Why Do Competitors Open Their Stores Next To One Another?

Pizza Hut located right next to Domino’s Pizza. Starbucks placed across the street from Coffee Bean. 3 petrol stations on the same intersection. Have you ever wondered why restaurants, gas stations, and cafes are usually crowded in one singular spot? There might be an occasion where you had been driving for hours without seeing a petrol station, but when you spot one, you see a couple more clustered together as well.

Although it might seem more logical for similar businesses to move further away from one another, business theories show that it is not optimal for competitors to be placed extremely far from one another. As competitive companies vie for the same piece of the pie, discover how game theories such as Hotelling’s Model of Spatial Competition, Social Optimal Solution, and the Nash Equilibrium play a part in determining the placement of a business with regards to its competitors.

Hotelling’s Model of Spatial Competition

One reason why you come across similar businesses appearing in groups instead of being spread evenly in the community is explained with a theory known as Hotelling’s Model of Spatial Competition. Harold Hotelling analysed a model of spatial competition, the location of different businesses in a similar market respect to one another.
According to Hotelling, when competing on location, each business wants the central point as it is the most strategic spot that allows it to be as close to as many customers as possible. Since every business has the same mindset, they will be competing with one another which eventually causes similar businesses to end up in a cluster focused on one specific point. This phenomenon is apparent for gas stations, fast food chains, gadget retailers, et cetera.

Brief Explanation of Hotelling’s Model of Spatial Competition

Imagine that there are two hamburger stalls that are located on the same stretch of a 100m street, and the road is a straight line. Both agree to place their stall evenly along the road, so that stall A will gain all the customers from the left of the middle and stall B will get customers from the right of the middle, creating what is known as the socially optimal solution – minimizing the distance required to reach either one of the stalls and both stalls gaining equal benefits from the placement of their stalls.

Progressively, both burger stalls will want to attract more customers than its rival, causing them to move nearer towards the centre of the street. Consequently, both stalls will eventually end up in the middle of the street – the central point, both serving 50% of the customers.

This is an example of Hotelling’s Model of Spatial Competition in real life, which explains why businesses of similar types tend to converge on a single point.

Socially Optimal Solution

In the passage above, the term “socially optimal solution” is mentioned. This is a state where it is the best solution for all parties involved; a win-win situation of sorts as both gains equal amount of benefits from the “agreement”.
In other words, the socially optimal solution is the most ideal plan or strategy for all businesses involved to maximise their benefits. However, the main problem of the socially optimal solution is that it is unstable, as both parties would want to get the upper hand on its competitor and have the capability to do so. This desire to gain an advantage over the other will cause either one of them to find a way to move to a position that benefits them better.

Following the example above, stall A perceives that if it moves 10 meters nearer to the middle towards stall B, it potentially enables it to capture some of stall B’s customers while retaining its original pool of customers. To get back its customers, stall B also moves 10 meters closer to the middle. Eventually, both stalls end up in the middle of the street, achieving what is game theorists coin as the Nash Equilibrium.

Nash Equilibrium

Nash Equilibrium is a state where a party chooses an option that benefits them the most despite the other parties’ choices. Following the example mentioned above, both stalls involved are unable to benefit by repositioning themselves away from the central position and will need to resort to neighbouring one another.
As both these stalls are now neighbouring one another and are unable to benefit from relocating, they will now need to rely on marketing strategies to gain an advantage over the other by differentiating their products, offering promotions, creating publicity, et cetera. This is also a reason why you notice that similar businesses tend to have promotions that reflect the ones offered by the competitors.

Pros of Clustering

Although the logical reasoning is to divide and conquer, spreading out services throughout a community might cause companies to face more vigorous competition. At the heart of many business’ strategies, most would like to keep their competitors as close as possible.

Firstly, being close to their competitors enables them to have a better idea of the strategies implemented by their rivals. This enables companies to gauge the feasibility of the strategy to identify if it can be mirrored to be used for their own, with some minor improvements. For example, McDonalds offers a new breakfast set that includes a burger, a coffee, and a piece of hashbrown for $5. KFC might find that the strategy has brought a rise in McDonald’s customer base and will use a similar strategy by introducing a breakfast set of their own to counter McDonald’s approach.

Another advantage brought by clustering is that it offers more options to customers. When customers see the different variations of similar products that fellow competitors provide, it might cause them to change their initial decisions. As an example, an individual who initially wanted to have Domino’s for lunch might catch a glimpse of Pizza Hut’s latest menu and choose the latter instead. The probability for customers to change their buying decisions are higher when there are several businesses gathered in a particular area.

Source: smallbusinesscan.com