More Evidence on the Need to Improve Content Marketing Efficiency

Earlier this fall, I published a post that focused on the need to make content marketing more efficient. The impetus for that post was a recent research study by Gleanster and Kapost that demonstrated the economic importance of improving the efficiency of content marketing activities and processes. Here are three of the major findings of the Gleanster/Kapost research:

• Large and mid-size B2B firms in the US collectively spend over $5.2 billion annually on content creation efforts.
• Poorly managed and/or cumbersome content management processes lead to an estimated $958 million each year in excessive spending on content marketing by large and mid-size B2B companies.
• $0.25 of every $1.00 spent on content marketing in an average large/mid-size B2B company is wasted on inefficient content marketing operations.

This month, I attended a webinar sponsored by SAVO that provided more confirmation of the importance of content marketing efficiency. The webinar included a presentation by Erin Provey, Service Director of the Strategic Communications Management practice at SiriusDecisions. Ms. Provey's presentation was based on data from the SiriusDecisions 2015 Cost of Content Survey and the SiriusDecisions Cost of Content Benchmark Model.

The SiriusDecisions research and analysis focused on how much B2B companies are spending on content development and on how much of that content is "productive." For this analysis, SiriusDecisions divided B2B companies into three size cohorts. Small companies were defined as those having less than $100 million in annual revenues. Medium-size companies have between $100 million and $1 billion in revenues, and large companies have more than $1 billion in revenues.

SiriusDecisions estimates that small companies invest about $900,000 per year in content development, medium-size companies spend about $10.8 million, and large B2B enterprises spend about $17.5 million. These spending amounts are averages, and they include both external and internal costs.

SiriusDecisions defined productive content as content that is "activated" by internal audiences and consumed by external audiences. Unproductive content is content that isn't used because it cannot be activated "as is" or because it can't be located. Across B2B companies of all sizes, SiriusDecisions estimates that 65% of all the content "owned" by companies goes unused. More specifically, SiriusDecisions says that 28% of content isn't used because it is "unfindable," and 37% is unusable due to low quality or lack of relevance.

Because of unproductive content, SiriusDecisions estimates that between 11% and 19% of the annual investment in content is wasted. In small B2B companies, this annual wasted spending amounts to about $100,000. Medium-size companies waste about $2 million, and large B2B enterprises waste about $2.3 million. As with the total cost figures, these waste amounts are averages.

The SiriusDecisions research provides a sobering dose of reality and more compelling evidence that virtually all B2B companies can realize significant financial benefits by improving the efficiency of their content marketing efforts.

Image courtesy of Carolyn Coles via Flickr CC.

What are the Core Disciplines of Modern Marketing?

It's no secret that the marketing landscape is more complex today than ever before. Heightened buyer expectations for greater relevancy, the proliferation of customer touch points, interaction channels, and marketing technologies, and the growing role of data and data analytics have all added complexity to the marketing function.

To succeed, in today's dynamic and complex marketing environment, companies must assemble teams of marketers who have the right skills and competencies. But, what specific competencies will a high-performing, modern marketing organization possess?

Tomasz Tunguz, a partner at the venture capital firm Redpoint, addressed this issue in a recent blog post titled The 9 Marketing Disciplines of Great SaaS Companies. Mr. Tunguz' post was based on a presentation made at a venture capital conference by Bill Macaitis, the former CMO of Zendesk. Although Mr. Tunguz' post and Mr. Macaitis' presentation dealt specifically with marketing at SaaS companies, most of the disciplines are equally important for other types of B2B companies.

Here are the nine marketing disciplines that Tunguz/Macaitis identified, along with my interpretation of each discipline's primary role(s).

Operations and Analysis - The team that is responsible for performing data analytics and for leveraging analytics to optimize marketing performance. Tunguz/Macaitis say this is the first team a company should build and that it is likely to become the largest team in the marketing organization.

Customer Evangelism - This team is primarily responsible for identifying and building relationships with potential customer evangelists and for leveraging customer endorsements.

Content - This team develops the company's content marketing strategy and plan, and creates and/or curates needed marketing content.

Paid Media - This team manages the activities that involve the use of paid media channels, such as TV/radio/print ads, display ads, and SEM.

Website Conversion - Tunguz/Macaitis say this team is a group of front-end and back-end engineers who are responsible for optimizing the company's website.

Product Marketing - This is the team that is focused on understanding specific customer needs (product-related), on segmenting the market, and on developing appropriate pricing structures.

Lifecycle Nurturing - This team is primarily responsible for strengthening relationships with existing customers.

Communications - Tunguz/Macaitis say this is basically the public relations team.

International - This is the team that manages the company's marketing efforts in areas outside the "home" country.

What do you think? Does this list identify all of the core disciplines that a high-performing, modern marketing organization requires? What would you add or change?

Image courtesy of arisexpress via Flickr CC.

HubSpot Research Offers Insights on Blog Performance

In an earlier post, I discussed some of the major findings of research by TrackMaven regarding the effectiveness of blog posts. The TrackMaven research contained data regarding the best day of the week for publishing blog posts, the best time of day to publish, and the optimal length of blog post titles.

While the TrackMaven study included some data regarding posting frequency, it didn't attempt to identify what posting frequency is best, nor did TrackMaven attempt to determine what blog post length is most effective.

After my earlier post was published, I discovered a blog post by HubSpot that provides several insights on these perennially important issues. This post describes a test that HubSpot ran on its own Marketing Blog to determine what its optimal editorial strategy should be. More specifically, the managers of the blog wanted to determine whether they should be publishing longer, more in-depth posts on a less frequent basis, or shorter posts on a more frequent basis.

To answer these questions, HubSpot conducted an experiment to determine how changes in blog posting frequency and content mix affected three key performance metrics - views, net new leads, and subscribers. To get the full flavor of what HubSpot discovered from its experiment, you need to read the HubSpot post. In this post, I'll focus on the findings that relate to posting frequency.

The experiment was conducted over a period of six weeks. During the first two weeks, HubSpot tracked the results produced by its existing editorial practices (the Benchmark strategy). In the second two weeks of the test, HubSpot reduced the number of posts published by about 50% and increased the percentage of longer, more in-depth posts. HubSpot called this the Low Volume, High Comprehensiveness (LVHC) strategy. In the final two weeks, HubSpot increased the number of posts published by about 50% (over the Benchmark number) and increased the percentage of shorter, less in-depth posts. HubSpot named this the High Volume, Low Comprehensiveness (HVLC) strategy.

Here's what HubSpot found:

• The Benchmark and HVLC strategies produced almost the same amount of blog traffic, but the LVHC strategy resulted in about 32% less traffic.
• During the LVHC phase of the experiment, the blog produced about 4% fewer leads than it did when the Benchmark strategy was used. During the HVLC phase, the blog produced almost twice as many leads, compared to the Benchmark strategy.

So, what can we learn from the results of the HubSpot experiment? Most of us would like to believe that content quality will trump content quantity. The HubSpot results suggest that, for blogs anyway, content quantity (posting frequency) has a significant impact on blog performance.

Note:  As part of its experiment, HubSpot categorized its blog posts based on the type of content they contained and then measured the performance of each type of post. I found these results to be particularly interesting, and once again, I recommend that you take the time to read the HubSpot post. For me, the most important takeaway was that no single type of blog content excelled at both traffic generation and lead generation. So you need to publish several types of posts to maximize the overall performance of your blog.

Why the Quality of Your Content Marketing Strategy Matters

For the past two years, the annual content marketing surveys by the Content Marketing Institute and MarketingProfs have pointed to the importance of having and following a documented content marketing strategy. In both the 2014 and 2015 editions of the survey, a majority of B2B respondents whose company had a documented content strategy rated their content marketing efforts as highly effective (a 4 or 5 on a scale of 1 to 5), while only a small minority of respondents with a documented strategy rated their efforts as ineffective (a 1 or 2 on the five point scale).

A recent study by the CMO Council, in partnership with NetLine Corporation, contains some findings that seem to contradict the results in the CMI/MarketingProfs surveys. Lead Flow That Helps You Grow was based on a survey of 213 senior marketing leaders primarily located in North America.

In the CMO Council study, only 10% of respondents said they have no content marketing strategy. At the same time, however, only 12% of respondents described their content marketing program as a "high performance engine." More importantly, most of the respondents were not particularly happy with their ability to leverage content to generate high-quality sales leads. Only 15% of the respondents described their demand generation strategies as very or highly effective. Twenty-nine percent of the respondents rated their demand generation strategies as moderately effective, and 32% said they were somewhere in the middle.

The CMO Council also asked survey participants what was causing their content marketing programs to under-perform, and the following table shows the top factors identified by survey respondents.

These factors indicate that many of the respondents to the CMO Council survey do not, in reality, have a well-conceived and complete content marketing strategy. If you have a sound content strategy, it's not likely that you will be developing content that isn't tailored for specific audiences, or that your content isn't relevant for your target audience, or that you aren't leveraging effective distribution channels.

In an earlier post, I discussed seven high-level questions that your content marketing strategy must address. If your strategy includes thorough answers to those seven questions, most of the problems shown in the above table won't exist.

Today's business buyers are awash in content, and it takes high-quality content to be successful when content is so abundant and easily accessible. But to consistently create and deploy content that will enable you to achieve your marketing objectives, you need an effective content marketing strategy. So in essence, the quality of your strategy is just as important as the quality of your content.

Should Marketers "Go All In" on Algorithmic Marketing?

One of the most profound changes in marketing over the past two decades has been the explosive growth in the development and use of marketing technology. Today, technology touches virtually every aspect of marketing, and our use of marketing technologies continues to grow.

Big data, predictive analytics, and "data-driven marketing" are now among the hottest topics in marketing circles, and most thought leaders say that companies are only beginning to scratch the surface when it comes to using data and analytics to automate marketing. Some thought leaders envision a not-too-distant future where computer algorithms direct many of the interactions between companies and their customers or prospects without human intervention.

For example, in The Marketing Performance Blueprint, Paul Roetzer, the founder and CEO of PR 20/20, writes:

"Imagine an algorithm-based recommendation engine for all major marketing activities and strategies. The engine will use a potent mix of historical performance data, industry and company benchmarks, real-time analytics, and subjective human inputs, layered against business and campaign goals, to recommend actions with the greatest probabilities of success. If built or acquired by marketing technology heavyweights, these tools will add algorithmic marketing strategy to the automation mix."

In an article for Marketing Land, Mr. Roetzer expanded on his view of the future:

"Natural language processing, hypothesis generation and dynamic learning are core components of the technology that will transform the marketing industry. Rather than simply automating manual tasks, artificial intelligence adds a cognitive layer that infinitely expands marketers' ability to process data, identify patterns, and build intelligent strategies and content faster, cheaper and more effectively than humans."

The support for data-driven marketing is strong and growing, and many enterprises are already using data and predictive analytics to automate some interactions with customers or prospects. I contend that marketers should approach automated algorithmic marketing with a healthy dose of caution and make sure they fully understand its limitations, as well as its potential benefits.

One important limitation is that algorithmic marketing relies mostly on behavioral data. Virtually all of the data about customers and prospects that falls under the rubric of "big data" is data describing behaviors and actions - the digital footprints that we leave behind as we use digital devices and channels to consume or exchange information. As I wrote in an earlier post, the problem with behavioral data is that it can tell us what someone has done (and often when and where he or she did it), but behavioral data alone doesn't tell us why someone took a particular action or behaved in a particular way. In many cases, behavioral data reveals little about customer attitudes and motivations, and these factors play a huge role in successful marketing.

We also need to be cautious about algorithmic marketing because it can make us overconfident. The vast amount of data that we can now access and analyze, and the growing power and sophistication of predictive analytics software can easily lead us to think that algorithmic marketing is more effective and reliable than it actually is. In reality, algorithmic marketing makes us susceptible to a version of the McNamara Fallacy.

The McNamara Fallacy was named for Robert McNamara, the US Secretary of Defense during the early stages of the Vietnam War, and it relates to his approach to managing the war effort. The term was coined by the noted social scientist Daniel Yankelovich, who expressed it in the following terms:

"The first step is to measure whatever can easily be measured. This is OK as far as it goes. The second step is to disregard that which can't be easily measured or to give it an arbitrary quantitative value. This is artificial and misleading. The third step is to presume that what can't be measured easily really isn't important. This is blindness. The fourth step is to say that what can't be easily measured really doesn't exist. This is suicide."

Like all humans, we marketers have a strong tendency to base our decisions on the evidence that's easily available to us, and we tend to ignore the issue of what evidence may be missing. Psychologist Daniel Kahneman 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. My point here is that it can become easy for us to believe that the data we can track, collect, and analyze is the only thing that matters, and therefore that predictions and recommendations based on that data are inevitably correct. But, it's just not that simple or straight forward.

I'm not arguing that marketers should ignore or avoid using big data, predictive analytics, and algorithmic marketing. These tools can be immensely powerful,so the key is to use them wisely and to remember that they're neither complete nor perfect.

Image courtesy of Daniel Morrison via Flickr CC.

New Research on B2B Content Marketing Trends and Practices

This summer, Starfleet Media published The 2015 Benchmark Report on B2B Content Marketing and Lead Generation. The Starfleet report is based on a survey of high- and mid-level marketing and sales professionals that was conducted in the second quarter of this year.

The survey produced 324 qualified responses, and respondents represented B2B companies of all sizes, from very large (more than $1 billion in revenues) to very small (less than $1 million). Most of the respondents (69%) were affiliated with companies located in North America, while 22% were affiliated with European companies.

In many ways, the findings of the Starfleet survey echo the results of research from several other firms. For example:

• Almost nine out of ten respondents (89%) said their primary high-level objective for investing in content marketing is to acquire new customers.
• The top three specific objectives for content marketing identified in the survey were generate more leads (92% of respondents), raise brand visibility (90%), and generate better leads (87%).
• The top four types of content assets used in the past twelve months were case studies (67% of respondents), company-branded white papers (62%), company-branded webinars (58%), and company-branded e-books (52%).
• Almost nine out of ten respondents (86%) identified  creating compelling content as their biggest content marketing challenge.
• Companies across all industries produced or licensed an average of 5.5 new content assets over the past twelve months.

The Starfleet survey also revealed a few incongruities that are worth noting. For example, 90% of survey respondents agree or strongly agree that unbiased third-party content is generally perceived as more credible than company-branded content, while 83% agree or strongly agree that third-party content generally produces higher-quality leads. However, only 38% of respondents said their company had used research reports licensed from third parties during the past twelve months.

Starfleet also found a significant disparity in the number of content assets that companies create or use. According to the report, software providers produced or licensed an average of eight new content assets over the past twelve months, while the average for all other types of companies was only 3.5 content assets.

The Starfleet research also confirmed that B2B companies are making a substantial financial commitment to content marketing. Thirty-three percent of survey respondents said they spent more than half of their marketing budget on content marketing during the past twelve months, and more than one-third of respondents (36%) said they plan to allocate a greater portion of their marketing budgets to content marketing over the next twelve months.

Illustration courtesy of Flickr CC and TopRank Online Marketing

There's No Such Thing as "No Decision"

B2B companies that track the performance of their demand generation efforts often categorize the outcomes of potential deals as wins, losses, or no decisions. In many cases, no decision is a catch-all category that is used for all potential deals that aren't successfully closed or lost to a competitor.

Research has shown that no decisions occur frequently. For example, the 2015 Sales Performance Optimization survey by CSO Insights found that between 20% and 28% of forecast deals result in no decisions. In this research, the term forecast deals referred to sales opportunities that were sufficiently "ripe" to be included in revenue projections for a specific fiscal period. Sales Benchmark Index takes a broader view of the issue and has estimated that 58% of the typical sales pipeline will stall or result in no decision.

There are two fundamental problems with the won-lost-no decision framework, one of which is that the no decision label is inaccurate 100% of the time. When a prospective customer does not buy (either from you or from a competitor), the prospect is making a choice to either remain with the status quo, or use internal resources to "fix" the status quo in some way. Whichever the case, the prospect has made a decision, and we need to evaluate the outcome accordingly. Calling this outcome a no decision can easily lead us to view the cause of the outcome as simple inaction.

The second problem with the no decision label is that it is too generic to inform marketing and sales leaders what really caused a prospect to opt for the status quo. This is important information because it enables us to identify the causes that we can affect and to separate those from the causes that are beyond our control.

For example, a prospective customer may choose to stick with the status quo because your offering and those of your competitors don't provide enough value to justify making a change. If you're experiencing a significant number of these outcomes, it's likely that you're targeting the wrong prospects or that your lead qualification process isn't revealing the "lack of fit" as quickly as it should.

Or, you may have prospects that decide to remain with the status quo because they don't recognize the real value that your solution would deliver. In this case, your marketing content and/or your selling process may not be adequately communicating value to your prospective customers.

In both of these scenarios, you can reduce the number of what are typically called no decisions, either by using a better definition of your target market or a more rigorous lead qualification process, or by improving your ability to communicate the value that your solution will provide.

In other cases, prospective customers may decide to stick with the status quo because of events or circumstances that are beyond your control. Some examples would include:

• A change in company leadership that results in a shift of strategic priorities
• A downturn in the financial performance of the prospective customer that results in tighter controls on spending

To improve demand generation performance, you need to know why you win or lose, whether the loss is to a competing company or to the status quo. To gain this understanding, we need to recognize that there's really no such thing as no decision.

Illustration courtesy of Flickr CC and Dan Moyle