Marketing Mixed Modeling

In many businesses, the marketing fund is allocated based on the marketing manager’s experience, departmental budget allocation rules and sometimes ‘gut feelings’ of business leaders. Those traditional ways of budget allocation yield sub optimal results and in many cases lead to money wasting on certain irrelevant marketing efforts. Market Mixed Models can be used to understand the effects of marketing activities and identify the key marketing efforts that drive the most sales among a group of competing marketing activities. The results can be used in marketing budget allocation and take out the guess work that typically goes into the budget allocation.

How do firms allocate their budget today?

How do they evaluate the effectiveness of those investments?

How do they plan to invest their limited marketing budget in the future?

How to ensure the highest return on the investments whiles building brand image and reputation?

Apply predictive analytics to guide the company’s marketing budget allocation by using Marketing Mix Modeling. MMM is the use of statistical and analytical methods to quantify the effectiveness of various marketing activities (marketing mix) and systematically optimizing marketing budgets and allocation resources into profitable marketing efforts.

Innovative Approaches to Measuring Advertising Effectiveness

A popular saying illustrating how difficult it was to reach potential customers using traditional advertising is attributed to John Wanamaker: "Half the money I spend on advertising is wasted; the trouble is I don't know which half."

Probably the most famous quote in the field of marketing is the apocryphal line attributed to John Wanamaker about the difficulty of assessing the impact of advertising spending. Here we are, roughly 100 years since that phrase was first popularized, and Wanamaker’s words continue to resonate with today’s marketing executives just as much as ever before. The development of customer tracking technologies, measurable media, sophisticated attribution models, and platforms that facilitate controlled experiments all promise great advances in our ability to make more precise statements about the effectiveness of advertising spending, but many lingering questions remain.

With this vexing backdrop in mind, the Wharton Customer Analytics Initiative and the Future of Advertising Program are joining forces to commission leading-edge research projects to develop state-of-the-art methods and insights in this important area. Specifically, we are looking to support high-quality, data-oriented research that will demonstrate just how far we’ve come since Wanamaker first expressed his frustrations with measuring advertising impact. We have in mind three broad sub-categories of research:

New modeling approaches that leverage customer-level data on behavioral responses to advertising
(2) Advances in experimental methods that can isolate and quantify different advertising effects
(3) Other innovative approaches that represent a significant step beyond traditional advertising effectiveness measurement methods