The digital footprint that people leave behind when they use their mobile phones for voice, sms and data transactions creates a pattern of behavior related to location and time. This digital footprint is used to derive data-driven insights for mobility.
Talas created a bespoke, one time study based on a spatio-temporal analytic methodology with the objective of analyzing subscriber’s behaviour patterns using Talas’ Big Data platform, Talino. The study is delivered through a visualization tool to better assess patterns pertaining to consumer behavior.
Data Attributes we will attempt to cover in the study may include:
- Travel Path
- Pedestrian Traffic
- Average Income
- Median Income
Mobility patterns allow an organization to create customer profiles that use movement between locations as a factor of affinity among a group of customers. It includes trails that illustrate the path through which subscribers habitually travel. These patterns are discovered through analytic methods such as displacement time series. The client could then infer future behaviors of their customers from these mobility patterns.
Footfall analytics give a deeper insight on the number and movement of people at a certain area for a given period of time. Having this kind of information enables the client to have deeper understanding to help enhance decision-making and action planning. Clients will be able to tell the foot traffic within a certain area at a given time and they can infer potential customer behaviors from this and act accordingly.