Digital transformation is a key buzzphrase nowadays, and it has become a major topic for many businesses - but what is “digital transformation”? Put simply, it’s the process in which business can change the way they stay connected to their customers, using new and dynamic ways of analyzing and communicating with an always connected consumer. Organizations are using traditional spatial data assets in exciting new ways to support this digital transformation.
One area this has exploded in recent years is the analysis of location data from mobile devices (coordinates of the device’s geographic location), which fuels a wide range of activities that are driving digital transformation.
Consumers are increasingly using mobile devices for a wide range of activities, which generate a vast quantity of data, including their location. This means that there’s a whole new stream of consumer data that needs to be cleaned, referenced, analyzed and acted upon.
Many businesses are now handling, processing and analyzing exponentially growing volumes of data from mobile devices using big data environments. When analyzing location data from mobile data, three key spatial reference datasets are required. These datasets have been used by GIS practitioners for many years but are finding new favor with the growing army of data scientists:
- Point of Interest (POI) data, traditionally used for local search and retail location analysis.
- Postcode and administrative boundaries traditionally used to visualize and analyze market data.
- Demographics, traditionally used to create target markets for direct marketing and to analyze retail performance, measure market penetration and market share.
All three datasets have been transformed to be used in big data environments to allow large scale processing and analysis of mobile location data to create:
- Timely and relevant messaging, marketing and advertising to the individual based on their location.
- Customer profiles to inform next best action, upsell/cross-sell opportunities, personalized offers and retention marketing.
- Audience profiles to identify target consumer markets for marketing and advertising campaigns.
Harnessing location data from mobile applications and analyzing this with location reference data allows retailers to give a personal shopper experience. A store location can be used to:
- Create a geofence so offers and notifications can be tailored to the individual entering the store.
- Combine journey start and end points with points of interest data, boundaries and geo-demographics. This generates profiles of existing customers and enables target marketing towards similar populations.
- Provide accurate marketing messages to the areas where a consumer works, lives and plays.
This analysis allows many businesses to support their digital transformation from traditional, physical messaging to dynamic digital marketing and advertising - providing a truly personalized experience that takes into account the individual, where they are and when they are there.