Increasing Sales through Optimized Media Mix


A luxury appliance manufacturer spent a substantial amount in media to reach potential consumers, drive them to the website and showrooms, and, ultimately, convert them into customers.

We worked closely with the brand and media teams to address the key business questions and inform optimization decisions regarding media investments:

  • What is the optimal media mix and spend for the next one to two years?
  • Which channels and tactics are most likely to lead to higher sales?
  • Can we project impact on KPIs given various media investment scenarios?


Because the manufacturer doesn’t sell directly to consumers, there is a lack of visibility surrounding detailed sales data.

Moreover, the long sales cycle for the high-end products necessitates a thoughtful approach to sales measurement, especially when trying to understand its relationship with media activity.

Several metrics were analyzed to determine the best fit as a proxy for sales. We decided that shipment data was the most accurate measurement of demand and sales. Additionally, we were able to use our analysis from a quantitative survey focused on the lead-to-conversion timing to help define the average sales cycle, which was used in the modeling efforts.


Since 2020, when our initial analyses uncovered major gaps in the mobile experience, we have worked closely with the nonprofit and its partners to directly implement and influence changes to the mobile experience.

To understand the impact of marketing on sales, we used Media Lever Evaluation, an agent-based model complemented by time-series modeling. Using a large array of data, including product shipments, consumer perceptions and preferences, media investments, and competitive and economic data, the model simulated consumer behaviors and marketing touchpoints in a dynamic marketplace to forecast future outcomes.

The Media Lever Evaluation model not only allowed us to quantify the historical impact of media on product purchase but also enabled stakeholders to build strategic what-if scenarios using budgets and specific marketing strategies to evaluate and benchmark potential returns. This ultimately enabled scenario optimization by projecting sales and comparing the most optimal decisions based on the range of potential return.


The Media Lever Evaluation results showed that 24% of unaided awareness and 15% of product shipments were attributed to paid media activities, which was a critical piece of information in supporting future media proposals.

Furthermore, the forecasted scenarios uncovered a complex dynamic between spend and media mix. With these learnings, the media team made strategic decisions to optimize the media mix and reallocate funds to best meet business objectives. Additionally, the team leveraged these insights to deliver a $1,000,000 media reinvestment recommendation and established a benchmark for product purchase based on marketing spend.