The Growing Influence of Compelling Thought Leadership

Business buyers broadly agree that thought leadership content impacts their purchase decisions at every stage of the buying process. And recent research by Edelman and LinkedIn shows that the influence of thought leadership is growing. The research also shows, however, that thought leadership is a double-edged sword.

In November of last year, Edelman and LinkedIn conducted a survey (the "2018 survey") of U.S. business decision makers and purchase influencers to measure the impact of thought leadership content on B2B purchase decisions. The survey produced 1,201 responses from individuals representing a wide range of industries and company sizes. In this research, Edelman and LinkedIn asked survey participants several questions about how they use thought leadership content and how it affects their purchase decisions.

Edelman and LinkedIn conducted a survey in October and November of 2017 (the "2017 survey") that addressed many of the same issues. This type of "longitudinal" research can be particularly valuable because it reveals the degree and direction of change that has occurred from one survey to the next.

Several findings of the Edelman/LinkedIn research clearly demonstrate that the influence of thought leadership in the B2B purchasing process is growing. The following table shows how respondents in the 2018 survey and the 2017 survey described their consumption of thought leadership content and its impact on how they make purchase decisions.

These findings clearly show that B2B decision makers are spending more time engaging with thought leadership content and that the impact of such content is growing across the entire B2B buying journey.

The 2018 survey also revealed that thought leadership is a classic double-edged sword. For example:

• 75% of survey respondents said they will start following a writer or organization based on their thought leadership, and 60% said they will stop following a writer or organization because of poor thought leadership.
• 92% of respondents said thought leadership has increased their respect for an organization, and 46% said that poor thought leadership has decreased their respect for a company.
• 58% of respondents said they have decided to award business to a company because of its thought leadership, and 29% said they have decided not to award business to a company because of poor thought leadership.

These findings are particularly important given how business decision makers evaluate the quality of the thought leadership content they encounter. The following table shows how respondents in the 2018 survey and the 2017 survey rated the overall quality of thought leadership:

As the table shows, B2B decision makers don't believe the quality of thought leadership has improved since 2017. While the percentage of excellent or very good thought leadership increased (18% in 2018 vs. 14% in 2017), the percentage of good content decreased (53% in 2018 vs. 60% in 2017), and the percentage of mediocre to very poor content increased (30% in 2018 vs. 26% in 2017).

The most important lesson for B2B marketers in the Edelman/LinkedIn research is this:  There is no safe middle ground when it comes to thought leadership. Great thought leadership makes a positive impact on buyers at every stage of the buying process, but poor thought leadership results in lost business opportunities.

Image courtesy of Affen Ajlfe (www.modup.net) 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

B2B Highlights From the Latest CMO Survey

The findings of the latest CMO Survey by Duke University's Fuqua School of Business, the American Marketing Association, and Deloitte were published a few days ago. The latest results are based on responses from 323 marketing leaders at U.S. B2B and B2C companies. Sixty-eight percent of the respondents were affiliated with B2B companies, and 97% were at VP-level or above.

The CMO Survey is conducted semi-annually, and it's a valuable resource for capturing the views of U.S. marketing leaders regarding the overall economic and competitive environment, and major trends in marketing. In addition to overall results, survey findings are reported by four economic sectors - B2B product companies, B2B services companies, B2C product companies, and B2C services companies.

In this post, I'll discuss some of the major findings in the February 2019 edition of survey. Unless otherwise indicated, the results discussed in this post are based exclusively on the responses of B2B marketers.

Driving Growth is the #1 Challenge

Driving business growth has become the top challenge for marketing leaders in all types of businesses. The CMO Survey asked participants to rank their three biggest challenges in order of importance. The following table shows the top four challenges identified by the entire survey panel and by B2B respondents. The table shows the percentage of respondents who ranked each challenge first in importance.

Measuring Marketing Impact Remains Challenging

Demonstrating the financial impact of marketing activities remains a significant challenge for many marketers. As the above table shows, surveyed marketing leaders identified proving ROI of marketing activities as their fourth highest rated challenge.

The CMO Survey also asked marketing leaders how they demonstrate the long-term impact of marketing spending, and the following table shows how B2B marketers responded.

As this table shows, only 35.4% of respondents from B2B services companies, and 24.0% of respondents from B2B product companies say they can prove the long-term impact of marketing quantitatively.

Given these findings, it shouldn't be surprising that B2B marketers ranked marketing impact as their top C-suite communication challenge. Seventy-three percent of respondents from B2B services companies, and 67.3% of respondents from B2B product companies said demonstrating the impact of marketing actions on financial outcomes is challenging when they are communicating with other C-level executives.

Mixed Signals on Marketing Analytics and AI

Analytics, artificial intelligence, and machine learning have been hot topics in B2B marketing circles for the past few years. The findings of The CMO Survey provide important insights about several aspects of these topics.

B2B marketers expect to increase their spending on marketing analytics over the next few years. Respondents from B2B product companies said they are currently spending 6.6% of their marketing budget on analytics, and they expect the percentage to increase to 11.6% over the next three years. Respondents from B2B services companies currently devote 7.3% of their budget to analytics, and they expect the percentage to grow to 13.3% by 2022.

The CMO Survey findings also show that despite their commitment to analytics, B2B marketers don't believe that analytics currently have a big impact on company performance. On a 7-point scale (where 1 = no impact, and 7 = a high impact), respondents from B2B product companies gave analytics a rating of 3.7, and respondents from B2B services companies rated the impact of analytics at 3.8.

The survey also found that the use of artificial intelligence in B2B companies is still relatively limited. The survey asked B2B marketers how they are using AI in their marketing activities. The following table shows the top six activities identified by B2B survey respondents.

As the table shows, a majority of B2B marketers report using artificial intelligence for content personalization, and about half say they are using AI to gain customer insights. Less than 40% of marketers are using AI for most of the remaining activities.

Top image source:  The CMO Survey (www.cmosurvey.org).

What's Required for Effective Demand Generation

CSO Insights (a division of Miller Heiman Group) recently published its 2018-2019 Sales Performance Report. This report describes the findings of the 2018-2019 sales performance survey, which generated responses from nearly 900 global sales leaders.

Sixty-one percent of the respondents were either executive managers or senior sales managers, and respondents represented 23 industries. Half of the respondents (50.8%) were located in North America, and the balance were based in EMEA, APAC, and Latin America.

The CSO Insights study focused specifically on the performance of the sales function, but the survey findings provide valuable insights for everyone involved with B2B demand generation. That's because many of the factors that characterize successful sales performance also apply to the other business functions that play important roles in demand generation.

The Defining Attributes of High Performance

CSO Insights identified three defining attributes of high-performing sales organizations. The study found that respondents from top-performing organizations were more likely than other respondents:

• To say their company has a customer-centric culture
• To report they have a high level of alignment between their sales process and customers' decision-making journey
• To say they are confident in the ability of their sales reps to provide valuable insights and perspectives to potential buyers

These characteristics can be extended and applied to the other business functions involved in demand generation. For example, a marketing organization that excels at demand generation is more likely to:

• Be part of a company with a customer-centric culture
• Align its programs and messaging with the customer buying journey
• Create and use content that provides valuable insights to potential buyers

The authors of the survey report acknowledge this point when they write:  "Looking at all three of these characteristics together shows that . . . [high-performing] organizations are embracing 'customer experience' as a broad concept, of which sales process and salespeople are just one piece."

Lead Generation Needs Significant Improvement

The CSO Insights research highlights several areas where better cross-functional collaboration is needed to improve demand generation performance. One of those areas is lead generation. Survey respondents identified improving lead generation as one of their four primary objectives for the coming 12 months, and they also identified the inability to generate enough qualified leads as the second most significant barrier to achieving demand generation success.

Unfortunately, the CSO Insights report reveals a distressing lack of alignment between sales and marketing when it comes to lead generation. For example, only 29.5% of the survey respondents said their sales and marketing teams have an agreed upon, formal definition of who is a legitimate sales lead. And the level of lead definition alignment between sales and marketing has actually gotten worse since 2014, as the following chart shows.

The low level of sales-marketing alignment also shows up in lead nurturing. Only 33.9% of the survey respondents said their sales and marketing teams have an agreed upon, formal process for nurturing leads. Another 30.8% said they have an informal process - whatever that means.

The CSO Insights research provides more compelling evidence that effective B2B demand generation requires a coordinated effort by both sales and marketing, and that sales-marketing alignment is still very much a work-in-progress.

Top Image Source:  CSO Insights (a Division of Miller Heiman Group).

Where Customer Experience Stands in 2019

Two recently-published reports paint a decidedly mixed picture of the current state of customer experience (CX) management. Most business leaders now recognize that providing great customer experiences is a critical source of competitive advantage and a primary driver of business performance. So these two reports provide an important snapshot of how far companies have come in their efforts to improve customer experiences.

Forrester's Predictions 2019

In its Predictions 2019 report, Forrester observed that CX performance has been flat for three consecutive years. The report stated that in Forrester's Customer Experience Index research, ". . . few businesses made real gains, most continue to plateau, and some fell back."

Forrester also noted that 89% of surveyed CX professionals do not believe that the ROI of customer experience is well established in their companies. As a result, Forrester predicts that 20% of companies will abandon strategic CX initiatives in 2019 and turn to price reductions to secure short-term gains.

Forrester's report describes the outlook for customer experience management in 2019 in somewhat pessimistic terms:  "There is a strategic and structural mismatch between what CX needs to do and what CX is allowed to do or is capable of doing. 2019 will see that mismatch continue to play out."

The 2019 Global Customer Experience Benchmarking Report

The 2019 Global Customer Experience Benchmarking Report by Dimension Data provides detailed insights about the state of CX around the world. This report was based on a global survey of customer experience professionals that produced 1,114 responses. Respondents were located in 59 countries across the Americas, Asia Pacific, Europe, Australia and New Zealand, and the Middle East and Africa.

I found the overall tone of this report to be fairly pessimistic, but when I reviewed the specific survey findings, I thought they painted a somewhat more optimistic - or at least a more balanced - picture.

For example, there is a broad consensus about the importance of customer experience. Almost nine out of ten of the survey respondents (88.3%) said their organization views customer experience as a competitive differentiator.

The research also found that most companies have a defined customer experience strategy, although only a small minority of companies are measuring the ROI of their CX efforts.

• 36.3% of the respondents said they have a "high level" CX strategy that is aligned to brand positioning
• 30.9% said a CX strategy exists and is recognized as crucial to organizational strategy
• 13.3% said a clear CX strategy exists and its ROI/value is defined and measured
• Only 19.5% of the respondents said they have no formal CX strategy

More than three out of four of the survey respondents (76.7%) indicated they were fairly or very satisfied with their current customer experience capabilities, although only 10.3% said they were very satisfied. Perhaps more important, more than three-fourths of the respondents (77.1%) said their customers would rate their company's CX capabilities as 6 or higher on a 10-point scale (where 0 = poor, and 10 = excellent).

The Dimension Data research also revealed some additional areas of concern. For example, 29.5% of respondents said customer experience is seen as relevant in some business functions only, and that there is no organization-wide ownership of CX. Another 23.9% said CX is owned by individual business functions that are supported by a central CX team.

As I noted earlier, most respondents to this survey said they have some form of a company-wide CX strategy. So it appears that many companies are still leaving the execution of that strategy to individual business functions. This approach can easily produce CX efforts that are not tightly coordinated, and it can also result in customer experiences that are not completely consistent.

Overall, the Dimension Data research indicates that many companies have made substantial progress on CX, but it also shows that more work is needed to deliver consistently great customer experiences.

Image courtesy of frontriver via Flickr CC.