With Measurement It’s all About the Math

. August 15, 2017

Marketing and math haven’t always meshed. Marketing was long considered a creative endeavor somehow divorced from the rigor and transparency of science-based business process. The very definition of analytics – “the scientific process of transforming data into insight for making better decisions” – caused tension in marketing-dom.

But no longer. Better analytic methods, new data-driven technologies and C-suites calling for greater marketing accountability have changed all that.

What’s sometimes lost in the rush to install marketing measurement is this: The cornerstone of successful marketing analytics is the math behind it. If you don’t have the math right, by definition your attribution will be wrong, and by extension your allocations and attempts to optimize your investments will also be wrong.

Good math is the cornerstone of good marketing analytics.

Effective marketing resource allocation depends on accurately attributing revenue to different marketing investments online as well as offline and at point of purchase.

Early versions of marketing analytics included traditional forms of measurement that we’ve had for decades, such as media mix models, agent-based models, digital attribution and simple correlations using Excel spreadsheets. Today’s more advanced analytics tap rely on predictive analytics and other marketing science to help companies reallocate billions of advertising dollars while realizing double-digit sales lifts with zero additional spend.

Advanced analytics in marketing can hone in on hundreds of a given company’s business drivers, from pricing, distribution and online reviews, to social media chatter, advertising and hard sales data to uncover critical insights about what’s really driving results, and what to do next in the real world. The allocation step is where you put what you’ve learned from attribution and testing into play. Then you can quickly measure outcomes, validate models by running real-time tests, and make course corrections to optimize allocations and results.

Spotting ‘Quant Quackery’

Surprisingly, some brands are still using largely discredited simple marketing mix econometric methods, or for online marketing “last click attribution.” Such models aren’t looking at the total ecosystem, nor are they measuring the precise impact of, say, TV on search, or search’s impact on retail sales. Simply plugging offline spend into digital marketing analytics models doesn’t achieve “cross-channel” analytics.

Using flawed models is like crediting a single movie theater for an Academy Award winning performance, or trying to win a football game with just eight players on the field. Unfortunately, while the measurement buzzword “attribution” is everywhere these days, many of the solutions trying to solve for this challenge are sub-par. Yet models that play without all the pieces are little more than what we might call quantitative quackery.

For example, to get a true, holistic view of what’s going on and thus make better business decisions, you need all forms of digital data (search, social, mobile, etc.) in the analytics. Without it you’re missing a rich vein of information about consumer behavior. And remember, even if you spend a large part of your budget offline, that doesn’t mean online behavior – the consumer’s “digital life” – isn’t influencing the decision-making process.

Relying heavily on old-school data “samples” is another problem.

Samples may still have a place, but part of big data’s beauty is the ability to use all the data from online and offline marketing and sales channels, plus external factors (such as the weather or unemployment), not just samples that are far more error-prone.

Still more quant quackery occurs when marketing analytics focus on attribution for only a small piece of the overall enterprise. To achieve accurate results you have to factor in enterprise-wide relationships. Think of it an advanced form of the old connect-the-dots exercise, only in this version you have to include dots for activities and outcomes you might not be able to actually see, but which exist nonetheless in the data.

Quantifying Marketing’s Business Impact

The right math behind the analytics is essential to bringing data to life for marketing organizations, thus allowing for faster insights and better decision-making. This includes such things as:

  • Quantifying the long-term impact of brand advertising (brand equity).
  • A holistic approach that includes all online and offline methods and channels.
  • Deploying the latest technology, not simple regression models.
  • Transformational thinking that takes marketing analytics beyond simple “research project” status toward enterprise-wide adoption.

Consider a major auto industry player that’s a superstar in the world of marketing analytics. This company’s cross-functional analytics team has the daunting task of making sure the company spends its $1+ billion marketing budget as effectively as possible while contributing to business goals, achieving the best ROI and increasing shareholder value.

They do it with advanced analytics that allow the company to run continuous marketing strategy simulations under a wide range of complex variations. These simulations employ cross-media attribution insights that help the company predict with greater accuracy than ever how changing the amount spent in one marketing area will likely impact the performance of advertising elsewhere, and what this all does for the bottom line.

Using advanced analytics, this giant global marketer has also been able to coordinate local and national marketing and dealer incentive budgets, and simply by shifting allocations generate tens of millions of dollars in new revenue from the same spending level.

Almost any company can deploy more advanced analytics by focusing on methods that avoid the above pitfalls. But one thing is sure: Businesses that don’t will be left behind.

An earlier version of this article first appeared in Analytics Magazine. 

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Category: Adoption, Articles, How-To, Impact, Research, Strategy

About the Author ()

Daniel Kehrer is Executive Editor of the ANA Data Analytics Center (DAC), a leading voice of thought leadership and education in marketing measurement, data and analytics. He is also the Founder of BizBest Media Corp. and previously headed marketing at MarketShare LLC, an advanced marketing analytics technology company.

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