What our customer say

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
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.”
Head of Business Intelligence
at Espresso House
Espresso House (EH)

Koro optimizes Marketing Mix Model and Influencer Measurement with Kuwala

KoRo is one of the biggest food startups in Germany selling superfoods.  KoRo started as an online store, but now also sells its products offline in 9,000 grocery stores across Europe. In Germany, for example, at Alnatura, Edeka and Rewe.

  • Transformation of unstructured influencer data
  • Building a marketing mix model for several key markets
  • Recommendation for budget allocations
“ We strongly professionalize our marketing analytics. Hence the collaboration with Kuwala, because we want to see how we can optimize our marketing mix.”
Florian Schwenkert

at Koro

Stadt Leipzig and Kuwala organizes a data workshop at the CCC-Congress

The city of Leipzig redefined its open data strategy towards mobility data. It was crucial to identify and leverage the status quo and create a workshop format for +50 developers at Europes biggest developer conference.

  • Data Audit of available open data of the city of Leipzig
  • Developing a workshop concept based on "Data Sprint"
  • Moderation of the Workshop and Endreporting for City of Leipzig
“ We thank for the very professional preparation and the unique experience at the CCC Congress. It was an important part in defining the open data strategy”
Interreg Project Lead
Stadt Leipzig

Projects Overview

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

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

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

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

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

Customized implementations of GeoSage

The dashboard empowers mobility providers to improve their services on the basis of AI-driven analytics and thus be able to react quickly and with certainty to the ever-changing demand and supply situation in large urban areas. The dashboard visualizes real-time information about supply and demand. We aggregated various external data-sets and predicted hotspot areas in cities

To gain better insights into customer behavior at the point of sale (POS), we have developed a dashboard that evaluates geographical data and social media information. The data can be combined with SKU data from an ERP system. This makes it possible to analyze activity and interest profiles at any location in a city. We were able to build a dashboard predicting passenger flows and recommendations for replenishment to improve the ERP system.

We analyzed over 3 Million images from social media through a computer vision algorithm and tracked the content down to a geo-location with a timestamp. Knowing what customers are preferable doing helps to anticipate event promotion plans, demand predictions, and content strategies.

Depending on location and time of day a taxi driver gets a driving recommendation, based on our predictions, where the highest probability to pick up a new passenger currently is. The information is communicated seemless on a mobilephone of the driver.