Inside Maven: The Strategy Engine’s methodology

The process begins by establishing a dealership’s true Local Market Area — the geography where the majority of its sales originate and where future sales growth potential is greatest.

Steps:

1. Surface raw sales data: The Strategy Engine pulls the dealership’s rolling 6-month vehicle sales registration data via Experian.
2. Filter out anomalies: To maintain accuracy, the model removes:
   • Zip codes averaging fewer than one sale per month over the 6-month period
   • Used-car, fleet, and auction transfer sales records
   • Outlying zip codes located more than 50 miles from the dealership
3. Trim low-return areas: The territory is narrowed to include only the zip codes collectively representing roughly 80% of the dealership’s total sales — where marketing impact will deliver the strongest returns.
4. Calculate market share: The model then determines both the dealership’s market share within their LMA and the brand’s regional market share within the DMA.

Once the LMA is established, the Strategy Engine sizes the opportunity by combining multiple data sources to understand the audience and potential demand.

Inputs include:

• Total population: Derived from U.S. Census data
• Streaming population: Based on media consumption data
• In-market and pre-market car shoppers: Identified using automotive intent data
• Brand and dealer performance: Benchmarked to calculate expected sales and growth potential

This step defines the total addressable market and sets the foundation for precise media planning.

The Strategy Engine:

1. Applies Maven’s execution and strategy frameworks, modeled from over 75 million historical walk-ins, website visits, and vehicle sales generated from past streaming campaigns.
2. Calculates the precise media budget required to reach the identified audience at the optimal frequency.
3. Produces detailed guidance — including media mix, audience targeting, inventory sources, and geotargeting.
4. Projects reach, frequency, and market share growth using historical performance modeling, allowing users to adjust spend with an interactive budget slider to see potential outcomes.

The result is a transparent, data-backed strategy designed to show what it takes to win, and what happens when you do.