Australia’s motor vehicle landscape is undergoing important shifts, as the rise of EV’s continues alongside the likes of SUVs and Australian classics such as Holden exit the market. This highlights the need for up to date and accurate data on consumers preference for their next vehicle purchase. To help answer questions on this, RDA is happy to announce its new data pack of Motor Vehicle propensity variables, which show the profound effect that local area population characteristics have on the type of vehicle chosen. The data pack contains 176 variables across 5 categories, including…
Motor Vehicle Class (10 variables), for example:
- Hatch
- Sedan
- SUV – Large
- Ute
Motor Vehicle Type (6 variables), for example:
- EV
- Caravan
- Luxury
- Hybrid
Motor Vehicle Makes (34 variables), for example:
- Audi
- Tesla
- Hyundai
- Harley-Davidson
Motor Vehicle Series (12 variables), for example:
- BMW 5 Series
- Lexus NX Series
- Mazda CX Series
- Mercedes-Benz C Class
Motor Vehicle Models (115 variables), for example:
- Jeep Wrangler
- Kia Cerato
- Porsche Cayenne
- Toyota Landcruiser
You can see the full data dictionary for this dataset here.
Where does the data come from?
Our Motor Vehicles (2022) Dataset is based on the new Motor Vehicle Census published by BITRE late last year, which is the replacement to the retired Motor Vehicle Census that the Australian Bureau of Statistics was previously publishing.
We apply advanced statistical techniques and machine learning to bring the data from its native postcode granularity (of which there are ~2,500 areas in Australia) to the fine grained SA1 level (of which there are ~60,000 areas in Australia). In doing this, we transform the original data from its historical view to a more forward-looking view of the propensity to buy new vehicles based on the characteristics of households at SA1 level.
What Can I Do With it?
The Motor Vehicles (2022) data supports a wide variety of use cases in the geoTribes Explorer, including…
Improved Audience Targeting
Target directly as geospatial audiences within OOH coverage areas or postcodes and find the locations with the highest propensity to buy a new vehicle of a particular make or model type. Often used by Media Agencies & OOH Media Owners to build better targeting strategies in response to media briefs and campaigns.
Strategic Retail Planning
Understand the types of customers that live near retail sites to better inform a retail network strategy for the future. Often used by Car Dealerships, Service & charging stations or Automotive Retail stores.
Competitor Targeting
Identify battlegrounds where two automotive brands have the highest competitive intensity and understand the people who live in them to create a more nuanced view of the market. Often used by automotive manufacturers and agencies to better shape their marketing strategy.
If you have any questions about using Motor Vehicles (2022) data in your market planning, contact the friendly RDA Technical Team on +61 2 8923 6600 or send an email to info@rdaresearch.com