[Research Round-Up] Salesforce Survey Examines the State of Marketing

Source:  Salesforce

(This month's Research Round-Up focuses exclusively on the latest edition of the State of Marketing survey by Salesforce. The new Salesforce survey is a large global survey of B2B and B2C marketers, so it provides a broad perspective on the priorities, challenges, and attitudes of the marketing community.)

Salesforce recently published the findings of its latest State of Marketing survey. The latest survey is the ninth edition of the Salesforce research. It was in the field February 5 - March 12, 2024.

Survey Demographics

• The survey produced 4,850 responses from marketing decision-makers
• Respondents were drawn from 30 countries across North America, Latin America, Asia-Pacific, and Europe
• Respondents worked in 18 industry verticals
• 50% of the respondents worked in B2C companies, and 50% worked in B2B or B2B2C companies
• 39% of the respondents were VP-level or above
• 50% of the respondents were with mid-market companies (101 - 3,500 employees), 30% were with small and medium-sized companies (1 - 100 employees), and 20% were with large enterprises (over 3,500 employees)

Marketing Performance Levels

Salesforce classified survey respondents based on their self-reported level of marketing performance and used these categories to report some survey findings. The three categories used in the survey report are:

• High performers - Respondents who were completely satisfied with the overall outcomes of their marketing investments.

• Moderate performers - Respondents who were highly satisfied with the overall outcomes of their marketing investments.

• Underperformers - Respondents who were moderately or less satisfied with the overall outcomes of their marketing investments.

Marketers' Top Priorities and Challenges

Salesforce asked survey participants to identify their top priorities and biggest challenges, and the following table shows how respondents answered those questions.

It shouldn't be surprising that survey respondents identified "implementing or leveraging AI" as their top priority and their biggest challenge. Artificial intelligence, particularly generative AI, has been the hottest topic in marketing since OpenAI released ChatGPT in late 2022.

It's also notable, but not surprising, that the top priorities and the biggest challenges are nearly identical. These survey respondents clearly believe that for marketing to have the greatest possible impact on the business, they must successfully address their biggest challenges.

The State of AI

The survey revealed that the implementation of AI is still in its early stages. Thirty-two percent of all respondents said they have fully implemented AI in their operations.

Salesforce did find that high performers were 2.5x more likely than underperformers to have fully implemented AI. Forty-two percent of high performers said they have fully implemented AI, compared to only 17% of underperformers.

The survey also asked participants how they were using or planned to use AI, and the top five use cases identified by respondents were:

1. Automating customer interactions
2. Generating content
3. Analyzing performance
4. Automating data integration
5. Driving best offers in real time

While marketers are excited about the potential benefits of AI, they also have concerns about embracing the technology, particularly generative AI. The top five concerns about generative AI identified by survey respondents were:

1. Data exposure or leakage
2. Lack of necessary data
3. Lack of strategy or use cases
4. Inaccurate outputs
5. Copyright or intellectual property concerns

The State of  Marketing survey also provides valuable data on several other topics, including:

• The strategies and sources marketers are using to collect customer data
• Where and how much marketers are using personalization in their marketing programs
• How marketers are measuring marketing performance

The Salesforce State of Marketing survey is one of the research studies I pay attention to every year. I wish Salesforce provided a breakdown of responses between B2B vs. B2C companies and by country (or at least region), but even without this more granular reporting, the survey still provides important insights.

[Book Review] An Essential Guide to Artificial Intelligence for Marketers

Source:  Trust Insights

The marketing world has been going gaga over ChatGPT for the past four months. 

The generative AI application from OpenAI was released to the public on November 30, 2022, and analysts have estimated that it reached 100 million monthly active users in January of this year.

The capabilities of ChatGPT have amazed many users, including me. More importantly, the buzz surrounding ChatGPT has ignited an arms race among tech companies to develop and roll out new or enhanced applications enabled by artificial intelligence. 

For example, Microsoft - which had already invested $1 billion in OpenAI - recently confirmed that it will invest an additional $10 billion in the company, and announced that it is incorporating some ChatGPT-like functionality in its Bing search engine. Google, Salesforce, Hubspot, and a host of other firms have also recently announced new or enhanced applications featuring generative AI capabilities.

The application of artificial intelligence to marketing isn't new, but the intense reaction to ChatGPT by both the public and tech companies suggests that we are on the cusp of a step change in the use of AI in marketing.

Until now, many marketers have been able to function effectively with only a superficial understanding of artificial intelligence. But, as AI becomes increasingly integral to marketing at more and more companies, it will be imperative for marketers to have a better grasp of AI principles and techniques.

Christopher S. Penn, the co-founder and Chief Data Scientist of Trust Insights, and a recognized authority on analytics, data science, and machine learning, has written a book that will help marketers begin their journey toward a better understanding of artificial intelligence.

AI For Marketers:  An Introduction and Primer (Third Edition, 2021) provides a sound introduction to basic AI techniques and illustrates how AI can be used to improve marketing performance.

What's In the Book

AI For Marketers contains 18 chapters, but the book's content falls into four broad topic categories.

In the opening two chapters, Penn discusses the importance of AI in marketing and explains why marketers often struggle with AI. He argues that one reason marketers find AI challenging is that, " . . . AI and its prerequisites are deeply entrenched in mathematics and statistics - two fields which are not strong points for most marketers." (Emphasis in original)

Penn devotes four chapters to an explanation of the basic techniques of artificial intelligence. He defines AI and explains algorithms, models, and types of machine learning. He also covers the vital importance of good data and provides a useful data quality framework.

The third major topic category in AI For Marketers is a description of several practical applications of AI in marketing. Penn places his discussion in the context of problems that AI can help marketers address. For example, he explains how attribution analysis can help marketers forecast what strategies, tactics, and tools will deliver the best results, and how dimension reduction and feature selection can help marketers identify which data points are important.

Lastly, Penn discusses how companies can successfully incorporate artificial intelligence in their marketing efforts, and how marketers can prepare their careers for AI. He lays out seven steps that describe the process of becoming an "AI-first company," and he covers the people and process governance capabilities that companies need to be successful with AI.

My Take

Writing a book about the use of artificial intelligence in marketing is a daunting task because the field is evolving so rapidly that a book can easily become outdated soon after it's published.

Writing a book about AI for marketers is even more challenging because most marketers have little, if any, education or training in statistics or computer science, both of which are essential components of artificial intelligence.

Christopher Penn does an admirable job of addressing both of these challenges in AI For Marketers. He focuses on the core fundamentals of artificial intelligence and on the basic applications of AI in marketing. He also explains AI concepts and techniques in an informal, easy-to-understand way, making the subject accessible to marketers who haven't been trained in statistics and computer science.

In the book, Penn argues that marketers don't need to become practitioners of AI in the sense of learning statistics, data science, and machine learning. He uses the analogy of "chefs and farmers" to illustrate his argument. He wrote:

"Talented chefs take great ingredients and, using the right tools and skills, transform those ingredients into delicious food . . . However, what's the likelihood that the chef is also a farmer . . . Almost none . . . [Chefs] may have some sense of what's gone into an ingredient, but they're not the ones to focus on the details of the ingredient's creation . . .

The typical outcome of an artificial intelligence platform is a model that creates insights or makes decisions. The software . . . plugs into our marketing infrastructure and spits out highly refined products from the raw ingredients - data, algorithms, and analyses. The machines are the farmers, and we are the chefs." (Emphasis in original)

I would contend that Penn's argument goes a little too far. I believe that many marketers - particularly those in more senior roles - will need to delve a little deeper into artificial intelligence than Penn suggests.

Penn wrote that marketers ". . . should know what great data, algorithms, models, or decisions look like . . ." It's difficult - probably impossible - to determine whether an algorithm is "great" and fit for purpose if you don't know how the algorithm works and what its strengths and limitations are. While AI For Marketers is a solid introduction to the topic, it doesn't go quite deep enough to provide this level of information.

Even with this one caveat, I strongly recommend Chris Penn's book. AI For Marketers is a great resource for marketers who are beginning their journey toward a greater understanding of artificial intelligence and its expanding role in marketing.

One final point. The third edition of AI For Marketers was published in 2021 and therefore doesn't capture the tsunami of developments in AI that have occurred over the past few months. I subscribe to Chris Penn's Almost Timely Newsletter, and he has already provided numerous valuable insights regarding the recent developments in AI.

I have no inside information, but I suspect that a fourth edition of AI For Marketers is already in the works. In other circumstances, I might recommend that you wait a bit for the new edition of the book. But artificial intelligence is poised to become so important for marketers that I think you should read the third edition now and be prepared to read the fourth edition when it appears. 

[Book Review] An Insightful (and Timely) Guide To Marketing Metrics

Source:  Kogan Page

Most marketers will readily acknowledge that effectively using metrics, analytics, and data has become critically important to successful marketing. Companies can now access a huge amount of data regarding the behaviors of customers and prospects, and, therefore, "data-driven marketing" has become a guiding mantra for marketers.

Many marketers are now using metrics, analytics, and data to track the performance of some marketing activities, but relatively few companies are systematically using metrics, analytics, and data to make strategic marketing decisions. As a result, many companies are missing the opportunity to improve future marketing performance.

That's one of the main themes of Christina Inge's new book, Marketing Metrics:  Leverage Analytics and Data to Optimize Marketing Strategies (Kogan Page, 2022). Ms. Inge is the founder and CEO of Thoughtlight, a technology consulting firm and marketing agency that specializes in digital marketing and analytics strategies.

Ms. Inge clearly states her objective for the book when she writes:

"This book shows you how to apply the latest analytics to all aspects of marketing management . . . [I]t provides step-by-step instructions on how to create a data-driven marketing strategy . . . They [marketing metrics] are not just about understanding what happened in the past, but also about using that information to shape what will happen in the future."

What's In the Book

Marketing Metrics is a holistic, thorough, and non-technical guide to the use of metrics, analytics, and data in marketing. In Chapter 1, Christina Inge introduces her topic and lays the foundation for the content in the remainder of the book.

The discussion of specific metrics is found in Chapters 2-10. Ms. Inge describes and explains how to use a wide range of metrics including:

• Customer, channel, and branding metrics (Chapters 2-5)
• Content metrics (Chapters 6-7)
• Product, pricing, and "place" metrics (Chapters 8-9)
• Testing methods and the use of metrics in agile marketing (Chapter 10)

Marketing Metrics then addresses two issues relating to marketing management. In Chapter 11, Ms. Inge reviews the major laws and regulations pertaining to data privacy, and she describes several frameworks relating to data governance and the ethical use of data. In Chapter 12, she discusses the importance of "data evangelism," and she explains how to design effective marketing dashboards.

Chapter 13 of the book describes the skills that marketers need to be "metrics-driven," and Chapter 14 contains an extensive list of publications and other resources relating to marketing metrics and analytics.

My Take

Marketing Metrics will be a valuable resource for any marketer who doesn't have extensive experience using metrics and analytics to track current marketing performance and support strategic marketing decisions.

Christina Inge does an excellent job of making a complex topic both interesting and accessible to marketers. Her writing is clear, and she manages to thoroughly explain the metrics she discusses, while almost entirely avoiding the use of mathematical formulas.

Ms. Inge also provides numerous examples throughout the book to illustrate how marketers can use metrics to make better decisions and improve marketing performance.

One of the most important insights in Marketing Metrics is found in the opening chapter. Ms. Inge contends that companies must develop a "metrics-driven culture" in order to use data effectively, and she further argues that the creation of a metrics-driven culture requires marketers to be comfortable with ambiguity and uncertainty. She writes:

"Interpreting data depends on context, which by its very nature contains a whole lot of ambiguity . . . So, although numbers can give us answers, they also raise a lot of questions, and those answers they provide are not always clear or easy ones. This means that smart metrics-driven marketers learn to navigate uncertainty using data as a compass, not always as a map, let alone turn-by-turn directions."

Unfortunately, many marketers have a tendency to expect metrics and analytics to provide clear-cut answers, and these unrealistic expectations can lead marketers to ignore the results produced by their metrics and analytics efforts or simply avoid using metrics and analytics when making decisions.

In a 2020 survey of marketing leaders and analytics practitioners by Gartner, respondents said that analytics influenced only 54% of their marketing decisions. When asked why analytics wasn't used to support more decisions, one of the top reasons cited by respondents was analysis does not present a clear recommendation.

Lastly, Marketing Metrics is a timely book. With uncertainty about future economic conditions likely to persist for at least the rest of 2023, it will be more important than ever for marketers to maximize the results produced by their marketing programs. Marketing Metrics can help marketers achieve this vital goal.

[Research Round-Up] The State of Artificial Intelligence in Marketing

(The marketing world has been captivated by ChatGPT for the past several weeks. The generative AI-powered chatbot developed by OpenAI has been widely hailed by some members of the marketing community, and widely criticized by others. While the jury is still out on the actual impact of ChatGPT on marketing, the use of artificial intelligence has been one of the hottest topics in marketing for the past few years. So, it seems appropriate that this month's Research Round-Up features two recent surveys that explore the state of AI in marketing.)

2022 State of Marketing and Sales AI Report by the Marketing AI Institute and Drift

Source:  Marketing AI Institute and Drift

• A survey of more than 600 marketers representing a wide range of industries and company sizes
• 49% of the respondents were director level or above
• 65% of the respondents were based in the United States, the UK, India, Canada, and Germany
• 38% of the respondents work exclusively in B2B - 41% work in both B2B and B2C
• Survey open from June 1, 2021 to June 1, 2022

The objective of this survey was to gain insight into how marketers are using artificial intelligence to support their activities and programs. Overall, the survey findings indicate that the use of AI in marketing is still in the early stages.

Two-thirds of the respondents (67%) said they were still learning how AI works and exploring use cases and technologies. Just 15% of the respondents reported that they had achieved wide-scale implementation of AI.

When asked how they would classify their understanding of AI terminology and capabilities, 45% of the respondents rated their level of understanding as beginner, 43% said intermediate, and only 12% said advanced. In addition, only 29% of the respondents said they are highly confident or very highly confident in their ability to evaluate AI-powered marketing technologies.

The research found that marketers recognize the importance of AI and expect its use to grow significantly in the near future. Fifty-one percent of the survey respondents said AI is very important or critically important to their marketing success over the next 12 months. And another 33% said it is somewhat important.

Over three-quarters of the survey respondents (77%) said they are currently automating 25% or less of their marketing tasks using AI, but a similar proportion of respondents (74%) said they expect more than 25% of their tasks will be automated using AI over the next five years.

The final part of the survey report provides interesting data on 50 marketing AI use cases across five categories of marketing activities - planning, production, promotion, personalization, and performance. This portion of the research should be particularly useful for any marketer who is evaluating potential AI use cases.

The state of AI in 2022 - and a half decade in review (McKinsey & Company)

Source:  McKinsey & Company

• This article discusses the major findings from the 2022 McKinsey Global Survey on AI
• Survey produced 1,498 responses
• Survey respondents represented "the full range of regions, industries, company sizes, functional specialties, and tenures."
• Survey was in the field from May 3 to May 22, 2022 and from August 15 to August 17, 2022

Note:  McKinsey's Global Survey on AI focuses on the use of artificial intelligence by business organizations, not exclusively on the use of AI in marketing. However, some of the survey findings are specifically about the use of AI by the marketing and sales function.

McKinsey has been conducting surveys to track the use of AI in business for the past five years, and the research shows that AI adoption has more than doubled during that period. In the 2022 edition of the survey, 50% of the survey respondents said they had adopted AI in at least one business function, up from 20% in the 2017 edition of the survey.

However, the survey also showed that the proportion of survey participants using AI has plateaued between about 50% and about 60% for the past four years.

The research found that the average number of AI capabilities that organizations use has doubled over the past four years, rising from 1.9 in the 2018 survey to 3.8 in the 2022 survey.

The investment in AI has increased significantly over the past five years. In the 2017 edition of McKinsey's survey, 40% of the survey respondents at organizations using AI said that more than 5% of their digital budgets went to AI; in the 2022 edition of the survey, more than half of the respondents reported that level of investment. In addition, 63% of the respondents in the 2022 survey said they expect their organization's investment in AI to increase over the next three years.

Of the ten most commonly adopted AI use cases identified by respondents in the 2022 survey, three were marketing and sales use cases - customer service analytics, customer segmentation, and customer acquisition and lead generation.

Lastly, 70% of the survey respondents at organizations using AI for marketing said their marketing and sales function realized revenue increases in 2021 from the adoption of AI.

 

Why a Cautious Approach to Marketing Analytics Makes Sense

Fueled by the exponential growth of online communications and commerce, marketers now have access to an immense amount of data regarding customers and potential buyers. Marketers have recognized that this vast sea of data can be a rich source of insights they can use to improve marketing performance and drive business growth.

The use of data in marketing has a long history, but it's been one of the hottest topics in marketing circles for the past several years. The benefits of "data-driven marketing" have been touted so frequently by so many industry analysts, consultants and technology providers that leveraging data is now viewed as essential for effective marketing. As a result, many marketers have made substantial investments in data collection and analytics capabilities.

The Real-World Use of Marketing Analytics

Despite the abundance of data and the increasing power and sophistication of data-related technologies, the actual use of data analytics in marketing isn't as pervasive as all the hype would suggest. In the September 2022 edition of The CMO Survey, respondents reported that marketing analytics is used in 48.9% of projects 

A survey of marketing analytics users conducted by Gartner earlier this year produced similar findings. In that research, respondents said marketing analytics influences just over half (53%) of marketing decisions.

When Gartner asked survey participants why analytics isn't used to support more marketing decisions, the two most frequently cited reasons related to data quality and management - "data are inconsistent across sources" and "data are difficult to access."

However, Gartner's survey also found that the practices of business decision makers are impacting the use of marketing analytics. For example, a third of the respondents said decision makers tend to use the output of analytics when it supports the action they've already decided to take and to ignore such output when it points to a contrary action. Hello, confirmation bias!

In addition, about a fourth of the survey respondents said decision makers don't review the information provided by marketing analytics, reject the recommendations provided by marketing analytics, or decide to rely on gut instincts to make their decisions.

Satisfaction With Marketing Analytics is Mixed

Research has also found that satisfaction with the impact of marketing analytics is mixed. For example, the September edition of The CMO Survey asked participants to rate the contribution of marketing analytics to company performance using a 7-point scale, where 1 = "Not at all" and 7 = "Very highly." 

Fifty-eight percent of the survey respondents rated the contribution of marketing analytics at 5 or above, indicating a relatively high level of satisfaction with the impact of analytics.

But in earlier research by Gartner, 54% of the surveyed senior marketing leaders - CMOs and VPs of marketing - said marketing analytics had not produced the impact on their organization they had expected.

Some industry analysts have suggested that underutilization and the perceived lack of business impact may cause some company leaders to reduce their investment in analytics capabilities. 

Commenting on the findings of Gartner's 2022 survey, Joseph Enever, a Senior Research Director in the Gartner marketing practice, said, "By 2023, Gartner expects 60% of CMOs will slash the size of their marketing analytics department in half because of failed promised improvements."

A Cautious Approach to Analytics May Be Wise

But is it altogether bad for marketing leaders to approach the use of marketing analytics with a healthy amount of caution? I don't think so, and here's why.

Marketing analytics can fail to deliver the expected benefits for several reasons. First, the hype surrounding the use of data and analytics in marketing has raised the expectations marketers and other business leaders to inflated levels. And second, marketers are still learning how to generate insights from data and analytics that will make meaningful contributions to business performance.

It's also becoming clear that the data most marketers are relying on, and how they are using that data, can produce "blind spots" that lead to less-than-expected results. An October 2020 article in the Journal of Marketing identified four of these potential blind spots.

1. "First, marketing data may result in prioritizing short-term growth ahead of long-term growth."
2. "Second, marketers may overly rely on historical, internal data at the expense of forward-looking, external growth opportunities."
3. "Third, marketing data may create a preference for more easily measured digital touchpoints at the expense of offline channels."
4. "Finally, marketers may rely on available data in lieu of representative or predictive data."

(Emphasis in original)

The fourth blind spot cited in the Journal of Marketing article alludes to a broader issue relating to the use of marketing analytics and also points to an important limitation of data-driven marketing.

As I noted earlier, marketers now have access to a huge amount of data regarding their customers and potential buyers. But the data most marketers are using to fuel analytics, while vast, is not comprehensive. It doesn't provide a complete picture of the wants, needs or mindset of a potential buyer. Therefore, the recommendations produced by analytics are not always as accurate as we often assume, and this partially explains why analytics doesn't always deliver the expected results.

Given this limitation, business leaders (including marketing leaders) should view the outputs of marketing analytics with a critical eye and not become overconfident in the accuracy of those outputs or the business impact they will produce.

Like all humans, we marketers have a strong tendency to base our decisions on the evidence that's readily available, and we tend to ignore the issue of what evidence may be missing. Daniel Kahneman, winner of the 2002 Nobel Prize in Economic Sciences, has a great way to describe this powerful human tendency. He uses the acronym WYSIATI, which stands for what you see is all there is. 

The vast amount of data at our fingertips and the seductive capabilities of marketing analytics technologies can easily lead us to believe that the data we collect and analyze is the only thing that matters, and that simply isn't true.

I'm not arguing that marketers should not use data, analytics and data-driven marketing. These tools and techniques can be immensely powerful. The key is to use them wisely and to remember they're neither complete nor perfect.

Image courtesy of Rick B via Flickr (Public Domain).

[Research Round-Up] B2B Highlights from "The CMO Survey" - The Impact of Marketing Analytics and "Working from Home"

Source:  "The CMO Survey" (Christine Moorman, 2022)

(This month's Research Round-Up continues my review of selected B2B findings from the September 2022 edition of "The CMO Survey." In this post, I'm discussing what the survey found pertaining to the growth and impact of marketing analytics and "working from home" in B2B marketing.)

In last month's Research Round-Up post, I discussed some of the major findings in the latest edition of "The CMO Survey." "The CMO Survey" is directed by Dr. Christine Moorman and is sponsored by Deloitte LLP, Duke University's Fuqua School of Business and the American Marketing Association.

This research has been conducted semi-annually since 2008, and it consistently provides a wealth of information about marketing trends, spending and practices. I provided a detailed description of the survey in my earlier post, so I won't repeat that here.

In this post, I'll cover two more findings from the survey that I found particularly interesting. As in my earlier post, I'll be discussing the responses of B2B marketers exclusively unless otherwise indicated. The percentages and other numerical values in this post are the mean of applicable survey responses, also unless otherwise indicated.

The Growth and Impact of Marketing Analytics

"The CMO Survey" asked participants several questions relating to their investment in, and use of, marketing analytics. Respondents with B2B product companies said they currently spend about 10% of their marketing budget on analytics, while those with B2B services companies said they devote about 7% of their budget to analytics.

Spending on marketing analytics appears poised to increase. Respondents with B2B product companies said they expect to spend just over 15% of their marketing budget on analytics in the next three years, while those respondents with B2B services companies expect to spend about 13% of their budget on analytics in the same time period.

The survey also asked participants to rate the contribution of marketing analytics to their company's performance using a 7-point scale, where 1 = "not at all" and 7 = "very highly." Just over two-thirds of the B2B marketer respondents (67.6% of respondents with B2B product companies and 67.3% of those with B2B services companies) rated the contribution of marketing analytics at 4 or above.

These findings indicate that the B2B survey respondents had a generally favorable opinion of marketing analytics. However, other research paints a different picture.

For example, a survey conducted earlier this year by Gartner found that analytics only influences 53% of marketing decisions. Commenting on the survey findings, Joseph Enever, a Senior Research Director in the Gartner marketing practice, said, "By 2023, Gartner expects 60% of CMOs will slash the size of their marketing analytics department in half because of failed promised improvements."

The Extent and Impact of "Working From Home"

One of the most profound effects of the COVID-19 pandemic on business organizations has been the proliferation of remote work - a/k/a "working from home."

When the pandemic began in early 2020, many companies quickly enabled most of their administrative employees to work exclusively from home. Nearly three years later, many companies are using a "hybrid" model of work. While the specifics vary, they typically require employees to be "in the office" some number of days each week, but allow them to work remotely on the other days.

Remote/hybrid work and "return to the office" have been hot topics in the business media for the past several months, but most of the coverage has focused on these topics at the company or industry level. "The CMO Survey" provides several important insights about the extent of remote work in marketing and the impact of working from home on the marketing function.

Remote work appears to be fairly widespread in B2B marketing. In the September edition of the survey, respondents with B2B product companies reported that more than half of the people in their marketing organization are working from home all or part of the time. Respondents with B2B services companies reported that 57% of their marketing employees are working remotely all the time, and 49% are working from home some of the time.

"The CMO Survey" also asked participants about the impact of remote work on five attributes of their marketing organization. The following table summarizes how survey respondents described the impacts.

As this table shows, most of the surveyed B2B marketers do not think remote work has made their marketing organization less productive. In fact, significant percentages of the respondents reported that working from home has improved their organization's productivity.

The table also shows, however, that B2B marketers are concerned that remote work is having a negative impact on the culture of their marketing organization and on their ability to properly socialize younger team members.

[Book Review] A Strategic Guide to Using Artificial Intelligence in Marketing

Source:  Stanford University Press

The use of artificial intelligence (AI) in marketing has been discussed by an army of industry pundits, and recent research suggests that it may be nearing mainstream adoption. For example, in the seventh edition of Salesforce's State of Marketing survey (conducted May - June 2021), 60% of the respondents said they have a "fully developed" AI strategy, up from 57% in the 2020 edition of the survey.

It's easy to find e-books, white papers, and articles discussing the role of AI in marketing, and there are dozens of books dealing with the technical aspects of artificial intelligence and the social and cultural ramifications of AI.

There are far fewer full-length books that provide a detailed treatment of how AI can be used to support marketing decisions and enable more productive marketing programs. For that reason, I looked forward to reading The AI Marketing Canvas:  A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing (Stanford University Press, 2021).

This book was written by Raj Venkatesan, a professor of business administration at the University of Virginia's Darden Graduate School of Business Administration, and Jim Lecinski, a clinical associate professor of marketing at Northwestern University's Kellogg School of Management.

What the Book Covers

The AI Marketing Canvas includes four major sections.

Part 1 (Chapters 1-3) - This section lays out the authors' point of view on the importance of artificial intelligence in marketing and provides an overview of the book's content. Venkatesan and Lecinski state their position on AI in explicit terms at the beginning of Chapter 2. They write:

"In this new economy . . . we believe there is one way and one way only to win, and that is with AI and machine learning - developed against a rock-solid marketing strategy . . . These strategies also need to be driven by marketing leaders whose obsession is to find ways to use AI and machine learning to personalize the customer relationship at every juncture." (Emphasis in original)

Part 2 (Chapters 4-6) - Chapter 4 discusses the emergence and power of "network" business models (e.g. Amazon, Google) that are enabled by technology platforms. In Chapter 5, the authors describe their four-stage customer relationship model (acquisition - retention - growth - advocacy), and they review the three "waves" of marketing (mass-segmented marketing, data-driven marketing and one-to-one personalized marketing). Chapter 6 explains some of the basic concepts and uses of artificial intelligence and machine learning.

Part 3 (Chapters 7-13) - This part contains the core of the book's content. In Chapter 7, the authors introduce the AI marketing canvas, and then they devote a chapter chapter to a discussion of each "stage" of the canvas framework.

The AI marketing canvas is primarily a matrix created by the four customer relationship components ("moments") and a five-stage AI maturity model that includes foundation, experimentation, expansion, transformation and monetization. According to the authors, companies that achieve a high level of success with AI in marketing will move through most of these five stages of AI maturity.

Part 4 (Chapters 14-16) - The final section of The AI Marketing Canvas provides guidance for implementing the concepts discussed in the previous portions of the book. Venkatesan and Lecinski adopt John Kotter's change management model from his 1996 book Leading Change, and they argue that changes will be needed across four organizational dimensions - people, process, culture and profit - to maximize the impact of AI in marketing.

The authors conclude their book with an unambiguous "call to action" for marketers and marketing leaders. They write:  "To be a successful marketer in the coming years, you must decide now whether you will engage and become an expert in AI marketing, or sit on the sidelines and watch the 'AI bus' pass you by - or worse, run you over."

My Take

The AI Marketing Canvas is an ambitious book that, for me, doesn't quite live up to its promise. The authors expressly state that the mission of their book is to provide ". . . a road map you can use to build an effective marketing plan - one that accounts for all that is required to effectively apply AI and machine learning to your marketing - so you can win"

Rather than a detailed "road map," the book is more like an impressionist painting than a close-up photograph. It addresses most of the important issues, but it doesn't contain enough detail to be called a "how-to" manual.

That being said, The AI Marketing Canvas provides some valuable information for marketers who are novices when it comes to artificial intelligence. For example, Chapter 6 is a good introduction to AI and machine learning, but marketers will need to learn a little more to have a good working knowledge of AI and ML.

Another particularly valuable part of the book is Chapter 8, which discusses the digital infrastructure required to collect and process the customer-related data that is necessary to feed AI applications. Providers of AI-enabled software applications typically emphasize the powerful capabilities of their solutions, but they don't talk as much about the volume or quality of data that's required for those capabilities to perform as intended.

Put simply, the output produced by an AI application will only be as good or reliable as the quality of the data it uses to generate that output. In Chapter 8 of the book, the authors emphasize that building a robust data infrastructure is the first essential step in implementing AI in marketing, and that this process doesn't end. Starbucks is one of the companies featured in The AI Marketing Canvas, and the authors note that Starbucks has been developing its data collection and AI capabilities for over ten years.

Artificial intelligence is already an integral part of marketing at many larger enterprises, and the use of AI in marketing is destined to become more widespread in the near future. The AI Marketing Canvas is a worthwhile resource for marketers who are just starting to learn about artificial intelligence. If you already have a basic understanding of how AI works and how it can be used in marketing, other resources will be more useful.

[Book Review] Welcome to the "Fifth Paradigm" of Marketing

Source:  Amazon

Yesterday, a Google search using the term future of marketing returned over 500,000 results, and my search was limited to the past year. Clearly, there are an abundance of views about how marketing will evolve over the next several years.

A quick scan of the first several pages of search results revealed that many of the articles and other materials focused on a specific aspect of marketing or a particular marketing technique. It's more difficult to find content that takes a longer-term, big-picture view of the future of marketing. A new book by Raja Rajamannar, the Chief Marketing and Communications Officer at MasterCard, fills the gap.

In Quantum Marketing:  Mastering the New Marketing Mindset for Tomorrow's Consumers (HarperCollins Leadership, 2021) , Rajamannar lays out his vision for how marketing needs to evolve in the face of a deluge of emerging technologies that will change how people obtain information, communicate and live their daily lives. 

Technological developments have already driven huge changes in how marketing is practiced, but Rajamannar argues that the changes we have seen so far amount to only the tip of a massive iceberg. He writes, "The last five years have seen more change in marketing than the previous fifty. And the next five years will outpace all of them put together."

Rajamannar contends that we are standing at the precipice of the fifth paradigm of marketing, which he calls "Quantum Marketing." In this impending new era, the marketing function will have the potential to ". . . leapfrog toward astonishing levels of consumer insights, real-time interactions, and hyper-targeted, hyper-relevant consumer engagement."

But Rajamannar also argues that marketing needs a fundamental reset to take full advantage of the opportunities the era of Quantum Marketing will offer.

He notes that marketing is currently in a crisis, with a growing number of companies " . . . fragmenting the 4 Ps of marketing . . . and distributing them across multiple areas outside of marketing." He also refers to research showing that most CEOs say they have little confidence in their marketing team, and he suggests that many CEOs don't see value in marketing.

The Landscape of Quantum Marketing

Rajamannar devotes most of the book to a description of his vision of what marketing can look like in the era of Quantum Marketing. He addresses a wide range of topics, including:

• The continuing - and explosive - proliferation of customer data (Chapter 4)
• Advances in the capabilities of artificial intelligence and the rapid growth of marketing-related AI use cases (Chapter 5)
• The emergence of a slew of new technologies that will enable new ways to connect with customers (Chapter 6)
• The impact of blockchains on the marketing/advertising ecosystem (Chapter 7)
• The need for a new approach to customer loyalty (Chapter 10)
• The declining impact of traditional advertising (Chapter 11)

Importantly, Rajamannar also includes a discussion of marketing ethics, and he ends the book with a detailed description of the characteristics and skills that CMOs will need in order to succeed in the era of Quantum Marketing.

A Worthwhile Read

Quantum Marketing provides a timely and important perspective on where marketing stands today and how it needs to evolve to remain (or become) a driver of growth and competitive advantage in a rapidly-changing world.

Most of the content that marketers see and/or hear on a day-to-day basis is focused on short-term strategies and tactics. While this type of content is useful, it's important for marketers to occasionally take a step back and think about longer-term issues. Quantum Marketing provides that longer-term perspective.

I do question whether Rajamannar gives sufficient weight to the impact that privacy concerns could have on how marketing evolves over the next several years. His vision of marketing's future depends on companies having relatively unfettered access to consumer-related data. In fact, he suggests that new and emerging "connected" devices such as smart speakers, autos, home appliances and wearables will add a tsunami of data to the vast amount that already exists.

The uncertainty is:  How much of this data will be available to companies for marketing purposes? Google's decision to block third-party cookies in its Chrome browser has been widely publicized and discussed, and so has Apple's move to require user permission before allowing third-party cookies in its iOS 14.5 update released last April. According to a December report by AppsFlyer, only 46% of global iPhone users (37% of U.S. users) have opted-in to tracking.

If governments step in with new data privacy regulations, or if other private companies follow the lead of Google and Apple, some of the futuristic marketing techniques described in Quantum Marketing are less likely to be widely implemented.

Even with this caveat, however, Quantum Marketing is an important book and a worthwhile read for marketers.

Why Marketers Shouldn't Ignore "Out-of-Market" Prospects

If you've ever visited California wine country, you may have fantasized about owning a vineyard. Acres of trellised grapevines laid out in neat rows create an idyllic landscape, like the one shown in the above photograph.

Of course, the reality is that operating a vineyard is hard work. And some of that work must be done long before the vineyard owner receives a payoff.

For example, it typically takes three years for newly-planted grapevines to produce a useable harvest. During those three years, the vineyard owner must install a trellis system to support the vines as they grow, and young vines must be regularly pruned and "trained" to grow correctly. They must also be judiciously watered, occasionally fertilized and constantly protected from harmful insects. And all of this work must be done before the vines produce the first dollar of revenue for the vineyard owner.

Some of you may be wondering what this brief foray into grape horticulture and vineyard management has to do with B2B marketing. Quite a bit actually, particularly for B2B marketing leaders who need to develop marketing strategies and programs that will produce sustained short-term and long-term revenue growth.

To generate maximum revenue growth over an extended period of time, marketing leaders must design programs that will maximize performance in the present, while simultaneously investing in programs that will lay the foundation for success in the future.

The Challenge of Out-of-Market Prospects

So what does this mean in practice? The starting point is a broad definition of the market. As I wrote in a recent post, identifying all potential growth opportunities is far less likely to occur when marketing and other business leaders fail to take an expansive view of their market.

In B2B, a company's "market" should be defined to include all organizations located in the company's service area that could derive substantial benefits and earn an attractive ROI by purchasing and using the company's product or service. When the market is defined in this way - that is, by customer "fit" - it will include almost all of the prospective customers the company can potentially earn revenue from.

At any given point in time, however, most of the organizations comprising a company's market are not considering the purchase of a solution like the one the company offers. Many veteran marketing and sales professionals call this circumstance the "95-5" rule, meaning that at any point in time, 95% of the company's potential customers are "out-of-market," while only 5% are actively "in-market."*

Based on our definition of the market, out-of-market organizations are a good fit for the company's product or service, but these prospects are not ready to begin a buying process. And it's unlikely that typical demand generation programs will persuade them to change their position. 

However, many potential customers that are out-of-market in the present are likely to be in-market at some point in the future. So, out-of-market prospects are like those young grapevines in a vineyard. They aren't productive today, but if handled properly, they can be productive in the future.

The issue for marketing leaders is what marketing programs, if any, should be used with out-of-market prospects. There are currently two major schools of thought regarding this issue.

In This Corner . . .

Some marketing practitioners, agencies and consultants argue that marketers should use intent data and predictive analytics to determine when an organization is likely to be in-market, and then focus marketing efforts on those prospects. Not surprisingly, this approach has been loudly advocated by firms that sell intent data and/or predictive analytics technologies.

Most proponents of this approach don't explicitly say that marketers should ignore out-of-market prospects, but some come pretty close. Consider, for example, this blog post passage from a firm operating in the intent data/predictive analytics space:

"To avoid wasting time and money pursuing prospects that either already just bought the product from your competitor or are not serious about buying yet, your team should focus on the right people, targeting them at the right time by leveraging intent data, which will help you understand total active demand. Instead of a broad market of generic buyer personas, it enables you to find specific accounts that are active in your market."

And In This Corner . . .

Other marketing practitioners, agencies and consultants contend that companies should reach out to all organizations that are a good fit for the company's product or service regardless of whether those prospects are currently in-market. The proponents of this approach typically stress the importance of brand building to long-term revenue growth.

My Take

I'm not aware of any rigorous research study that compares the effectiveness of these two approaches. The analysis performed by Les Binet and Peter Field in 2019 comes close, but Binet and Field expressly acknowledged that their findings should be viewed as tentative.**

Despite the limited amount of direct evidence, I contend that it would be risky for most B2B companies to ignore prospects that don't make the in-market cut. Such an approach is dangerous because it fails to account for an important aspect of how business buyers make purchase decisions.

The conventional view is that a B2B buying process begins when a company's leaders or managers recognize a need or a problem and decide to do something about it. These "buyers" then gather information about the need or problem, evaluate possible solutions and may or may not decide to buy a product or service to address the need or problem. So the traditional view of B2B buying is that information gathering, learning and evaluation all occur after an intentional buying process is underway.

But business decision makers rarely begin a buying process with a clean slate. Every day, they are forming impressions of companies, brands and products from touch points like ads, content resources, news reports and conversations with business colleagues and friends. 

When something triggers an intentional buying process, these accumulated impressions exert significant influence on the purchase decision. For example, a 2020 study by The B2B Institute and GWI found that millennial business buyers, ". . . spend the most time on research, explore the widest range of vendors, and yet are the most likely to ultimately pick one that they already know."

If marketers focus their efforts solely on in-market prospects, they'll be abandoning the opportunity to influence the perceptions and preferences of many future potential buyers and likely missing out on future growth opportunities. Such an approach would be like a vineyard owner failing to properly nurture the young grapevines that will drive the vineyard's future revenues.

* The percentages in the 95-5 rule are not meant to be taken literally. The actual percentages of out-of-market vs. in-market prospects will vary from industry to industry and company to company. What makes the rule valid in a general sense is that companies almost always have far more out-of-market prospects than in-market prospects.

** It would be very hard to design and conduct a study of this issue that is scientifically sound because of the difficulty of controlling for all the variables that could affect the research outcomes and because the study would need to be conducted over an extended period of time.

Image courtesy of Aaron Logan via Flickr (CC). 

Senior Marketers Say Analytics Isn't Meeting Expectations

 

Using data to inform marketing decisions is widely seen as critical for marketing success. But recent research has found that most senior marketing leaders are disappointed with the results they've obtained from their analytics investments. Read on to learn what senior marketers are saying about the unfulfilled promise of marketing analytics.

Marketers have been using data to support their activities and programs for decades. And the volume of data available to marketers has exploded in recent years because of the exponential growth of online communications and commerce.

Marketers have recognized that this vast sea of data has the potential to provide insights about existing and prospective customers that can be used to improve the effectiveness of their strategies, activities, and programs. As a result, many marketers have made substantial investments in data acquisition and analytics capabilities.

The Unfulfilled Promise

Despite the increased attention and investment, recent research has shown that most senior marketing leaders aren't satisfied with the results they've obtained from their investments in marketing analytics.

The Marketing Data and Analytics Survey 2020 by Gartner Research was a survey of over 400 marketers. The survey participants were split fairly evenly between producers (those who provide marketing analytics) and consumers (those who receive marketing analytics output). Forty-nine percent of the respondents were in North America, and 51% were in Western Europe. More than 80% of the respondents were with organizations having $1 billion or more in annual revenue. Therefore, this research reflects the perspectives of large enterprise marketers.

In the Gartner survey, a majority of senior marketing leaders - CMOs and VPs of marketing - were unimpressed with the results they've received from their marketing analytics efforts. Fifty-four percent of the senior marketing respondents said that marketing analytics had not produced the impact on their organization that they had expected. The survey also found that analytics only influences 54% of marketing decisions (on average).

The Gartner survey results echo the findings of the February 2020 edition of The CMO Survey sponsored by Duke University's Fuqua School of Business, the American Marketing Association, and Deloitte. In this research, survey respondents indicated that marketing analytics was used in marketing decision making only 37.7% of the time. That was down from 43.5% in the February 2019 edition of the survey.

The CMO Survey also found that marketing analytics has only had a modest impact on company performance. The survey asked participants to rate the contribution of marketing analytics to company performance using a 7-point scale (1=not at all and 7=very highly). In the February 2020 survey, respondents rated the contribution of marketing analytics at 3.9. The same question has been asked in each edition of the survey since at least August 2012, and the rating has not varied significantly over that entire period.

Why Analytics Isn't Meeting Expectations

Gartner's research also sought to identify why marketing analytics isn't meeting marketer expectations. The survey asked participants why they don't use analytics to support marketing decisions. The following table shows the top four reasons identified by the survey respondents.

Of all the reasons shown in this table, I find the first one to be the most interesting. Apparently, some marketing leaders don't use the output of marketing analytics to support decisions when the output conflicts with their intended course of action.

It would be easy to describe this reason as a classic example of confirmation bias. Marketing leaders seek information that will justify the action they've already decided to take, and they ignore any contradictory information.

In fairness, though, more is probably behind this reason. If a marketing leader perceives that the output of marketing analytics is based on poor quality data, or if the output doesn't provide a clear recommendation or actionable insights, he or she may feel justified in ignoring that output.

There's no doubt that data and analytics are increasingly important for marketing success. Unfortunately, these tools - like many other marketing technologies and techniques - have been hyped by vendors and industry pundits, and that hype has contributed to unreasonable expectations. Marketing leaders need to have a realistic view of what data and analytics can and can't do. That's the topic I'll address in my next post.

Top image courtesy of Petr Sejba (www.moneytoplist.com). CC

The Promise and Perils of Focusing on "In-Market" Prospects

Intent data and predictive analytics have been hot topics in B2B marketing circles for the past few years. Simply put, intent data is information collected about the online activities of a person with the goal of using that data to identify or predict purchase intent. To make this prediction, intent data is processed using a software application with predictive analytics functionality.

Some providers of intent data and/or predictive analytics capabilities have been rather effusive in describing the benefits of their solutions. Consider, for example, these two blog post passages from firms operating in the intent data/predictive analytics space:

    "To avoid wasting time and money pursuing prospects that either already just bought the product from your competitor or are not serious about buying yet, your team should focus on the right people, targeting them at the right time by leveraging intent data, which will help you understand total active demand. Instead of a broad market of generic buyer personas, it enables you to find specific accounts that are active in your market."

    "The opportunity represented by intent data is obvious:  find in-market buyers before they enter the funnel by tracking their online behavior and content consumption on different websites. Get enough of a head start and you can land a deal before they even consider your competition, shorten your sales cycle, and cut your customer acquisition costs."

This is heady stuff because the ability to know which prospects are engaged in an active buying process could enable fundamental changes in the practice of B2B marketing.

The Promise

For example, suppose that your company has implemented account-based marketing. With intent data and predictive analytics, you could select ABM target accounts based on both fit (how well a prospect matches your ideal customer profile) and interest (whether a prospect is "in-market"). You could also frequently update your list of target accounts so that you have a near real-time view of which accounts are engaged in an active buying process.

This sounds like marketing nirvana, right? When you know which of your prospects are in-market, you can focus your marketing programs on this "low-hanging fruit," which should result in higher conversion rates, increased marketing efficiency, and as the blog passage says, lower customer acquisition costs.

The Perils

It's clear that some companies can reap substantial benefits from using intent data and predictive analytics in their marketing efforts. But intent data still has some important limitations that marketers need to understand. Those limitations have been widely discussed in articles and blog posts. For example:

• What Is Intent Data & How Can You Use It?
• How to Assess the Relevancy of 3rd Party Intent Data
• Intent Data is Great. Except When it Isn't

Using intent data and predictive analytics to focus marketing efforts on in-market prospects also presents a broader hazard. If taken to the extreme, it can lead marketers to ignore prospects that don't make the in-market cut. This is dangerous because it disregards an important aspect of how business buyers make buying decisions.

The conventional view is that a B2B buying process begins when a company's leaders or managers recognize a need or a problem and decide to do something about it. These "buyers" then gather information about the need or problem, evaluate possible solutions, and may or may not decide to buy a product or service to address the need or problem. So our traditional view of B2B buying is that information gathering, learning, and evaluation all occur after an intentional buying process is underway.

But business decision makers rarely begin a buying process with a clean slate. Every day, they are forming impressions of companies, brands, and products from touch points like ads, content resources, news reports, and conversations with business colleagues and friends. When something triggers an intentional buying process, these accumulated impressions become pivotal because they shape the initial consideration set.

The initial consideration set contains those companies and/or solutions that business decision makers immediately think of when they're faced with the potential need to buy something. And being included in the initial consideration set really matters. Research by McKinsey in the B2C space has found that brands in the initial consideration set can be up to three times more likely to be purchased than brands that aren't in it.

I suspect the impact is slightly less in B2B, but being part of the initial consideration set is still important because it all but guarantees that your solution will be one of those evaluated in the formal buying process. And, you have to be invited to the party before you can be asked to dance.

The importance of being in the initial consideration set explains why it would be a mistake for most B2B companies to focus their marketing efforts exclusively on in-market prospects.

At any given moment in time, a large majority of your most attractive prospects - those with high potential value and good fit - will not be engaged in an active buying process and would not qualify as being "in-market." These attractive prospects may not be likely to buy in the near term, but that doesn't mean they are unlikely to buy in the longer term.

If you focus your marketing efforts solely on in-market prospects, you'll be abandoning the opportunity to influence the perceptions and preferences of high-value future buyers.

Image courtesy of Fertile Ground via Flickr CC.

The Benefits and Limitations of Look-Alike Modeling

Demand Gen Report recently published a white paper describing the benefits of using look-alike modeling powered by artificial intelligence (AI) to improve lead generation performance. The white paper argues that B2B marketers can use "AI-fueled" look-alike modeling to get more qualified leads that convert at higher rates.

The principles underlying look-alike modeling aren't new. For years, astute B2B marketers have been identifying important attributes of their best existing customers and using those attributes to create a profile of their "ideal prospect." Then, they would use this ideal prospect profile to identify target audiences for outbound lead generation programs and otherwise guide lead generation efforts.

The current incarnation of look-alike modeling does essentially the same thing, but in a more sophisticated way using AI-powered data analytics.

Several technology providers now offer solutions that include or support look-alike modeling, and most of these solutions take similar approaches to the look-alike modeling process.

• They extract data regarding a company's existing customers from the company's internal technology systems including, but not necessarily limited to, the CRM and marketing automation solutions.
• Most solution providers have developed or obtained access to extensive databases regarding business organizations. The modeling solution will combine the company's internal customer data with any additional data regarding these customers in the provider's database. This enables the solution to create a more detailed picture of the attributes of the company's existing customers.
• The modeling solution then uses an algorithm to analyze the combination of internal and external customer data to identify the attributes that the company's existing customers have in common. The result of this analysis is usually called a customer data model.
• The solution then runs the company's customer data model against the provider's database of businesses to identify companies that resemble the model.

The major advantage of AI-powered look-alike modeling is that it incorporates far more data points than humans can realistically use when the process is done manually. Therefore, AI-powered modeling enables marketers to build a richer and deeper customer data model, and it does a better job of identifying companies that are likely to be good prospects.

Look-alike modeling can be an effective tool for improving B2B demand generation performance, but like any business tool or methodology, it has some limitations.

First, for look-alike modeling to be effective, a company needs to have enough existing customers to build a customer data model that's reliably predictive. One provider of look-alike modeling has indicated that a company needs at least 500 existing customers to build a reliable model. While 500 may not the the absolute minimum, effective look-alike modeling does require a company to have a substantial number of existing customers, and a start-up or young business may not be able to meet this requirement.

Second, look-alike modeling can be less effective when a company is marketing new products or services. If a new product or service appeals to a different type of customer than the company's other products or services, a customer data model based on the company's existing customers may not identify the right prospects for the new product or service.

The important point here is that look-alike modeling is a powerful tool for improving demand generation performance, particularly when it's enhanced with artificial intelligence. But B2B marketers should also remember that like any business methodology, look-alike modeling has a few important limitations.

Image Source:  Flickr.com

Why You Need to Be Careful With One Feature of the New Demand Waterfall

The new SiriusDecisions Demand Unit Waterfall has received lots of accolades since its introduction this spring, and the accolades are richly deserved. But one of the new demand stages should be labeled Use With Caution.

In May, SiriusDecisions unveiled the latest iteration of it venerable Demand Waterfall with great fanfare. SiriusDecisions calls the new version the Demand Unit Waterfall, and it's depicted in the following diagram:

Source:  SiriusDecisions

The first version of the Demand Waterfall was introduced in 2006, and over the past decade, thousands of B2B companies have used the waterfall model to track and manage their demand generation efforts. So it shouldn't be surprising that the introduction of the Demand Unit Waterfall has generated quite a bit of interest in the B2B marketing world. Here's a small sample of the reactions from pundits and practitioners:

• What's The Impact Of The New Demand Unit Waterfall? Hear From 30 B2B Experts (Demand Gen Report)
• How We've Implemented (Parts of) The Demand Unit Waterfall for ABM (Tony Yang, Mintigo)
• Diving into the new SiriusDecisions Demand Unit Waterfall (John Steinert, MarTech Today)
• SiriusDecisions:  The New Demand Unit Waterfall (John Common, Intelligent Demand)

For an in-depth discussion of the new Demand Unit Waterfall, I recommend that you watch the recording of this SiriusDecisions webinar.

The early reaction to the Demand Unit Waterfall has been overwhelmingly positive, and I agree with those who say that it more accurately reflects the realities of B2B demand generation.

• By focusing on demand units, the new waterfall recognizes that most B2B buying decisions are made by groups of people, not by individuals.
• By eliminating the waterfall stages that focused on the sources of individual leads and on the "ownership" of demand generation activities, the new waterfall implicitly recognizes that demand generation has become a team sport that involves marketing, business development, and sales throughout the whole process.

Use Caution With "Active Demand"

My biggest reservation about the new Demand Unit Waterfall is the possible implications of the Active Demand stage. SiriusDecisions defines Active Demand as the demand units that are showing evidence of acute need or buying intention. In other words, Active Demand refers to demand units that are currently "in-market" for the type of solution you sell.

SiriusDecisions analysts are suggesting that companies should focus their outbound demand generation activities on in-market prospects. Not surprisingly, this is the approach also advocated by many providers of B2B predictive analytics software.

This approach may work for some types of B2B companies, but it won't work for all. Here's why.

The identification of in-market prospects relies heavily on the use of intent data, which is data regarding the online behaviors of potential buyers. Intent data - particularly third-party intent data - can be valuable for some types of B2B companies, but it can be almost useless for others. To learn more about the limitations of intent data, take a look at this post by Jingcong Zhao at the Marketo blog, this post by Todd Berkowitz at the Gartner blog, and this white paper by Infer.

The bottom line is, business and marketing leaders should be cautious about relying on their ability to accurately identify in-market buyers. And they should be particularly cautious about focusing all - or even most - of their marketing efforts on such buyers. I've discussed this issue in two earlier posts. If you'd like to see my view on this issue, take a look at Why B2B Marketers Need to Care About "Casual Learning" and Why Marketers Shouldn't Go All In on In-Market Buyers.

Have We Really Improved Marketing Productivity?

The recent pace of change in B2B marketing has been nothing short of breathtaking. Over the past 10-15 years, new marketing technologies, channels, and techniques have appeared in rapid succession, and many of these innovations are now in widespread use. B2B marketing automation, content marketing, inbound marketing, and social media marketing are just of few of the technologies and techniques that have changed B2B marketing over the past decade or so.

By all indications, the pace of change is not slowing. During the past couple of years, many B2B companies have adopted account-based marketing, and many have begun using predictive marketing analytics technologies to support ABM and other marketing efforts. And just within the past few months, we've started to hear that machine learning and artificial intelligence will have a major impact on B2B marketing in the near future.

All of these innovations have promised to improve marketing effectiveness and efficiency, and numerous research studies purport to show that they are delivering a wide range of benefits. But have these innovations really improved the bottom-line productivity of B2B marketing? Can we show - in a credible and convincing way - that B2B marketing is more financially productive today than it was 10 or 15 years ago?

Obviously, these questions must be answered on a company-by-company basis. Some B2B marketers may be able to show that their marketing efforts have become significantly more productive over the past several years. But there is evidence suggesting that some aspects of B2B marketing performance haven't improved as much as we might have anticipated.

One indicator of B2B marketing and sales productivity is the efficiency of the demand generation process. Efficiency is usually measured by the percentage of potential buyers or leads who are "converting" from one lead stage to the next.

Many B2B companies use the Demand Waterfall model developed by SiriusDecisions to describe the stages of the lead-to-revenue process, and from time to time, SiriusDecisions publishes "average" and "best-in-class" conversion rates that link to the Demand Waterfall. The following table shows the conversion rates reported by SiriusDecisions for 2008 and 2014:

What is most striking about this data is that it indicates there was essentially no improvement in conversion rates - particularly the overall lead-to-revenue conversion rate - between 2008 and 2014.

The 2008 conversion rates largely reflect marketing productivity before many of the marketing innovations mentioned above had become widely adopted. But research has shown that by 2014, a significant number of companies were using these technologies and techniques.

• 60% of large companies (more than $500 million in annual revenue) had acquired a marketing automation solution. (Raab Associates)
• 93% of B2B marketers were using content marketing in some form. (Content Marketing Institute/MarketingProfs)
• 86% of companies were running inbound marketing programs. (Hubspot)

Of course, lead conversion rates aren't the only relevant measure of marketing productivity, and there may be a reasonable explanation for the lack of improvement shown in the SiriusDecisions data. For example, the 2014 conversion rates would not have captured the impact of the shift to account-based marketing that's occurred over the past couple of years. Nevertheless, this data should be a wake-up call for B2B marketers.

Senior company leaders are increasingly expecting marketers to demonstrate that their activities and programs are creating economic value for the enterprise and improving enterprise financial performance. Many senior leaders are no longer satisfied with the tactical performance indicators (campaign response rates, content downloads, etc.) that marketers have traditionally used to describe marketing performance. What senior business leaders really want to see is proof that marketing is delivering financial results and that the dollars they are investing in marketing are being spent as efficiently as possible.

The important point here is that the value of any marketing technology or method must ultimately be judged by whether its use improves marketing productivity. So that's what marketers must be prepared to demonstrate.

Top image courtesy of Kelly Teague via Flickr CC.