Automotive Audience Segments
We’ve predicted the likelihood of motor purchase preferences of Australian households for specific types of new and used vehicles (e.g Small SUV), specific makes (e.g Hyundai) and a combination of both (e.g Hyundai Small SUV).
12x more effective targeting
We’ve built a series of audience segments that achieve on average 12x improved targeting effectiveness at the top centile of our models. So how do we do it?
- We compliantly source automotive transaction data from a data partner that describe the characteristics of actual purchasing households. It covers 50% of cars purchased in the last 5 years.
- Our analytics team use that purchase data, combined with our data universe to build propensity models covering over 240 unique new and used vehicle types and makes, that indicate the most predictive characteristics of purchase behaviour. All 12m households are then scored High, Medium or Low for each vehicle, with the highest lift in the top centiles of our models.
- Segments are onboarded into any internal or external environment with zero reliance on cookies. We have relationships and connections with most ad tech platforms, including DMPs, DSPs, publishers and trading desks.
12x more effective targeting
We’ve built a series of audience segments that achieve on average 12x improved targeting effectiveness at the top centile of our models. So how do we do it?
- We compliantly source automotive transaction data from a data partner that describe the characteristics of actual purchasing households. It covers 50% of cars purchased in the last 5 years.
- Our analytics team use that purchase data, combined with our data universe to build propensity models covering over 240 unique new and used vehicle types and makes, that indicate the most predictive characteristics of purchase behaviour. All 12m households are then scored High, Medium or Low for each vehicle, with the highest lift in the top centiles of our models.
- Segments are onboarded into any internal or external environment with zero reliance on cookies. We have relationships and connections with most ad tech platforms, including DMPs, DSPs, publishers and trading desks.
242 propensity driven audience segments
Car Type
- Caravan
- Caravan (Used)
- Electric Vehicle – Passenger
- …
- Light Commercial
- Light Micro Passenger
- Light Micro Passenger (Luxury)
- …
- Small Passenger (Used)
- Small SUV
- Small SUV (Used)
Car Make
- Alfa Romeo
- Aston Martin
- Audi
- …
- Jaguar
- Jeep
- Kia
- …
- Volkswagen
- Volvo
- Yamaha
Car Make & Type
- Audi – Light Micro Passenger
- Audi – Medium Passenger
- Audi – Small Passenger
- …
- Mazda – Light Commercial
- Mazda – Medium Passenger
- Mazda – Medium SUV
- …
- Volvo – Medium SUV
- Volvo – Small Passenger
- Volvo – Small SUV
Automotive Audiences validated with genuine purchase data
smrtr’s propensity models identify households with an increased likelihood to purchase over 240 specific types of new and used vehicles (e.g small SUV), specific makes (eg. Hyundai) and a combination of both (eg. Hyundai Small SUV).
The models are scientifically validated against actual purchase data we receive as an ongoing supply. In the Mercedes Benz example, the actual and predicted lift over random (baseline) is highly correlated. So the models do what they are supposed to!
Use cases
The possibilities are endless, but the most common applications for this data asset include:
Car manufacturers: can target buyers using predictive analytics down to an individual make and model.
Finance & Insurance companies: can target car finance and insurance prospects using a combination of demographics, income, and vehicle preference.
Media: can extend their targeting capability and offer much richer insight to their audience.
Automotive Audiences validated with genuine purchase data
smrtr’s propensity models identify households with an increased likelihood to purchase over 240 specific types of new and used vehicles (e.g small SUV), specific makes (eg. Hyundai) and a combination of both (eg. Hyundai Small SUV).
The models are scientifically validated against actual purchase data we receive as an ongoing supply. In the Mercedes Benz example, the actual and predicted lift over random (baseline) is highly correlated. So the models do what they are supposed to!
Use cases
The possibilities are endless, but the most common applications for this data asset include:
Car manufacturers: can target buyers using predictive analytics down to an individual make and model.
Finance & Insurance companies: can target car finance and insurance prospects using a combination of demographics, income, and vehicle preference.
Media: can extend their targeting capability and offer much richer insight to their audience.
Got Questions?
We have all the answers! Well, maybe not all but we’ll do our best to help.
