Redesign and Data insights
Redesign and data insights
- Native app designs
- New feature launch
- Native app designs
- New feature launch
DriveScore uses telematic tracking via a mobile phone’s sensors to accurately generate data about a persons driving behaviours, and subsequently converts this into a score that ‘good drivers’ can use to get discounted insurance premiums and other rewards and incentives.
- Overall audit and design refresh of the app, including updating UX and componentry.
- Creation of data driven insight reporting to promote greater user engagement and value in the app and the offer.
- Integration of insurance application and results cards.
- Car Hub admin management for tracking and managing MOT’s, Road Tax renewals etc
DriveScore uses telematic tracking via a mobile phone’s sensors to accurately generate data about a persons driving behaviours, and subsequently converts this into a score that ‘good drivers’ can use to get discounted insurance premiums and other rewards and incentives.
- Overall audit and design refresh of the app, including updating UX and componentry.
- Creation of data driven insight reporting to promote greater user engagement and value in the app and the offer.
- Integration of insurance application and results cards.
- Car Hub admin management for tracking and managing MOT’s, Road Tax renewals etc
A drivers' device uses machine learning to log all journeys it senses and will categorise them as is best understands. The 5 most recent drives sit on the dashboard, which link to the full journey list.
Non-driven trips (bus, train etc.) are displayed as a clipped version of a driven journey card. The visibility of these ‘non-drive’n trips can be toggled on and off to allow for easier scanning of drives, and identification of wrongly classified trips.
A user can easily reclassify can trip by selecting a different category from the trip classification listings.
The Trip Details page reveals comprehensive insights about the driving performance on this particular journey - aggregated across the 5 key sectors that contribute to a users Drive Score. ‘Events’ such as Speeding, or Harsh Acceleration will be shown here and linked to where and when on the journey map each one occurred and to what severity.
Further anecdotal drive data, such as the weather and location of the drive, to average speed and estimated fuel consumption are also shown on this page.
A drivers' device uses machine learning to log all journeys it senses and will categorise them as is best understands. The 5 most recent drives sit on the dashboard, which link to the full journey list.
Non-driven trips (bus, train etc.) are displayed as a clipped version of a driven journey card. The visibility of these ‘non-drive’n trips can be toggled on and off to allow for easier scanning of drives, and identification of wrongly classified trips.
A user can easily reclassify can trip by selecting a different category from the trip classification listings.
The Trip Details page reveals comprehensive insights about the driving performance on this particular journey - aggregated across the 5 key sectors that contribute to a users Drive Score. ‘Events’ such as Speeding, or Harsh Acceleration will be shown here and linked to where and when on the journey map each one occurred and to what severity.
Further anecdotal drive data, such as the weather and location of the drive, to average speed and estimated fuel consumption are also shown on this page.
A users score is based on their most recent maximum 365 days (1 calendar year) of driving - even if there are days with no drives recorded. The Insights section informs a user about their aggregated performance across all aspects of their driving over this period instead of just per drive.
The idea was again to create as much user engagement and almost gamification for the user to see how they (hopefully) have improved over time and incentivise them to keep driving well to get cheaper insurance.
This was done through their Score History over time, and their cumulation of Events and the patterns and frequencies of Events that their driving behaviour produces.
A users score is based on their most recent maximum 365 days (1 calendar year) of driving - even if there are days with no drives recorded. The Insights section informs a user about their aggregated performance across all aspects of their driving over this period instead of just per drive.
The idea was again to create as much user engagement and almost gamification for the user to see how they (hopefully) have improved over time and incentivise them to keep driving well to get cheaper insurance.
This was done through their Score History over time, and their cumulation of Events and the patterns and frequencies of Events that their driving behaviour produces.
Users can add their car (or multiple cars) to the Car Hub, and automatically have dates such as their MOT and Road Tax renewal populated from the DVLA database and create renewal reminders for them.
They can also add their insurance renewal date so that they can be reminded near their renewal time that they can use their Drive Score to potentially get a better price.
Users can add their car (or multiple cars) to the Car Hub, and automatically have dates such as their MOT and Road Tax renewal populated from the DVLA database and create renewal reminders for them.
They can also add their insurance renewal date so that they can be reminded near their renewal time that they can use their Drive Score to potentially get a better price.
Because initial DS users were Clearscore users and had to sign up for the app, we already had a good majority of a users personal info, so we could pre-populate a lot of insurer search data fields and in some instances bypass them altogether to streamline the search for cheaper insurance.
The search results cards can be toggled between annual and monthly amounts without having to resubmit and waste more of a users time.
Their Drivescore saving % is highlighted at the top of each result. Additional bolt-ons can be added to the overall deal at a users choosing - increasing the premium accordingly.
Because initial DS users were Clearscore users and had to sign up for the app, we already had a good majority of a users personal info, so we could pre-populate a lot of insurer search data fields and in some instances bypass them altogether to streamline the search for cheaper insurance.
The search results cards can be toggled between annual and monthly amounts without having to resubmit and waste more of a users time.
Their Drivescore saving % is highlighted at the top of each result. Additional bolt-ons can be added to the overall deal at a users choosing - increasing the premium accordingly.