Sunday, July 30, 2023

Not Everything In Marketing Can Be Measured

In a recent article for, Christopher Penn wrote, "Everything in marketing is measurable, from top to bottom, from brand to customer satisfaction to purchase - you can measure 100% of marketing." He argued that when people say some aspect of marketing can't be measured, they mean that, ". . . they don't have the budget and resources to measure what they care about." 

Christopher Penn is the Chief Data Scientist of Trust Insights and the author of AI For Marketers:  An Introduction and Primer, which I recently reviewed. He's a recognized authority on artificial intelligence and data science, so I'm reluctant to disagree with him on anything relating to marketing analytics. But, I have a different view on this issue.

Every marketing channel, tactic, or program can be measured in some ways, but not everything about every marketing activity is measurable.

Despite what you've heard and read, you can't actually measure the financial impact of most individual marketing channels, tactics, and programs. The financial performance of these aspects of marketing can be modeled if you have the right data, tools, and skills. However, the information produced by a model is qualitatively different from the information obtained through measurement.

Measurements vs. Models

Measurement can be defined as the process of quantifying some property of an object or event. In marketing, we can measure the number of visits to a website, the number of e-mail opens and click-throughs, and the number of times a particular content resource is downloaded.

We can also measure the duration of events, such as how long someone spends on a web page. The common denominator in these and all other measurements is that they are based on observations of actual objects or events.

A model, on the other hand, is a simplified representation of a process or system created using statistical principles and mathematical equations. The usual purpose of a model is to describe the current or past behavior of a system or process or to predict its future behavior.

Because models rely on statistical rules and mathematical calculations rather than observations of actual things or events, model outputs are inevitably approximations or estimates of what exists or occurs in the real world.

A model that is properly designed and trained using a sufficient amount of the right data can produce reasonably reliable outputs, but there is always a "margin of error." In short, a statistical model, despite the appearance of mathematical precision, will always be more like an impressionist painting than a photograph.

Why Financial Impact Can't Be Measured

The financial impact of most individual marketing channels, tactics, or programs can't be measured because it can't be observed. To determine the financial impact of these aspects of marketing, you would first need to determine how much of customers' buying decisions were due to the channels, tactics, or programs you're trying to value. And there's just no reliable way to observe that causal effect.

It's tempting to think you could use a survey to ask customers about the impact of various marketing channels, tactics, and programs on their buying decisions. After all, customers should be able to tell you what caused them to make a particular purchase decision. To understand why this approach doesn't work, consider this thought experiment.

Below are the ingredients used to make Nestle's famous "Toll House" chocolate chip cookies:

  • 2 1/4 cups all-purpose flour
  • 1 tsp baking soda
  • 1 tsp salt
  • 2 sticks butter
  • 3/4 cup granulated sugar
  • 3/4 cup packed brown sugar
  • 1 tsp vanilla extract
  • 2 eggs
  • 2 cups Nestle Toll House Semi-Sweet Chocolate Morsels
  • 1 cup chopped nuts
Suppose that these are your favorite cookies - you really love them. If I gave you this recipe and asked you to tell me how much of your enjoyment each ingredient is responsible for, how would you answer? What percentage of your enjoyment is due to the flour? The salt? The brown sugar?
I suspect you would say my question is impossible to answer, and you would be right. That's because your enjoyment - the "value" of the cookies - arises only when all of the recipe's ingredients are combined, and there's no accurate way to attribute a percentage of the enjoyment/value to individual ingredients.
The same is true of buying decisions. A customer's decision to buy results from the combined effect of all the interactions and experiences that occurred during the customer's buying cycle, and there's no realistic way to observe the mental impact of any individual interaction or experience. And, since those impacts can't be observed, they can't be measured.
The financial impact of individual marketing channels, tactics, and programs can be modeled using marketing mix modeling and multi-touch attribution modeling, but marketers need to remember that the outputs of such models are only approximations of reality and treat them accordingly when making decisions.

Image courtesy of Pat Pilon via Flickr (CC).

Sunday, July 23, 2023

How to Judge the Strength of Your Value Propositions

Compelling value propositions are essential for successful marketing. The best way to determine the effectiveness of a proposed value proposition is to test it with real potential customers, but that approach isn't always practical for many B2B companies. This article describes a framework that B2B marketers can use internally to judge the strength of their value propositions.

The textbook definition of a value proposition is ". . . a concise statement of the benefits that a company is delivering to customers who buy its products or services." (Investopedia) A value proposition is a promise you make to potential buyers about the value they will receive by becoming your customer. 

Value propositions are a core component of every company's business and marketing strategy. When you're formulating a business/marketing strategy, there are two questions you must answer very early in the strategy development process.

  1. What type(s) of customers will we seek to serve?
  2. How will we create/deliver superior value for/to those customers?
How you answer these questions will affect every aspect of your strategy, and the second question requires you to develop and articulate clear value propositions.
There are dozens of value proposition templates available online and in books, and almost as many models or frameworks that describe the components of a value proposition or the process marketers should use to develop value propositions.
Some of these templates, models, and frameworks are useful, but developing compelling value propositions is still a challenging task that entails significant background work and requires a combination of art and science.
The best way to gauge the strength of a proposed value proposition is to test it will real potential buyers. Large B2C companies frequently use this approach. For example, a consumer products company may run different versions of a TV ad in different market areas and monitor the performance of each version. Or, they may test the strength of different value propositions using focus groups.
This approach isn't as practical for many B2B companies because they tend to have fewer potential customers and longer sales cycles, and because B2B buying processes typically involve multiple "buyers."
There is, however, a way for B2B marketers to evaluate the likely effectiveness of a proposed value proposition. The following diagram depicts a basic framework that B2B marketers can use internally to judge the strength of their value propositions.

This diagram shows that the strength of a value proposition results from the interplay of three factors - the needs and priorities of prospective buyers, the strength of your competitors' offerings, and the strength of your company's offering.
The diagram also shows where winning, toss-up, and losing value propositions are typically found in the framework, and the following table compares the attributes of strong, so-so, and weak value propositions.

Winners - Your value propositions will be strong when the attributes of your offering are aligned with the needs and priorities of your prospective buyers and when your offering is superior to your competitors' offerings (relative to buyer needs and priorities).
Losers - Your value propositions will be weak or irrelevant if the attributes of your offering aren't aligned with the needs and priorities of your prospective buyers. They will also be weak if the attributes of your offering are inferior to your competitors' offerings.
Toss-Ups - In a three-circle Venn diagram, the "sweet spot" is usually where all three circles overlap, but that isn't the case here. If your value propositions are aligned with your prospective buyers' needs and priorities, but they focus on attributes of your offering that are just equivalent to what your competitors are offering, you won't have a competitive advantage. You'll win some deals and lose some deals, and whether you win or lose will likely depend on price.
In some cases, your value propositions will need to include points of parity (the attributes of your offering that are equivalent to what your competitors are offering) as well as points of difference (the attributes of your offering that are superior to what your competitors are offering). Some of those points of parity may be very important to a prospective buyer, so your value propositions need to show that you "break-even" with your competitors on those points.
The bottom line:  Strong value propositions will emphasize points of difference but include points of parity.

Top image courtesy of jonny goldstein via Flickr (CC).

Sunday, July 16, 2023

[Book Review] Why You Shouldn't Always Try To Reinvent the Wheel

Source:  Amazon

In his new book, Evolutionary Ideas:  Unlocking ancient innovations to solve tomorrow's challenges (Harriman House Ltd., 2022), Sam Tatam argues that marketers and other business professionals don't always need to create extravagant or entirely new solutions to successfully address significant and perplexing problems or challenges.

Evolutionary Ideas is one of the newest in a group of books that deal with the application of behavioral science principles to marketing and other aspects of business.

Sam Tatam is well-positioned to write about this topic. He is currently the Global Head of Behavioral Science at Ogilvy, where he leads a global team of psychologists and behavioral economists. He holds a master's degree in organizational psychology from Macquarie University in Sydney, Australia. 

What's In the Book

The central thesis of Evolutionary Ideas is that principles of behavioral science and evolutionary psychology provide the raw materials for innovative solutions to some of the most perplexing challenges faced by businesses and other organizations.

Sam Tatum argues that the conventional approach to innovation and problem-solving is heavily influenced by two myths. The first is that big problems need big solutions. In other words, we typically think that the more significant a problem or challenge is, the more expansive or "revolutionary" the solution needs to be.

Tatum writes, "We fail to see that, particularly when developing psychological solutions, the rules are different. The subtle, small and incremental can have unexpectedly significant impacts."

The second innovation myth is that new problems require new solutions. The reality, Tatum argues, is that many of the problems or challenges we perceive to be unique and new are actually variants of issues that nature or human psychology has already addressed. Therefore, we should focus more on adapting and applying those natural or psychological solutions that evolution has already shown to be successful.

Evolutionary Ideas is structured in two parts. In Part 1, Tatum discusses three types of "tools" that enable an "evolutionary" approach to innovation and problem-solving.

  • Evolved natural solutions - Those created by adapting solutions that exist in the natural world, i.e. biomimicry
  • Evolved technical solutions - Those created by applying the innovation/problem-solving methodology known as TRIZ

  • Evolved psychological solutions - Those created by applying principles of behavioral sciences
Part 2 of the book is devoted to applying principles of behavioral science to address five of the most fundamental challenges ("contradictions") facing business and marketing leaders.

  • "Reinforcing Trust Without Altering the Truth" (Chapters 6-9)
  • "Aiding Decisions Without Limiting Choice" (Chapters 10-13)
  • "Triggering Action Without Forcing a Decision" (Chapters 14-17)
  • "Boosting Loyalty Without Increasing Rewards" (Chapters 18-21)
  • "Improving Experience Without Changing Duration" (Chapters 22-25)
Tatum concludes his book with an extensive list of questions relating to these five challenges. The questions are designed to stimulate thinking about how behavioral science principles can be leveraged to address each challenge.
My Take
I enjoyed Evolutionary Ideas, and it would be a worthwhile read for anyone involved in marketing or customer experience management.
The book is organized and written well. Sam Tatum's informal writing style makes the book easy to read, and its brief chapters make it easy to consume in bite-sized portions.
Evolutionary Ideas does not contain a detailed academic discussion of the principles of behavioral science and evolutionary psychology, although Tatum does include extensive endnotes and sources for further reading.
Therefore, Tatum's book is particularly well-suited to be the second or third book you read about these topics. For example, whenever clients ask me what they should read to learn about behavioral science, I always recommend they start with Thinking, Fast and Slow by Daniel Kahneman and Nudge:  The Final Edition by Richard Thaler and Cass Sunstein.
Evolutionary Ideas and Nancy Harhut's recent book (which I reviewed here) would both be good choices to be next on your reading list.

Sunday, July 9, 2023

[Research Round-Up] Insights on B2B Marketers, B2B Buyers, and Marketing "Superpowers"

(This month's Research Round-Up features three studies that look at the state of B2B marketing budgets and marketer attitudes, the state of online B2B buying, and what influences B2B buying decisions.)

The State of B2B Marketing Budgets (2023 Edition) by Integrate and Demand Metric

Source:  Integrate and Demand Metric
  • An online survey of B2B marketers with 547 qualified responses
  • 53% of the respondents were affiliated with "Mostly B2B" companies - 47% with "Both B2C and B2B" companies
  • 56% of the respondents were located in the United States - 44% in the United Kingdom
  • 71% of the respondents were with companies having $100 million or less in annual revenue
  • Survey conducted March 16-23, 2023
This survey addressed a wide range of issues relating to B2B marketing including the state of budgets and staffing, how marketers are adapting to economic uncertainty, and the level of marketer optimism.
Here are some of the major findings from the survey.
  • 23% of the surveyed marketers said their 2023 budget was unchanged compared to 2022, while 29% reported a slight (14% or less) increase, and 23% reported a slight decrease.
  • The picture on staffing was similar. Twenty-seven percent (27%) of the survey respondents reported no change in staffing this year, while 24% reported a slight increase, and 24% reported a slight decrease.
  • Over two-thirds (69%) of the surveyed marketers said they had met or exceeded their growth targets over the preceding six months. Most survey respondents were also optimistic about marketing's performance in 2023. More than seven in ten (72%) believe it is likely or very likely their marketing team will meet or exceed its goals this year.
  • When survey participants were asked what marketing areas or tactics they are optimizing for growth in 2023, the top-ranked selection (chosen by 57% of respondents) was customer marketing/upselling/cross-selling. Thirty-eight percent (38%) of the survey respondents ranked customer marketing as their top area of investment in 2023.
The B2B Future Shopper Report 2023 by Wunderman Thompson Commerce & Technology

Source:  Wunderman Thompson
  • A survey of 2,261 purchase managers, procurement managers, purchasing clerks, agents, purchasers, and C-level executives
  • All respondents had a purchasing budget of more than $24,000 and were with companies having annual revenue of more than $595,000
  • Respondents were drawn from eleven countries around the world
  • Survey was conducted November 21, 2022 - December 5, 2022
This is the third edition of Wunderman Thompson's B2B Future Shopper research. The new edition of the report contains 77 pages, and it's filled with valuable insights regarding the attitudes and behaviors of global B2B buyers.
It's impossible to do this research justice in a brief summary, but here are a few of the major findings.
  • 49% of global B2B buying/spending is now online, and survey respondents predicted the level will rise to 57% in five years.
  • 46% of the respondents said they are frustrated with buying B2B products online.
  • 67% of the surveyed buyers said they start their purchase journey online, with the supplier's website/mobile site being the top channel for both search and purchases.
  • 43% of the respondents said they changed some of their suppliers in the previous 12 months, with pricing issues being the primary reason for switching suppliers.

Source:  Merkle B2B
  • Based on a survey of 3,622 buyers and users of B2B products and services
  • Captured insights into 6,767 B2B brand experiences
  • Survey respondents were recent B2B buyers across technology, financial services, manufacturing, and professional services, and respondents were drawn from markets in North America, Europe, and APAC.
This research aimed to describe important attributes of B2B buying and identify the factors (the "decision drivers") that influence purchase decisions.
Merkle's research found that competition between B2B brands has become more intense. For example:
  • The average number of brands considered by B2B buyers increased from 3.1 in 2022 to 3.2 in 2023.
  • On average, B2B buyers are taking longer to make purchase decisions - 350 days in 2023 compared to 344 days in 2022.
  • The incumbent brand's position is less secure. In 2023, incumbents lost out in 34% of buying journeys, up from 29% in 2022.
The report discusses seven decision drivers that showed a new and elevated level of importance in the 2023 research and 13 decision drivers that remained as important in the 2023 study as they were in the 2022 edition of the research.
In the 2023 research, the decision driver that was the most important factor for buyers when making a purchase decision was, "I feel safe signing a contract with them."
This research provides several valuable insights, and I recommend that you read the full report.

Sunday, July 2, 2023

What B2B Marketers Can Learn From Missing Bullet Holes

Image Source:  Wikipedia

Data has become the lifeblood of modern marketing. It now touches almost every aspect of the marketing function. But using the wrong data (or the right data in the wrong way) can lead to ineffective and costly decisions. Here's one mistake marketers need to avoid.

Fueled by the explosive growth of online communication and commerce, marketers now have access to a huge amount of data about customers and potential buyers. Astute marketing leaders have recognized that this ocean of data is potentially a rich source of insights they can use to improve marketing performance. Therefore, many have made - and continue to make - sizeable investments in data analytics.

Data undeniably holds great potential value for marketers, but it can also be a double-edged sword. If marketers use inaccurate or incomplete data, or don't apply the right logical and statistical principles when analyzing data, the results can be costly.

The reality is, a variety of potential pitfalls lurk in almost every dataset, and many aren't obvious to those of us who aren't formally trained in mathematics or statistics. An incident that occurred during World War II dramatically illustrates a data analytics pitfall that is still far too common and not always easy to detect.

The Case of the Missing Bullet Holes*

In the early stages of the war in Europe, a significant number of U.S. bombers were being shot down by machine gun fire from German fighter planes. One way to reduce these losses was to add armor plating to the bombers.

However, armor makes a plane heavier, and heavier planes are less maneuverable and use more fuel, which reduces their range. The challenge was to determine how much armor to add and where to put it to provide the greatest protection for the least amount of additional weight.

To address this challenge, the U.S. military sought help from the Statistical Research Group, a collection of top mathematicians and statisticians formed to support the war effort. Abraham Wald, a mathematician who had immigrated from Austria, was a member of the SRG, and he was assigned to the bomber-armor problem.

The military provided the SRG with data they thought would be useful. When bombers returned from missions, military personnel would count the bullet holes in the aircraft and note their location. As the drawing at the top of this post illustrates, there were more bullet holes in some parts of the planes than others. There were lots of bullet holes in the wings and the fuselage, but almost none in the engines.

Military leaders thought the obvious solution was to put the extra armor in the areas that were being hit the most, but Abraham Wald disagreed. He said the armor should be placed where the bullet holes weren't - on the engines.

Wald argued that bombers returning from missions had few hits to the engines (relative to other areas) because the planes that got hit in the engines didn't make it back to their bases. Bullet holes in the fuselage and other areas were damaging, but hits in the engines were more likely to be "fatal." So that's where the added armor should be placed.

An Example of Selection Bias

The mistake U.S. military leaders made in the bomber incident was to think the data they had collected was all the data that was relevant to the problem they wanted to solve.

The flaw in the bomber data is now called a survival bias, which is a type of selection bias. A selection bias occurs when the data used in an analysis (the "sample") is not representative of the relevant population in some important respect.

In the bomber case, the sample only included data from bombers that returned from their missions, while the relevant population was "all bombers flying missions."

So why should B2B marketers care about bullet holes in World War II bombers? Because it's very easy for marketers to fall prey to selection bias. Here are a couple of examples:

  • Suppose you survey your existing customers to identify which of your company's value propositions are most attractive to potential buyers. Because of selection bias, the data from such a survey may not provide valid insight into what value propositions would be attractive to other potential buyers in your target market.
  • Suppose you develop maps of buyers' purchase journeys based primarily on data about the journeys followed by your existing customers and by non-customers who have engaged with your company. Because of selection bias, these journey maps may not accurately describe the buyer journeys followed by potential buyers who never engaged with your company.
Selection bias is a troublesome issue because, like all humans, we marketers tend to base our decisions on the evidence that's readily available or easily obtainable, and we tend to ignore the issue of what evidence may be missing. In many cases, unfortunately, the evidence we can easily access isn't broad enough to give us valid answers to the issues we are seeking to address.

*My account of the incident is drawn from How Not To Be Wrong by Jordan Ellenberg.