The six myths of digital marketing measurement

  How you measure digital activity radically affects the strategy and effectiveness of what you do. Leonie Gates-Sumner, client director, Millward Brown explains how myths about digital measurement can distort brand strategy. There is a phrase: “What gets measured gets done”. It’s true in many areas of marketing but in digital the scope for measurement(…)


Some Thoughts on Digital Attribution Models

Digital advertising has a distinct advantage over traditional forms of advertising in that it can monitor the ads to which customers respond, as well as draw direct connections between specific ads and actual purchases. Digital marketing seduces the advertiser who believes in data driven decision making. While digital inherently has a data advantage over other forms(…)


Digital Metrics Delirium: How to Drive the Results That Actually Matter

The world demands accountability, so marketers feel compelled to measure everything. But measurement technology is dramatically imperfect. The result is that, more often than not, we measure what’s easy to measure instead of what’s right to measure. Digital appears to be the “promised land” because its count-ability gives us the accountability we crave. The truth,(…)


What Works Best For Measuring Marketing Impact? Top-Down, Bottom-Up Or Sideways?

The discipline of marketing science arose from marketers’ number one need: to know what advertising efforts work. What are the forces that drive sales up or down, and by how much?

To answer these questions, marketers need data. In yesterday’s world of mass media broadcast advertising, aggregated data was all that marketers had to make data-driven decisions.

It was enough for marketing scientists to develop sophisticated econometrics models to fuel an entire discipline known as marketing or media mix modeling (MMM).

Marketing (Or Media) Mix Modeling (MMM)

These models use historical information to analyze the incremental impact of various marketing efforts on sales. They are complicated statistical models aimed at creating a regression-based relationship between the marketing activities and sales results, analyzing the contribution of each piece of the puzzle as a percentage of the total results to determine the effect that channel had.

Illustration of MMM from McKinsey report (PDF) Click to enlarge.

MMM models, which were originally developed in the late 1980s, are still used today — especially by big spenders like consumer packaged goods (CPG) marketers — to deliver powerful high-level insights to the forces that drive sales.

Some of these drivers are media channels that marketers can influence, such as TV advertising; others are completely external and independent, such as weather.

MMM insights are typically used to do strategic scenario planning, set annual budgets and optimize the marketing mix at a high level.

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