Identify the best areas to locate your new Points of Sale
Sales forecast data model for site selection
Coverage
History
Quality
85% model accuracy
Common use cases
Other companies are using this dataset for:
Consumer behavior analysis
Location intelligence
Retail Site Selection
Demand and Revenue forecasting
This model aims to provide a clear understanding of the key factors that influence the profitability of a business in a given location.
Background
This model helps identify the ideal areas for the location of new POS by measuring the impact of a set of variables at a geographic level on the sales of a particular type of business.
Featured attributes of the data
The model considers several key variables, including the number of pedestrians, traffic flow, resident population, income levels, and concentration of businesses. Furthermore, it is enhanced with data on the population’s interest in a particular topic, measured through their interactions on social media platforms.
The model evaluates all variables at the spatial grid level. It is possible to include existing points of sale and their respective revenue to estimate the billing of an additional POS in the best areas identified for a specific type of business. This extra information helps identify the ideal location for a new business.
Methodology
- Update Frequency: Quarterly and Yearly.
- Data Delivery Methods: S3 Bucket, Rest API, Soap API and Feed API.
- Delivery Formats: JSON, CSV and SQL.
- Geographic Coverage: Europe (32 countries), North America (10 countries including US) and South America (6 countries including Brazil).
Data Sample & Dictionary
- Request a simple data sample here.
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