UML

The Quest for Unified Marketing Measurement

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Multiple research studies have shown that measuring marketing performance remains both a top priority and a major challenge for most marketers. For example, in Demand Gen Report’s 2020 Marketing Measurement and Attribution Benchmark Survey, 82% of the respondents said that measuring marketing performance is a growing priority for their company, and 54% said their ability to measure marketing performance and impact needs improvement or is poor/inadequate.

Gartner’s Marketing Data and Analytics Survey 2020 found that a majority of senior marketers (CMOs and VPs of marketing) are disappointed with the results they have received from their analytics investments. Fifty-four percent of senior marketing respondents said that marketing analytics had not had as much influence in their organization as they expected.

These research findings show that marketing performance measurement is still very much a work in process. Last year, Google published a white paper that addressed three of the most important – and still unsolved – challenges relating to the measurement of marketing effectiveness. I covered two of these challenges in previous posts (here and here). This post will discuss the third “grand challenge” described in the Google paper.

“Unified methods:  a theory of everything”

At present, there are two main methods for measuring the effectiveness of marketing and advertising programs. Marketing mix modeling has been around for decades, and multi-touch attribution has now been used for several years. Each of these methods has strengths and limitations, and neither provides a comprehensive picture of marketing performance. As a result, the grand challenge for marketers is to develop a unified measurement method that will provide a holistic and accurate view of marketing effectiveness.

Marketing Mix Modeling (MMM) – MMM involves the use of statistical techniques to estimate the impact of marketing and advertising programs on incremental sales and/or other desired outcomes. These models are based on several months (or years) of historical data about sales and marketing/advertising spending across offline and digital channels. MMM also incorporates factors such as weather, competitive activity, seasonality, and overall economic conditions.

MMM is a top-down method that uses aggregate data; it doesn’t evaluate the actions of individual prospects or customers. Because MMM is backward-looking and doesn’t use individual-level data, it doesn’t provide the timeliness or granularity that is needed to support tactical marketing decisions.

Multi-Touch Attribution (MTA) – MTA is a bottom-up method that is based on data about the actions and behaviors of individual prospects and customers. MTA solutions focus primarily on the financial impact of digital marketing programs. Therefore, they can overstate the amount of revenue attributable to digital marketing activities. MTA solutions can also be inaccurate because they usually don’t account for a baseline of revenue that would exist without any marketing efforts.

Enter Unified Marketing Measurement (UMM)

Clearly, marketing leaders need (and want) a way to measure marketing effectiveness accurately and comprehensively. Since MMM and MTA use different types of data and measure different aspects of marketing effectiveness, one possible solution is to use both methods, and some companies have adopted this approach.

The Google white paper cited a 2018 survey conducted by ISBA (Incorporated Society of British Advertisers). In that survey, 29% of the respondents said they had fully integrated MMM and MTA. Another 39% of the respondents said they were using both MMM and MTA, but results are reviewed in silos.

During 2018, Google also conducted forty interviews with marketers at large brands in the UK about their marketing effectiveness practices. Here’s how the Google authors described what they learned about the integration of MMM and MTA:

“While this wasn’t a survey, examples of ‘fully integrated’ MMM and digital attribution were cited in fewer than a third of these conversations . . . When integration was mentioned, it was more the case that the advanced marketers now had effectiveness leaders whose role it was to understand marketing effectiveness as a whole, and consider results from different methods. These leaders were typically very aware of the pros and cons of MMM and digital attribution and were often blending insights from them, rather than integrating them at a technical level.”

I suspect that little has changed since Google conducted these interviews, especially given the disruptive impact of COVID-19 this year.

Several companies are now offering technology solutions that purport to provide unified marketing measurement. In The Forrester Wave(TM):  Marketing Measurement and Optimization Solutions, Q1 2020, Forrester evaluated nine “significant” vendors that offer some version of a UMM solution. The vendors included in the Forrester report were Analytic Partners, Ekimetrics, Gain Theory, Ipsos MMA, IRI, Marketing Evolution, Merkle, Neustar, and Nielsen.

The Issue of Accessibility

There’s no doubt that we have made significant progress in measuring marketing effectiveness over the past several years. However, advanced marketing measurement solutions aren’t cheap. In a 2018 report, Gartner estimated that companies pay from $100,000 to $250,000 on average for a one-year MMM or MTA solution. At this level of investment, these solutions aren’t affordable for many small and mid-size companies

But notwithstanding the cost, advanced measurement solutions can be a smart investment for many companies. In a 2018 report, Forrester noted that such solutions will often enable a 15% improvement in marketing ROI, and that spending on marketing measurement represents only 0.2% of total marketing spending.

Image courtesy of Tatinauk via Flickr CC.

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