How To The Rising (Part 4) : Location Comparison - A Simple Model

Rising from the ashes...
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Given two brick & mortar locations, which one is better, and is it worth the extra rent/lease?

I feel like I made a break-through, and wanted to share it with you...

At any store location there will be store-front signage.
According to Signage industry, store signage is equal to 24 full-page news paper ads every year. Based on this statement, we can use newspaper calculation to compare locations!

(Incidentally, newspaper response rate is very low at 0.5% among all media response rates).

24 full-page ads in a year suggests that the subscribers see the newspaper twice a month.

As suggested, we can calculate the signage effectiveness for a given location similar to a newspaper calculation. Similar to newspaper readers, we’ll assume 10% of viewers see the ad (store signage), and see the message 24 times a year with a 0.5% response rate. We can estimate potential number of customers for a site using the following equation:

newspaper_conversion_rate = 10% * 0.5% * 24
customers_per_year = site_traffic * newspaper_conversion_rate

site_traffic - This traffic is a two week estimate; remember, we are multiplying by 24 times in a year.

• The traffic can be the vehicle count for the nearest intersection, which can be obtained from local city engineering.
• A better option is utilizing a tool like SapStore to get foot traffic estimate
• There is also nothing wrong with doing it the old-fashion-way and sitting in front of store with a clicker (or secured mounted motion detection camera [or phone app]) to count the actual traffic.

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In this model I've ignored the significance of location (i.e. proximity to target demographics, competition, complementary business, etc), which I will attempt to tackle in another post.
Also, the newspaper demographics, circulation and reach is really different; so,
Signage can't be that great of estimate. Furthermore, the articles refer to billboard signs, as-appose to store-front signs.

Nevertheless, assuming all things are equal, you can quantitatively determine if one location is better than another and if the extra rent/fee is justified.

 

In the above model I suggested getting site traffic via numerous ways; but, I now want this traffic to better represent my prospective clients when calculating my conversion rate.

For this exercise I'm going to look at the traffic of neighboring business, whose clients are going to see my signage as they walk-by. Here is what I'm suggesting:

1. Compile list of businesses whose customer will see your business. For example, the business immediately to left and right of an open lease are great candidates.
2. Visit these businesses and explain to them that you are planning on leasing a space in the area and you are trying to estimate your physical traffic for the location.
3. Ask for the number of customers or transactions they had on previous day or week
4. Lookup the neighboring business demographics (or see if they know it)
5. Use the demographic data to calculate prospective clients for your site_traffic.
6. Plug-in this information into earlier calculation to determine your customer count.

For example, lets assume that I am considering to open a business immediately next to Safeway, which is a supermarket.
If I look at the respective demographics for Safeway, I see that 31% of its customers are 35-54 years old, 47% have kids, 48% household income above $60k, and 38% have college degree (or higher).
The probability of Safeway grocery customers meeting this demographics is 0.31 * 0.47 * 0.48 * 0.38 = 2.7%. This is the population of grocery customers that could potentially use my service.

Next, I multiply the total number of customers or transactions by my target demographics percentage from the neighboring business (i.e. 2.7%) to obtain prospective clients, which is the new number that will be used for site_traffic.
Thus, the site location signage is an ad (marketing spend) for the neighboring business customers; in this case, 2.7% of population is your target demographics.

We can continue and multiply this result by newspaper_conversion_rate to determine new customers acquired from this location.