Mix models are fundamentally flawed because of data deficiency
Incomplete and vague data is difficult to analyze using traditional media mix models. And the insights are often of questionable reliability.
Incomplete and vague data is difficult to analyze using traditional media mix models. And the insights are often of questionable reliability.
Successful TV campaigns build trial, stimulate repeat purchase, and maintain healthy consumer brand perceptions.
Ignoring this long-term effect favors corrosive price promotion over brand-building advertising.
Traditional market mix modeling approaches provide aggregated insights about marketing activities but do not address granular business questions.
Understanding optimization opportunities across customer journeys
Optimizing marketing efforts
Predictions and recommendations
Helped a technology giant in analyzing the causal impact of marketing spend across a variety of channels. Optimized sales by generating a media mix that maximized sales within our client’s initial marketing budget.
Helped a large online platform company optimize digital spend ratios to increase brand perception.
Generate granular insights to answer business questions such as:
– What is the impact of factors related to our operations vs. factors related to the external environment (e.g. weather, macroeconomic, competition)?
– What is the appropriate cross-media attribution and effect (e.g. To what extent does TV advertising drive Search)?
Analyze the impact of media on sales in context of underlying factors to understand:
– Saturation and diminishing returns from media spend
– Sales decomposition with an evolving baseline
Quantify indirect effects to evaluate the true return on marketing investment. Arrive at an optimal strategic balance between those effects by:
– Identifying potentially important consumer beliefs or attitudes toward the brand
– Evaluating the contribution of brand attitudes to long-run brand demand
– Linking marketing investments to an underlying sales baseline