When choosing a storefront location for your business, you will find that landlords will be eager to convince you that their location is the one for you. They might show you impressive foot traffic numbers, and try to guarantee you correspondingly high revenue. Another landlord might offer lower rent but correspondingly low foot traffic and potential revenues. If these arguments satisfy you – do not read any further. You already know it all.
Everyone else who wishes to build an effective business based on data – don’t trust landlords. They might not be deceiving you, but you should be capable of choosing the location yourself. What do you need to know? First, take the foot traffic going by the location and the demographics of potential visitors into consideration. How? You could hire someone to stand at the entrance of the store and count the number of people passing by and the number of people walking in for one month. Then you could attempt to gather information about them: their gender, age, race and purchasing habits. This would be a waste of time and resources. Today, we have a technology for everything.
The following case study will demonstrate the importance of gathering your own statistical data about your business and reviewing it regularly. This practice will help you increase customer turnout, conversions and revenue. It can even save your business if things go wrong.
Conor: A Case Study
Conor is a computer tech entrepreneur building a chain of stores that sell computer technology. One day he decides to open a store in a lesser-known town. Let’s call it Springfield. Conor has a business plan prepared. If he follows it, the store should break even in a year. But something goes wrong. Towards the end of the third quarter, it becomes clear to Conor that the plan isn’t working. The shop is 15% short of the profit it will need to break even by December. How did this happen? Here’s what Conor found:
· First, he looked at the investment plan. He looked at the conditions under which the store could break even by the end of the year.
· Then he looked at the Profit and Loss statements.
· Finally, he looked at the store’s basic statistics: the flow of customers, conversion and the average amount each customer spends.
Conor started by counting people: corridor pass-by frequency and street to store conversion coefficient or capture rate.
Let’s go back to last year, before Conor opened his store. Conor was planning on opening his computer technology store called “Computer Hardware”. The main location he looked at was in one of the largest shopping and entertainment centres in the city centre. The shopping centre has a total area of 200 thousand square metres. As well as a passer-by rate of 220 thousand people a week. An average of 880 thousand people a month. Or so the shopping centre’s representatives told him. Conor needs to know how many of those people will actually pass by his shop. To do this, he must count the pass-by frequency of the corridor where his shop will be located.
How can he do it? Conor installs special sensors, which count the number of people passing by. He learns that for “Computer Hardware”, the monthly average is 264 thousand visitors. Conor learned this figure before renting the space, doing any renovations or paying his staff’s wages. He collected accurate information, based on which he began to build his strategy. However, just knowing the corridor’s pass-by frequency is not enough.
Next, Conor had to predict how many of the 264 thousand people would enter his shop. This figure is called the capture rate (CR): the number of passers-by turned into customers. How did Conor know how many visitors would come into his shop? He didn’t, but he predicted the average CR by looking at the same figures from his other stores with a similar corridor pass-by frequency. Conor’s predicted CR was 8%, meaning that out of 264 thousand people in the shopping centre, 21,210 per month will likely go into Conor’s new shop. Obviously, the number of visitors to the shopping centre depends on the month and the season. Conor can get more accurate data after a year of running his store, and even more after two or three years. But for now, Conor is drawing up an investment plan based on average values.
Conor’s strategic plan
Conor has two metrics: corridor pass-by frequency and CR. Every investment plan should include Key Performance Indicators (KPI) for the shop’s first year of operations. Conor estimates his marginal cost of goods for the year at 18.6%, whereas the projected share of services is expected at 3.5%. So far, so good. Conor expects the average receipt to be $86 USD and the conversion of visitors to customers to be 6.4%.
Based on his KPI and his knowledge of the number of visitors, he can predict the store’s annual sales. Going into his first year, the plan was to earn $1,030,000! But as we previously stated, it did not work: in the third quarter, the shop was coming up 15% short of the profit it needs to break even. Why?
Conor locks himself in his office and studies his Profit and Loss statements for the first three-quarters of the year. He sees that, from January to July, the store operated at a loss. He looks at his investment plan and realises that the problem lies in lower-than-expected store visitation. It was only 55% of what he predicted, and his conversion rate was only 48% of his projection from the investment plan. How does he know this? Because he previously installed counters, which count the corridor pass-by frequency in his store. He also installed counters which analyse the number of visitors to his store and the conversion rate. Conor also knows that there are no issues with the average amount each customer spends, all thanks to those same counters. His question now is what to do with the visitation rate and conversion, in order for the shop to finally start making a profit?
Conor once again counts people and increases visitor frequency
To increase the store’s visitation frequency, it is necessary to know what the shopping centre’s corridor pass-by frequency will be in the fourth quarter. Conor didn’t open the shop at random. He pre-measured the average corridor pass-by frequency, which he knows is 264,000 people per month.
This figure was then taken as the basis for his investment plan. It now allows him to calculate the deviation between the predicted frequency and the actual frequency for the past three months. When Conor reviews his data, it turns out that the actual corridor frequency amounts to only 72% of the predicted frequency. Conor adjusts his prediction of corridor traffic for the months of October to December accordingly, to 72% of the predicted, which is a total of 190,080 people.
Conor can now calculate how many more people he needs to attract to the store to reach the planned level of sales. He attempts to increase his visitation frequency with the aid of marketing campaigns, sales and advertisements. Conor is armed with modern technology and can see, in real time, the effect of his efforts on the CR. He successfully increases this rate from 4.8% to 7%. As a result, the store’s visitation frequency increased by an average of 19% in the fourth quarter! Well done, Conor!
Conor counts people and increases his conversion rate by 2% while increasing profits by 20%
However, Conor still needs to increase his conversion rate. It turned out to be almost two times lower than anticipated in the investment plan. To increase the conversion, Conor worked with his staff, as the conversion rate of the store depends strongly on their competence and motivation! An increase in conversion from 10% to 12% will increase revenue by 20%. Not bad, right? Conor has all the necessary figures at his disposal. So, he makes quick calculations and develops a new plan of action. How can he achieve this 2% increase? It is only two extra customers out of every hundred who visit the store. One employee only needs to serve two more customers out of every hundred, or if the store has two employees, they only need to serve only one more customer each out of every hundred!
Then the store will bring in 20% more in revenue. Conor is counting on increasing the conversion by 0.5% a month by changing how he trains his employees and by changing his incentive system. He will pay out premiums for implementation of the sales plan and bonuses for selling extra goods. And, of course, he will replace inefficient employees. By increasing visitation frequency, conversion, and maintaining the average receipt, Conor’s store reaches a break-even point by December of 2016!
In conclusion: learn to count. To do so you will need to collect large amounts of data. To get this data, you need to set up counters. They are inexpensive and you can count on their data to increase your visitation frequency, conversions and earnings, just as they did for our friend, Conor.