Sunday, December 27, 2020

Our Most Popular Posts for 2020

This will be my last post of 2020, and I want to thank everyone who has spent some of his or her valuable time reading this blog. My goal for this blog has always been to provide content that readers will find informative, thought-provoking, and useful, and I've been immensely gratified by the attention and engagement this blog has received.

For the past several years, I've used my last post of the year to share which posts have been most widely read. The COVID-19 pandemic has obviously been the dominant event of 2020, and I have devoted several posts to COVID-related topics during the year. So this year, I'm providing two "top five" lists - one containing COVID-related posts, and one including posts on other marketing topics.

For these lists, I'm only considering posts that were published in 2020. I've ranked the posts based on cumulative total reads, so posts published early in the year have an advantage.
So in case you missed any of them, here are our top five most popular "non-COVID" posts for 2020:
And here are the top five COVID-related posts:
Happy New Year, everyone!

Image courtesy of Republic of Korea via Flickr CC.

Sunday, December 20, 2020

The Quest for Unified Marketing Measurement

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.

Sunday, December 13, 2020

Measuring the Long-Term Impact of Marketing

The conventional wisdom in the marketing community is that measuring the performance of marketing is now both necessary and achievable. And the conventional wisdom is accurate, at least in part.

The explosion of available data about customers and prospects and the expanding capabilities of marketing and analytics technologies have made some aspects of marketing easier than ever to measure. At the same time, however, measuring the impact of marketing on major business financial outcomes remains a difficult task.

Last year, Google published a white paper that discussed three of the most difficult challenges relating to the measurement of marketing effectiveness. The authors of the paper acknowledge that perfect solutions for these challenges don't currently exist. In fact, the primary objective of the paper was to focus on the areas where existing methods of measuring marketing effectiveness are "running up against the boundaries of the possible."

In my last post, I discussed the first challenge addressed by the Google authors - demonstrating a valid cause-and-effect relationship between a particular marketing activity and a particular business outcome. This post will cover the second "grand challenge" identified in the Google paper.

"Measuring the long term, today"

Some marketing programs are designed to produce results quickly, while others will have an impact over a longer period of time. According to some industry experts, many marketers have become too focused on the short term, to the detriment of overall marketing effectiveness.

Several recent research studies have confirmed that marketers are heavily focused on running short marketing programs and measuring short-term results. For example, in a 2020 survey of B2B marketers by The Marketing Practice (in association with Marketing Week), only 18% of the respondents said they run campaigns for more than six months, and only 20% said they measure the impact of campaigns beyond six months.

This survey was fielded about two months after COVID-19 economic lockdowns began, and the pandemic almost certainly affected the survey responses. But marketing "short termism" began long before COVID-19 reared its ugly head. The Google paper cited a 2018 survey of UK marketers by ISBA in which 61% of the respondents said they measure the impact of their marketing campaigns solely while the campaign is running or in the first three months after it ends. Only 13% said they measure impact for more than a year after a campaign ends.

A number of factors are driving this focus on short-term marketing results, but one of the most important is that long-term marketing effects are difficult to measure. And it's particularly difficult to measure the long-term financial impact of marketing. With marketing leaders under constant pressure to prove the value of their programs, it's not surprising they tend to favor marketing tactics that are easier to measure.

Despite the measurement difficulty, it's important that marketers and other senior business leaders understand the true value of longer term marketing programs. Research has shown that the highest level of marketing effectiveness is achieved when companies use both long-term ("brand building"} and short-term ("sales activation" or "demand generation") marketing programs.

What Marketers Need

The current state-of-the-art method for measuring long-term marketing impact is an enhanced version of marketing mix modeling. Unfortunately, this method requires several years of data, and the cost can be prohibitive for many companies. Equally important, this method, like all forms of marketing mix modeling, is backward looking, so it isn't that useful for marketers who need to make decisions in the present.

What marketers really need is a leading indicator - or a set of leading indicators - that can reliably predict longer-term business outcomes. Recently, share of search has emerged as a promising metric for this leading indicator role. Share of search can be defined as the volume of search queries for a specific brand as a proportion of all the search queries for all the brands that define a competitive category.

For example, suppose that there are five brands (A, B, C, D and E) in a particular product or service category and that over a given time period, a total of 100 searches were performed that included any of these brands. If brand A accounted for 35 of those searches, its share of search was 35% for that time period.

What makes share of search potentially valuable is that it is readily available on a real-time basis and it may be predictive of future outcomes like sales and market share. Recent research by Les Binet has shown that share of search can predict future market share in three categories - automobiles, energy (gas and electricity) and mobile phone handsets. Binet's research found that in these three categories, if a brand's share of search increases, its market share will rise over the following months. And conversely, when share of search declines, so does future market share.

Binet's research is very important, but it was also relatively narrow. It only involved three product categories. We will need more research to determine whether and to what extent share of search predicts future revenue growth and market share in other product and service categories. But if share of search does work in a wide range of categories, many marketers will have one of the key tools they need to make the measurement of long-term marketing effects straightforward, timely and affordable.

Image courtesy of Mike Lawrence ( via Flickr CC.

Sunday, December 6, 2020

Google Highlights "Three Grand Challenges" in Marketing Performance Measurement

Measuring the performance and financial impact of marketing has been (and remains) a major challenge for marketing leaders. In the 2020 Marketing Measurement and Attribution Benchmark Survey by Demand Gen Report, 54% of surveyed marketers said their ability to measure marketing performance and impact needs improvement or is poor/inadequate. The comparable percentage was 58% in the 2019 edition of the survey and 54% in the 2018 survey.

Marketing leaders widely agree about why they need a better process for measuring marketing performance. Seventy-five percent of the respondents in the Demand Gen survey identified the need to show marketing's impact on pipeline and revenue, and 58% cited the need to show ROI from all marketing investments.

Over the past two-plus decades, technological advances have significantly improved our ability to measure some aspects of marketing performance. Today, for example, most forms of digital marketing are highly "trackable." We can know who has opened our emails and who has viewed our content. We can even know how much time was spent with our content.

But, measuring the financial impact of marketing remains particularly difficult because of several inherent characteristics of marketing. A recent article at the Harvard Business Review website captures some of the difficulties:

"Marketing's environment is typically much 'noisier' that the factory floor in terms of unknown, unpredictable, and uncontrollable factors confounding precise measurement. Marketing activities can also be subject to systems effects where the portfolio of marketing tactics work together to create an outcome . . . Marketing actions may also work over multiple time frames . . . Finally, it is often difficult to attribute financial outcomes solely to marketing, because businesses frequently take actions across functions that can drive results."

An Important Perspective from Google

Last year, Google published a white paper that addresses the vital topic of measuring marketing performance. The paper is appropriately titled "Three Grand Challenges" because the authors focus on three of the most gnarly challenges relating to the measurement of marketing effectiveness.

The three "grand challenges" described in the Google paper are:

  1. "Incrementality:  proving cause and effect"
  2. "Measuring the long term, today"
  3. "Unified methods:  a theory of everything"
The authors acknowledge that no perfect solutions for these challenges currently exist. In fact, the main objective of this paper was to discuss the areas where current effectiveness measurement methods are "running up against the boundaries of the possible."
Given the importance of this topic, I'll be devoting three posts to the issues described in the Google white paper. This post will cover the first of Google's three "grand challenges."
The Cause and Effect Conundrum
The most fundamental challenge in measuring marketing effectiveness is demonstrating the existence of a valid cause-and-effect relationship between a particular marketing activity and a particular business outcome (i.e. revenue/sales). In marketing, such causal relationships are often impossible to "prove" directly. Instead, we must infer causation, and the challenge is to make sure that our inferences are based on valid evidence.
The Google authors noted that "randomized controlled experiments" are the gold standard for measuring causal effects. These experiments are similar to the clinical trials that are being used to test prospective COVID-19 vaccines. In the vaccine trials, participants are randomized into two groups, one of which receives the vaccine, and one of which receives a placebo. Then the vaccine developer tracks how many people in each group contract COVID-19 to measure the vaccine's effectiveness.
To run a randomized controlled marketing experiment, the first step is to identify a set of test subjects (i.e. potential buyers) who are as similar as possible. The test subjects are then randomly assigned to a test group or a control group. The marketing activity being tested is used with the test group, but not with the control group. The difference between the groups in the desired outcome (i.e. sales) is the estimated effect of the marketing activity.
Unfortunately, randomized controlled marketing experiments are not easy to use. For example:
  • They must be carefully designed to eliminate extraneous factors that could impact the results.
  • They can be expensive and difficult to administer.
  • They typically can test only one or two activities at a time.
As a result, such experiments aren't frequently used.
When randomized controlled experiments aren't (or can't be) used, marketers typically rely on historical (a/k/a "observational") data to measure marketing effectiveness. Marketing mix modeling and attribution modeling are two measurement methods that are based on observational data. The results produced by observational methods aren't as reliable as those from randomized experiments, but they are widely used.
To establish reasonable expectations for marketing measurement and build credibility in the C-suite, marketing leaders need to have open, frank, and evidence-based conversations with other C-level executives about which aspects of marketing can be measured precisely, and which aspects still require the use of assumptions, correlations and probabilities.

Image Source:  Google