The best Location Dataset for Location Decisions

Location Data gathered, cleaned and anonymized

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The most granular location dataset

Get any information about a place, up to date

  • Place information cross-validated with Google and OSM
  • Understand XX
  • XXXX
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A simple node on the Kuwala No-Code Environment

Fit the data to your use case with ease...

Improve your Grocery Delivery Business with external and internal data

visualization of chicago POIs
  • Implement demographics, POI, Google Trends and popular times data
  • Update data for your model globally
  • Inform the inventory, drivers and warehouse strategy
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Detect Food, Meal and Ingredients Trends through Google Search

visualization of chicago POIs
  • Identify Content Fields relevant to your brand
  • Compete and Benchmark your Brand Relevance
  • Go deeper into consumer psychography
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Build a Marketing Mix Model like Zalando with a few clicks

visualization of chicago POIs
  • Optimize your budget allocation across offline and online marketing channels
  • Model your brand effect and impact of trends across markets
  • Integrated seamlessly with your marketing data sources for automated updates
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Deploy a Uber like demand and supply system

visualization of chicago POIs
  • Identify the perfect service areas, optimize vehicle availability and recharging times
  • Integrate clean, external datasets such as demographics data, Google Trends, POI data, and many more
  • Visualize and automate your reporting for your operations team in your workflow tools
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Bring your location-based business to the next level

visualization of chicago POIs
  • Identify new store and warehouse locations with regards to your competition and market potential
  • Merge your consumer, location and competition data in one place
  • Model different scenarios and the impact on your sales
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Espresso House builds a next gen location analytics system with Kuwala

Espresso House is a coffee shop brand with over 400 shops in the Nordics. They used Kuwala to merge internal and external data sources in order to train a model that informs the the expansion team of market potentials.

  • Standardization of multiple data sources providing POI and behavioral insights data
  • Multi-layer data aggregation
  • Frequent data update in order to keep the prediction model up to date
Interested? talk to us
Stephanie Head of BI at Espresso House
“ Instead of only using empirical observations, we now take into account data from various sources in order to take data-driven decisions.”
Stephanie
Head of Business Intelligence
at Espresso House
Espresso House (EH)

Community

Free
  • All transformations
  • Install via GitHub
  • Support of the Kuwala community
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Cloud

Monthly Subscription
  • Collaborate with multiple users on projects
  • Pipeline management
  • Hosted and maintained version with additional tech support
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Custom

Custom Pricing
  • Solutions engineering
  • Prioritized integration requests
  • Custom onboarding and training sessions
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