Purchasing power, passerby vs. resident.

How has the purchasing power been used so far?

Purchasing power and retail purchasing power provide sound information on the economic strength of a region and are thus an indispensable foundation for location decisions. However, since the retail sector in Germany has so far lacked important data for analysis, the potential of these important key figures has not been exploited. WHAT A LOCATION! offers the possibility to differentiate retail purchasing power between passers-by and residents.

Data collection - purchasing power

The results of wage and income tax statistics serve as the basis for determining purchasing power. Deducting wage and income taxes from gross income yields net income. Using evaluations, pensions, unemployment benefits I/II, housing, child and parental benefits, as well as BAföG, are included in the calculations. The purchasing power calculation is based on the disposable income of all private households in Germany.

Data collection - retail purchasing power

The assortment purchasing power was determined on the basis of the continuous survey and analysis of consumer shopping behavior. In addition, the results are compared with the latest figures from official statistics. Further details on the data collection can be found here.

The problem: 

Both data are available on the market only from residents (registered persons). Regarding granularity, most companies use these values on a five-digit zip code basis, but at best, on a street level. Indeed, this is a budget issue. But one must ask the question: are residents my customers? Do the values differ between passersby and residents, and what impact can that have on location-based decisions? Or is the business more about people outside my zip code? 

Why is it important to distinguish between resident and passerby?

Let's take a concrete example: Imagine a specialty store for furnishing supplies in Berlin, preferably at a busy location such as Friedrichstrasse 120 (fictitious address). For reasons he cannot explain, the owner is experiencing increasing sales losses. All advertising measures and adjustments in the assortment have not led to any positive results. A process that has now lasted more than two years.

The company analyzes the retail purchasing power for the "Furnishing Supplies" assortment and finds that the value per household at their location is €250 (notional value). We know that this value is the retail purchasing power of the registered families in the zip code area of Friedrichstrasse 120. However, from experience, the owner knows that his customers come from all over the city and relatively less from the immediate neighborhood. His customers tend to be female and between 30-50 years old. The store does not have concrete evidence for this "gut feeling" because they do not have the necessary data to determine the actual catchment area of the passers-by. The assumption arises from the conversations with the customers and the evaluation of the postal codes on the few extra invoices issued by the customers, which, incidentally, does not provide any information about the overall potential of the passers-by.

What is the result for the business?

It's obvious: With the data currently available for the business; the company cannot make an informed decision to optimize advertising measures, adjustments to the product range, or the like to increase sales again. Why? The company lacked important information: 

• Catchment area of passers-by
• Retail purchasing power of actual passers-by
• demographic (age / gender) target group analysis of passers-by

In short: Who are the people in my location, where do they come from, and how high is their retail purchasing power for the furnishing supplies segment? This complex question gives rise to many exciting application possibilities for the retail store: 

• Where can my target group be found?
• Where does it make sense to place OOH advertising to reach the target group directly?
• Does my product range even fit the demographic structure of my site visitors?
• Has the pedestrian structure changed within the last few years and have I not noticed?
• Do passers-by even have the needed purchasing power?
• etc.

What could be the solution?

WHATALOCATION combines the purchasing power analysis data with geo-data from the German mobile network. The result is information about the value of actual passers-by at a location, because this usually differs from the reported households at a site, as they are subject to constant changes in the movements of passers-by (commuters, tourists, etc).

The store is able to distinguish between passerby and resident, which provides valuable information:

First, the company receives validated information about the catchment area of the passers-by and can filter them according to age groups and gender distribution. From a top 25 list (example for simplification) of postal codes, WHATALOCATION now calculates the average retail purchasing power of all passers-by in the catchment area and within the target group defined by the store:

Retail purchasing power passer-by: 689€
Retail purchasing power resident: 250€
(fictitious values)

The store can now identify seasonal changes in footfall patterns and adapt its actions accordingly to the constant changes in footfall patterns at various times. The owners can determine precisely where to place advertising and enjoy a much lower ROI on their campaign. The purchasing power of passers-by is significantly higher than that of residents. The high influx of "non-residents" underpins this.

The result can reveal a company's strengths and weaknesses, as it compares the purchasing power of residents with the real potential of passers-by, putting company results in perspective. Marketing campaigns, such as direct mail, can focus on areas with high purchasing power, reducing waste and increasing response rates.

In location evaluation and planning, for optimizing marketing measures or support in sales: the retail purchasing power of passers-by and residents, in comparison, provides a decisive and valuable building block for location decisions. Based on the existing potential for the home furnishings segment, any measures can be optimally tailored to the consumption preferences of local consumers. Likewise, our data provide a decisive competitive advantage regarding location issues.

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