From Building to Building...
The analysis of customer data is a central component of modern corporate strategies. It becomes particularly valuable when additional (anonymized) information about the customer's place of residence and purchasing behavior is included. Analysis at zip code level is often already useful.
Special issues make it necessary to look at individual buildings. A detailed analysis makes it easier for companies to understand consumer behaviour in relation to the location situation and to optimize targeted advertising or home delivery.
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With the House2House matrix
Nexiga, a dataset with building data, can provide a valuable database for detailed analyses.
Spatial patterns in shopping behavior
With the advent of digital apps for customers, especially in food retail, aggregated data on customers' addresses and purchases is increasingly available. By analyzing this (anonymized) data in relation to customers' places of residence, interesting spatial patterns in shopping behavior can be made visible.
A frequently observed phenomenon is the urban-rural divide in consumer behavior. In urban areas, the density of stores is higher and distances to shopping centers are shorter. Customers shop more frequently, but usually make smaller purchases. In rural areas, on the other hand, there are often fewer stores and the distances are greater. As a result, customers shop less frequently, but in larger quantities.
House2House - Insights at building level
With the House2House matrix , Nexiga offers a comprehensive data set that is available across all spatially relevant data at the level of individual buildings. This allows precise routing to be calculated from the perspective of car navigation or pedestrians and the result (distance) to be assigned to each relevant building.
The data and calculation options of Nexiga's House2House matrix can be used to determine the nearest store and the distance for the shortest route to a specific building.
- An overview of the process
Nexiga database
Use of the House2House matrix, which contains comprehensive spatial data at building level. This database enables precise distance and routing calculations.
Assignment of the nearest branch
Once the distances have been calculated, the data is analyzed to identify the nearest store for each building.
Distance/routing calculation
The House2House matrix can be used to calculate the precise routing for different means of transportation (cars, pedestrians). The shortest route between each building and the shops/branches is determined.
Presentation of results
The results are prepared in such a way that the nearest branch and the corresponding distance can be displayed for each building.
However, it is not only the infrastructural conditions that influence the spatial patterns of consumer behavior.
Socio-demographic factors often play an even greater role. These factors include income, family size, age and level of education. These (anonymized) factors significantly influence which products are preferred, how often purchases are made and whether online or offline shopping is preferred.
Location intelligence with Nexiga data
Nexiga's extensive data provides valuable insights into the economic potential of locations and regions. This data is particularly useful for companies to optimize their product ranges and make informed decisions related to locations and markets. By combining geodata on population and buildings, companies can carry out precise analyses and adapt their strategies accordingly.
- Socio-demographic factors
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Age and generation: Different age groups have different preferences and shopping habits. Younger generations tend to prefer online shopping, while older generations are more likely to visit bricks-and-mortar stores.
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Income and education: Disposable income influences purchasing power and willingness to spend money. More highly educated people often tend to make informed purchasing decisions.
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Marital status and household size: Families with children have different needs than childless households. Larger households tend to buy larger packs.
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Occupation and employment: Occupation can influence shopping style (e.g. work clothes, working hours). Commuters may have different shopping habits.
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Place of residence and surroundings: urban vs. rural areas: Accessibility to stores, transportation, etc. The place of residence can also influence cultural preferences.
USE CASE
As an exemplary use case, we show the benefits for a supermarket operator (food retail) who can access a wide range of data by introducing a customer app.
By evaluating the purchasing behavior of registered users linked to their place of residence, it is possible to analyze whether spatial patterns exist. The following key data must be taken into account in particular:
- Local focus areas in which the app is used more frequently than average
- Socio-demographic environment in relation to the products that are preferably purchased there
The application possibilities are very extensive. The building matrix (processing) is calculated by Nexiga and the results are made available as a database. Further analyses can be carried out by the company using database queries. A geographic information system (GIS) is not required, but the data can be further processed using stored coordinates with GIS solutions such as Marktanalyst Pro. The advantage is that the House2House data can be linked to internal company data.
Objective
- The product range and pricing policy is optimized from the perspective of the operator (company) based on customer data and preferences. (Where do customers who prefer home delivery cluster, how can they be optimally supplied, etc.)?
- Determination of target areas for the sale of certain product groups as well as the evaluation of catchment areas of stores and branches by analyzing spatial distances of social milieus.
- Assessment of sales potential: Based on building characteristics and features such as garden (by size) or other building data, the sales potential of garden tools, for example, can be better estimated.
- Marketing and customer loyalty measures such as special offers, vouchers or mobile payment offers can be targeted to customers (app users) directly in the app.
- Optimization of store network planning
- Increase in customer satisfaction
- Efficient logistics and home delivery
- Improvement of marketing strategies
