How can I use the data
Analyze the information you gathered about your customers in a location context. Location Intelligence will provide you with details and activities to build more profound and practical customer relationships and improve decision-making within your organization.
Catchment Area Analysis
Finding out the real catchment areas of your branches and comparing them with driving or walking catchment areas can help you optimizing the locations of your branches
Location Planning Parcel And Delivery Lockers
Locate the places with an insufficient coverage based on data about population. Evaluate planned locations and find out straightaway what amount of people will you cover
Market Potential And Share
Analyze your current market share on a spatial level to focus your expansion efforts on the areas with a potential customer base and a beneficial number of competitors
Competitive Intelligence Market Research
Analyze the presence of your competition and include the findings in the business strategy. Location data will help you to try targeting areas with more potential
Site Evaluation
Evaluate the retail potential of any address and measure its attractiveness within its natural catchment area. Choosing the right place for you business has never been easier.
| id | postcode | name | category | spend_mio_eur | spend_per_mio_country | spend_per_capita | spend_idx |
|---|---|---|---|---|---|---|---|
| 1 | 10115 | Berlin | Food and non-alcoholic beverages | 68.04 | 0.346 | 2540 | 107.7 |
| 2 | 10119 | Berlin | Food and non-alcoholic beverages | 48.22 | 0.245 | 2477 | 105 |
| 3 | 10435 | Berlin | Food and non-alcoholic beverages | 44.91 | 0.229 | 2549 | 108 |
| 4 | 10437 | Berlin | Food and non-alcoholic beverages | 77.4 | 0.394 | 2370 | 100.5 |
| 5 | 10439 | Berlin | Food and non-alcoholic beverages | 67.22 | 0.342 | 2309 | 97.9 |
| 6 | 10551 | Berlin | Food and non-alcoholic beverages | 41.06 | 0.209 | 2166 | 91.8 |
| 7 | 10553 | Berlin | Food and non-alcoholic beverages | 26.45 | 0.135 | 2240 | 94.9 |
| 8 | 10557 | Berlin | Food and non-alcoholic beverages | 42.93 | 0.218 | 2304 | 97.6 |
| 9 | 10559 | Berlin | Food and non-alcoholic beverages | 37.49 | 0.191 | 2235 | 94.7 |
| 10 | 10589 | Berlin | Food and non-alcoholic beverages | 34.64 | 0.176 | 2270 | 96.2 |
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Data Dictionary
| Column | Type | Label | Description |
|---|---|---|---|
| id | text | ||
| postcode | text | ||
| name | text | ||
| category | text | ||
| spend_mio_eur | text | ||
| spend_per_mio_country | text | ||
| spend_per_capita | text | ||
| spend_idx | text |