurant. We seek an area of high entropy because it tells us people who frequent the area have diverse tastes hence a new speciality restaurant like Big Square can do well in the area.

The positional probability is completed by looking at how much space is available for a given configuration. In our scenario, the configuration is captured by entropy. So, we calculate the area of a region and then multiply it by entropy to get our positional probability. A higher value will tell us there’s high diversity and more space for the diversity to thrive. An area might have a high diversity (entropy) but less space (high competition) to set up a new entity e.g. CBD.

The Conclusion

To compute the final probability, we calculate the joint probability of two probabilities (positional and conditional), in this case multiplying the two probabilities. From the diagram above, the Karen area has the highest probability (0.73) of a new store succeeding. This tells us a lot of restaurants have been set up around Karen which are very diverse and there is a lot of space (less competition) to set up a new one. The best candidate location would be Galleria Mall.

Astonishing is the other location gets very low probabilities — this is because the methodology makes assumptions that a good location should have an existing outlet. If the methodology is inverted and we consider a good location as one in which Big Square has no existing store and competition is healthy, then we get different results. Using Steiner Minimal Tress for the conclusion, we overlay the regions onto a map as shown below.