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Best Practices in Modeling and Forecasting Marketing’s Net Effects

Stewart Pearson

Partner, Optimal Net Effect (O.N.E™.)

Why Forecast Marketing?

Marketing, once the least quantifiable business function, now has more data than any other.   Yet marketers are challenged to measure their impact on business performance and growth.

According to Forbes, “78 percent of CMOs feel the inability to communicate, quantify and optimize the value of marketing hurts them professionally.”

With marketing’s shift to digital, marketing analytics became bifurcated into Market Mix Modeling (MMM) and Multi-Touch Attribution (MTA).  Both types of solution use different data sets and methodologies, and may deliver different results.  MMM has fallen out of favor:  slow to develop, backward-looking, consulting-led, and at too high a level to be actionable.  MTA operates in real-time but analyzes tactics and focuses on short-term.  The priority most companies now assign to attribution may be fueling corporate America’s neglect of brand and emphasis on content and promotion that drives clicks rather than brand value.

The answer is a solution at a level of aggregation between portfolio and click where decision-maker responsibilities are grounded. The intelligence will have value for decision-makers when it is at a level of aggregation that is actionable and forecastable.

CMO’s may be concerned about the challenge of quantifying and optimizing. But the real challenge is to forecast.  When marketing was creatively-driven and a function separate from operations and commerce there was little expectation that forecasts would be reliable. Now marketing is a science and primary force generating the consumer demand that drives sales, operations and growth.   Only with the ability to plan scenarios and forecast their outcomes with accuracy can marketers guide their companies to growth.

Can a Marketing Forecast be Accurate?

There is a perception in Marketing that we are in a period of wrenching change and volatility.   As eMarketer recently reported, this is not the case.  Consumers are responding to changes in technology, market structures and media, but their responses are evolutionary and the impact is forecastable.

Smartphone penetration is now well-established, and the data is plentiful.  For example, we have extensive experience of tracking the impact of smartphones on buyer behavior and the shift from retail to online.  Facebook penetration has been inching up rather than exploding as in earlier years, and – whatever the outcome of the controversies regarding its user data – usage of its service is unlikely to change immediately.  When a new trend sets in, we will be able to forecast its impact.

As eMarketer notes, “there may be a lingering perception that advertisers—stuck in their old ways—spend their money in ways far out of sync with how today’s consumers spend time with media. But that notion no longer holds up as a broad generalization. Indeed, in their eagerness to reach consumers via mobile devices, advertisers now spend an outsize share of their money there.”  And while mobile video are the fastest growing media, we can map their trajectory, optimize for their impact of sales, and forecast that impact into the future.

What matters is not the amount of time spent on individual media relative to their budget allocation, but the total effect of and the interactions within the marketing investment portfolio.  We can increasingly see these effects become clear with the impact of new media on existing channels.  For example, multiplatform television (Hulu, Netflix, You Tube, Watch ESPN) and radio (Sirius XM, Pandora, iHeart) significantly increase the effectiveness of Display Advertising.

To evaluate the forecasting challenge, Marketers can study the real-world research and experience of Philip Tetlock and Dan Gardner in SuperForecasting: The Art and Science of Prediction

What makes a “SuperForecaster”?

Tetlock and Gardner note that one of science’s great accomplishments has been to show that uncertainty is an ineradicable element of reality.  Marketers may know that instinctively.

In SuperForecasting Tetlock and Gardner study the track records of individuals and organizations with accurate histories of forecasting outcomes in diverse walks of life and business sectors.  They uncovered how human judgement and a distinct mindset enable individuals and teams to become ‘SuperForecasters’. They identify four common traits.

  1. Super Forecasters base decisions on data as opposed to instinct or even experience
  2. Super Forecasters are committed to updating their forecasts continuously based on the latest data.
  3. SuperForecasters viewed their initial forecast as a starting point, a ‘hypothesis to be tested, not a treasure to be guarded’.
  4. SuperForecasters have a learning and growth mindset.

“Only people with a growth mindset paid close attention to information that could stretch their knowledge. Only for them was learning a priority,” they write.

Tetlock and Gardner also confirm the critical factor is how vast volumes of data and algorithms have enabled increasingly accurate forecasting.

”In most cases statistical algorithms beat subjective judgment, and in the handful of studies where they don’t, they usually tie. Given that algorithms are quick and cheap, unlike subjective judgment, a tie supports using the algorithm. The point is now indisputable: when you have a well-validated statistical algorithm, use it.”

Effective Marketing Is Not About Luck

O.N.E.’s Growth Modeling framework generates Marketing Effectiveness Statistics (MES™) developed by Dr. Roy Ralston PhD in 1999 to link Media to Brand to Financial KPI’s and further refined since with work from data lead Paul Bowman; cloud technologist Zhong Hong, and global marketer Stewart Pearson. These efforts culminated in Optimal Net Effect (O.N.E.™).

 “O.N.E. modeling optimized our media spend across twenty-three DMA’s improving response 15% in the first quarter. O.N.E. also accurately modeled new tactics reducing significant waste. The rolling O.N.E. revenue forecast was accurate to less than 1% three quarters out, allowing rapid response to unplanned competitor actions and events like hurricanes,” says a Fortune 50 Marketing Executive.

Ultimately, growth comes via planning and forecasting – not luck.  Marketers who carefully and deliberately model and forecast leveraging a proven algorithm will consistently outperform those who don’t.

Contact O.N.E. to learn more about their Growth Modeling framework.

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