AI Analysis Model
The Customer Analysis Model is looking at interactions with the advertiser, such as visiting the website, engaging with marketing, and/or utilizing their vehicle wallet.
After observing and scoring these activities, AI will rank all the customers based on their scores. The higher a customer's percentile, the more engaged they are and likely closer to making a purchase.
Let's simplify this with a table:
| Percentile | UI label | Heat Bar |
|---|---|---|
| 0% | Build Awareness | 10% |
| 50% | Build Awareness + Loyalty | 30% |
| 85% | Near-Market | 50% |
| 95% | Active Browsing | 70% |
| 98% | Active Shopping | 90% |
| 100% | Ready to Buy | 100% |
This model helps us understand, not only, the customers' buying readiness, but our AI can better deploy automated household level marketing, and better meet their needs at every stage of the buying journey. It's like having a roadmap that guides our marketing activation with each customer.
Lead Acceleration
Customers can be accelerated by the AI and delivered directly into the CRM.
- Every single customer action and financial/vehicle data point in the platform is an input to drive the AI.
- Each customer is compared to every other customer for that dealership, and uses the above percentiles to score them every day.
- Directly observed actions, behaviors ,and steps taken (i.e. - like seeing customers back on the website, and email open rate) are weighted the most in the Machine Learning algorithm.
The outcome from the AI acceleration are the customers most likely to be in-market every month based on all the data consumed by the engine.
Customers can be manually sent to the connected CRM, read more on how HERE!