We collect behavioral data and process it. Amodo data gateway is source agnostic and collects a sea of information from a number of sources: Amodo white-labeled App, Amodo SDK, IoT devices, 3rd party platforms, wearables of all kinds. It all pours in via a South-Bound API and ends up in a Protobuf scheme as a collection of events in a serialized form, ready to be analyzed.
Regardless if the data source is a smartphone, an OBD dongle, or a preconnected car; raw GPS, Accelerometer, and Gyro data is processed and analysed to identify mode of transportation, vehicle speed and driving style, and in the case of a driver, their distraction levels while driving.
Analysed data is then placed in geographical, time, and weather contextual layers. This makes it ready for further processing, scoring, or usage in engagement campaigns.
By motivating users through our Engage modules to connect their social media accounts, Amodo platform is able to create a comprehensive user profiles that go beyond standard mobility and fitness analytics. Image and text analytics provide previously unattainable automatic insights about users habits.
Do they eat healthy food regularly and are they engaged in social activities, are just some of the insights gathered based on this unstructured data.
Fitness and lifestyle data is acquired either using our Mobility and Lifestyle SDK or directly from 3rd party platforms. Information such as activity level and type, as well as number of steps and sleep patterns becomes available for further analysis and customer segmentation.
Acquired data is enriched by various proprietary and 3rd party data layers. Information such as current weather, road types, accident risk zones, school areas, and other areas, or times, of interest are used to augment the value of input data and provide a more comprehensive data output for the other three Amodo modules.
Over time, analysed input data is crowdsourced and anonymised to create additional custom data layers, such as common accident areas, or roads on which drivers are most distracted.
Data we gather is given context through augmentation with other sources. It’s a cross comparison of the sensor data with 3rd party data sources: road infrastructure, type of settlements, weather, Amodo proprietary map data, insurance claims data and other proprietary and custom data layers. Contextualization is key. It drives insights. And those insights create the real business value.
Of course, data we analyze must be reliable. It is of utmost importance to have the data you can trust. We have mechanisms in place that will recognize if a user attempted an unauthorized actions to temper with the data, gaming the system just won’t work. That includes data simulations, account manipulations and other best practices in securing high data integrity.