“Adopting Marketing Mix Modeling is a key step to better manage the complexity of a multi-brand dealership like Autotorino. This approach allows us to precisely analyze the impact of our investments, reducing uncertainty and helping the team focus on developing innovative and targeted solutions. A strategy that allows us to react quickly to the market and strengthen our leadership in the industry.”

Giacomo Nani – Head of Digital at Autotorino

The company

Autotorino, founded in 1965 in Morbegno, Italy, began as a single car dealership and has evolved over the decades to become Italy’s leading multibrand dealership.

In 2015, Autotorino became the number one dealership in the country and, in 2017, was the first Italian company to enter the ICDP Guide to Europe’s Biggest Dealer Group’s ranking of the 50 largest European Dealers.

Today, Autotorino operates 71 branches in six Italian regions: Lombardy, Piedmont, Emilia-Romagna, Veneto, Friuli Venezia Giulia and Lazio.The company employs about 2,750 people and has annual sales in excess of 2 billion euros, with more than 63,000 vehicles sold each year.

The challenge

Autotorino’s goal is to optimize its marketing mix to increase sales and improve return on investment (ROI). In implementing the Marketing Mix Model (MMM), the company was faced with two key challenges: attributing sales to online marketing channels-with an increasingly multi-touchpoint journey, it is difficult to correctly attribute sales to each channel. The second challenge is establishing the impact of external factors on sales, the automotive market being influenced by various events ranging from economic conditions to fuel prices, bonuses and government incentives, all of which need to be integrated into the MMM.

The approach

Autotorino is therefore faced with the challenge of correctly attributing sales to online channels, optimizing budget allocation across media, and adapting to external factors that influence consumer behavior. The Marketing Mix Modeling (MMM) approach, combined with the advanced capabilities of the Robyn framework, developed by META, offers a data-driven solution to these challenges. By leveraging these capabilities, Autotorino has been able to accurately attribute sales to various digital touchpoints by identifying the most effective channels in conversions. Integration of the Robyn framework improves attribution through Bayesian models and machine learning, capturing nonlinear interactions and seasonal patterns to optimize multi-channel strategy. On the budget allocation front, the MMM allowed Autotorino to test investment allocation scenarios, including optimizing the allocation between new and used cars. In addition, by incorporating external variables such as the economy, fuel prices, trends, and incentives, the MMM helps to understand how these factors affect sales, enabling more accurate forecasts and agile adjustments to strategies and providing Autotorino with predictive insight to respond quickly to market dynamics and maintain strategic agility.

Results

1. Using the model on historical data, the company’s strategic investment in digital marketing was validated; in fact, the MMM showed that as the budget increased, it corresponded to an increase in traffic at the dealerships.

2. Optimization of budget allocation: through the MMM and Robyn simulations, Autotorino achieved precise budget reallocations, reducing the cost per acquisition (CPA) for new and used car campaigns on Meta by 10%.

3. Validation of the ability to forecast lead generation trends by segment with the consequent ability to adapt strategies according to economic variations, adopting a market-responsive approach.