Measuring the Incremental Impact of marketing on sales through Marketing Mix Modeling
Founded in the late 1980s, Pinko is an Italian fashion brand celebrating women’s independence and femininity through a contemporary and distinctive style.
With a mix of creativity and innovative design, Pinko has become a global symbol of personality and versatility.
-
ClientPinko
-
SectorFashion Luxury
-
Areas of ExpertiseMartech & Adtech, Attribution & Marketing Mix Modeling, Consulting
1. Goals
By investing in marketing activities across multiple touchpoints – both online and offline – Pinko wanted to understand the true impact of each channel on overall sales results. In particular, it needed a clear and integrated overview of performance to drive business decisions for greater efficiency.
The brand identified three priority areas that needed to be addressed:
- Understand the impact of brand awareness initiatives on sales: determine how activities aimed at increasing brand awareness were translating into measurable economic results.
- Analyze the role of different marketing channels: understand how campaigns distributed across digital (social media, display, search) and traditional (TV, print) channels contributed to sales in both ecommerce and physical stores.
- Assess the influence of external factors: identify and measure the impact of elements such as seasonality, promotions, local events, or the launch of new collections on overall performance, and distinguish them from the impact of media activities.
These challenges required a robust but flexible analytical methodology that could guide the brand toward a more strategic use of marketing resources.
2. Our Approach
To address these analysis needs, we partnered with Pinko to implement the Marketing Mix Model (MMM) as an advanced tool that uses data to define the impact of different marketing levers.
Marketing Mix Modeling (MMM)
The MMM was developed using advertising investment history, omnichannel sales metrics, and other relevant data provided by Pinko. The model allows you to:
- Break down the combined effect of channels: quantify the specific contribution of each channel (e.g. social media vs. TV campaigns).
- Incorporate external variables: integrate data from seasonal promotions, events, and other non-media drivers into the model for a complete view of sales.
- Evaluate future scenarios: simulate the impact of different budget allocations to optimize spending.
GeoLift to measure incremental impact
A GeoLift test was conducted to enrich the analysis, designed to isolate the incremental impact of awareness activities by comparing regions exposed to the campaign with unexposed control areas.
Through GeoLift, we have:
- Measured sales lift: this allowed us to gain insight into how exposure to the campaign could affect results in different geographic areas.
- Evaluated channel differences: we used the test results to complement statistical analysis and provide additional insight into the performance of different touchpoints.
By combining the statistical model with incremental testing, it was possible to provide the brand with a more nuanced view of marketing performance, supporting decision-making with useful insights for the future.
3. Key Wins
This approach has led to significant improvements in the brand’s ability to manage its marketing resources and achieve its growth objectives. Specifically, we were able to:
- Accurately measure the ROI of brand awareness campaigns: we have identified the specific impact of each activity across all channels (omnichannel).
- Optimize budget allocation: the insights we gained allowed us to better align investments with business priorities, improving the overall profitability of campaigns.
- Generate actionable insights: for example, we were able to identify the channels that perform best at certain stages of the customer journey and to optimize the mix to increase their impact.
The results also enabled Pinko to strengthen internal decision-making, making it more data-driven and focused on sustainable growth. The integrated use of MMM and GeoLift provided an internal benchmark, paving the way for future targeted investments and more effective monitoring of marketing performance.
