Sunday, September 27, 2015

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.

Sunday, September 20, 2015

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

Sunday, September 13, 2015

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

Sunday, September 6, 2015

It's Time to Focus On Content Marketing Efficiency

One of the best-known aphorisms in business is that effectiveness is about doing the right things, while efficiency is about doing things right. Effectiveness and efficiency are the yin and yang of high performance, and both are essential to producing superior long-term business results.

For the past several years, marketers have been working to improve both the effectiveness and efficiency of marketing activities and programs. When it comes to content marketing, however, most of the attention has been given to the effectiveness component of the performance equation. Marketers have been primarily focused on dealing with issues like:

  • What attributes must our content have to create meaningful engagement with our customers and prospects?
  • What content formats will be most effective with our target audiences?
  • What channels will best enable us to reach our potential customers?
Because content marketing is a relatively new practice for many companies, it's understandable that marketers have concentrated mainly on making their content marketing effective, and have been somewhat less concerned about content marketing efficiency. Doing the right things should take priority over doing things right, particularly when the activity or process is new and the requirements for success are not fully understood.

But now, B2B companies are making huge investments in content marketing, and the time has come for marketers to focus on making their content marketing activities as efficient as possible.

A new research study by Gleanster and Kapost reveals the economic importance of improving the efficiency of content marketing activities and processes. The study is based on a survey of 3,408 marketing professionals in US B2B companies having 250 or more employees.

Here are some of the major findings from the Gleanster/Kapost research:
  • Large and mid-size B2B firms in the US collectively spend over $5.2 billion annually on content creation efforts.
  • Managing the overall content process is the single biggest content marketing challenge for marketers in large and mid-size B2B companies (identified by over 90% of survey respondents).
  • 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.
  • B2B companies that have invested in optimizing their content marketing efforts have marketing cycle times that are 240% faster than average firms.
  • The average large/mid-size B2B company spends and extra $120,000 per year on internal marketing personnel to produce the same volume of content as a firm that has invested in optimizing its content marketing operations.
  • $0.25 of every dollar spend on content marketing in an average large/mid-size B2B company is wasted on inefficient content marketing operations.
The findings of the Gleanster/Kapost research are both provocative and compelling. Even if the economic estimates are only reasonably accurate, they clearly show that virtually all B2B companies could realize significant benefits by improving the efficiency of their content marketing operations.

Illustration courtesy Flickr CC and Darryl Moran