Understand city dynamics

Enables companies to unlock pockets of growth and generate significant economic value at the neighborhood level.

Cloud delivered cognitive insights

Ingests and curates high volumes of data and deploys advanced algorithms for targeted business actions

Industry and role based front end

Delivers intuitive actions across devices, including mobile, with seamless integration to business systems.

How it works

Every city is unique with its own highly localized collection of people, places, events – and data. The data is everywhere, including social media, the news, weather and more. IBM MetroPulse helps you transform this data into actionable insight to unlock new opportunities. It is a cloud-based service that applies cognitive computing to hyperlocalized data sets to harness the pulse of a neighborhood. Companies can dynamically respond to consumer demand, such as making sure that popular flavors of ice cream stay in stock or that batteries are shipped to areas anticipating severe weather.

Industry use cases


Banks can predict when certain ATM locations will be in higher demand by pairing customer traffic patterns with daily events that might draw additional traffic.

Consumer Products

Bottlers can identify optimal placements for vending machines, product mix recommendations and dynamic pricing based on hyperlocal external forces such as weather and local events.


Insurers can send customers severe weather alerts to proactively prevent damages and claims from happening and continue communicating after severe weather events.


Retailers can monitor real-time social sentiment across store locations, act early to identify trends and have their supply chain automatically respond and avoid out of stock situations.

Telecommunications & Media

Wireless providers can identify where network coverage needs improvement based on regular demand combined with social sentiment, weather and special events.

Travel & Transportation

Hotel chains can evaluate requests for certain in-hotel services and price accordingly based on proximity, availability of local resources and forecasted demand.

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