Measurence Scout: correlation between a location’s traffic and weather conditions

The goal of this solution is to improve Measurence Scout, a service currently operated by Measurence.

Scout is a location scouting solution that helps physical businesses make business data-driven decisions. Using its own WiFi technology, Measurence is collecting data about the traffic of people in and around phisical locations and provide analytical reports and business insights to the location managers. EW-Shopp adds an additional layer of information to this analytics: correlations with weather and events data.


  1. Add layers of intelligence to Measurence Scout (correlations with weather and events data)
  2. Build vertical specific products which enable clients to optimize their resources: sales promo activities; staff management; inventory & out-of-stock management; Integrate external datasets; In-depth analysis; Prediction
  3. Expand to new markets


We expect the enriched value proposition to help us improve our sales conversion rate and expand it on more locations (at least 50 locations; revenue increased by 10% per location).


  • Automotive
  • Retail
  • Out-of-home advertising
  • Commercial Real Estate


External weather and event’s data was successfully integrated to Measurence’s data flow. We have analysed correlations of people’s traffic and weather temperature, precipitation and cloudiness using historical data during 1 year for 5 shops in different parts of Italy.

We have built the prototipe of the dashboard that helps businesses allocate their advertising budget based on quantitative historical analysis of customer flows vs seasonality and weather trends. The feedback of the customers has been positive and gave us insights we’ll work on for the future release.


We found out that in Southern Italy, people prefer to visit the dealer locations during the rainiest days of the year rather than during extremely sunny days. However, in Northern Italy, we did not find any dependencies related to the number of visitors and precipitations.

We plan to investigate these dependencies in more details expanding our study to more location across Italy and adapt our prediction models based on those new learnings. Also, we found that internal marketing events have significant impact on the traffic increase. We plan to make prediction of people’s traffic for the next event.

RESEARCH & INNOVATION DOMAIN: Data integration, analytical and predictive modelling, user interactive interface including visualizations

TOOLS INVOLVED: ArangoDB, statistical and predictive modelling were realized in python, JavaScript library highcharts

CONTACT PERSONOlga Melnyk – Alessandro Prioni