The Operational Challenges That Come With ABM

The interest in account-based marketing has been growing dramatically for the past few years, largely because many B2B marketers believe that ABM will outperform other approaches to marketing. Not surprisingly, there is also  a great deal of hype surrounding account-based marketing, and this hype tends to minimize some of the challenges associated with building a successful ABM program.

The reality is, moving from traditional demand generation marketing to ABM can require companies to make some significant changes in how the marketing department operates, and these changes can be challenging for many companies.

Many B2B companies - particularly larger companies - structure their marketing operations by function (e.g. demand generation, product marketing, public relations, etc.) and/or by marketing channel (e.g. email, social media, etc.). As the name implies, account-based marketing requires marketing activities to be designed for identified target accounts, which can require staff marketers to take on new or different responsibilities.

How much change is needed depends largely on the overall size of the ABM program and on what specific type or types of ABM a company is implementing. Most ABM thought leaders and practitioners now recognize three "varieties" of ABM:

• Strategic ABM involves a very small number of target accounts and is extremely resource intensive. It typically involves the use of marketing content and marketing programs that are customized for each target account.
• ABM Lite focuses on groups of identified accounts that share similar business attributes and needs. It involves a larger number of accounts, but is less resource intensive than Strategic ABM. For example, marketing content and marketing programs may be customized for segments of target accounts, but not for individual accounts.
• Programmatic ABM emphasizes the use of new technologies to apply ABM-inspired techniques to a large number of accounts. It is the least resource-intensive variety of ABM, at least in terms of human resources.

There's not a great deal of published research about how companies are implementing ABM at the operational level. However, last spring ITSMA published the results of a survey that addressed some of the operational challenges that come with ABM.

In the ITSMA survey:

• The median number of accounts included in Strategic ABM programs was 10, while the median number of accounts in ABM Lite programs was 30.
• In Strategic ABM programs, the median number of accounts per dedicated marketer was 4, and the median number of accounts per part-time marketer was 3.
• In ABM Lite programs, the median number of accounts per dedicated marketer was 9, and the median number of accounts per part-time marketer was 10.

This one survey doesn't constitute the final word on the subject, but it does provide an indication of what human resources are required to run a successful ABM program. Suppose, for example that your company is planning to launch an ABM program that will have 10 Strategic ABM accounts and 30 ABM Lite accounts. Based on the ITSMA data, you will probably need to assign 2-3 full-time marketers to your Strategic ABM program and about 3 full-time marketers to your ABM Lite program.

Whether or not these numbers are exactly right, the important point here is that successful ABM requires a significant commitment of human resources. Much of the hype surrounding ABM has focused on how technology can enable companies to scale ABM efforts. While the right technology tools can automate certain aspects of ABM, it's critical to remember that effective ABM still needs a substantial amount of human time and skill. Therefore, marketing leaders need to think through what human capabilities will be required for their ABM program and then create a plan for supplying those capabilities.

Illustration courtesy of Till Westermayer via Flickr CC.

The Promise and Peril of Personalization at Scale

Marketers in virtually all kinds of companies are now intensely focused on improving the quality of the customer experiences their companies provide. Most marketers believe that the ability to personalize marketing offers and messages for individual customers at every touch point is critical to delivering outstanding customer experiences.

Numerous research studies have shown that personalized marketing can be highly effective. For example, in a 2016 survey of more than 1,500 US and UK consumers by Accenture Interactive, almost two-thirds (65%) of respondents said they are more likely to make a purchase from a retailer that sends them relevant and personalized offers. But there are also many well-documented examples of personalized marketing efforts that have failed miserably because they were clumsily executed, or because they were seen as a "creepy" invasion of privacy.

The ability to "personalize at scale" is highly dependent on data and technology. Many companies are already using data and predictive analytics technologies to automatically generate personalized marketing messages without human involvement, and many marketers believe that this practice will only become more commonplace. In fact, it's difficult to envision how personalization at scale can be achieved without a heavy reliance on data and technology.

But this creates a conundrum for marketers that I contend isn't fully appreciated. Automated, data-driven personalization will make personalization at scale more achievable, but it also increases the danger of getting personalization wrong.

An article by Charles Duhigg in The New York Times Magazine provides a great illustration of both the promise and the peril of automated, data-driven personalization. In this article, Mr. Duhigg describes how personalized marketing caused an unwitting father to discover that his teenage daughter was pregnant.

When I decided to write about this topic, my intention was to briefly summarize the content of Mr. Duhigg's article, but I quickly determined that a brief summary simply would not do justice to the subject matter of the article. I strongly recommend that you take the time to read the entire article if you are involved in developing personalized marketing programs.

For me, there are three important takeaways from the article.

• First, data and predictive analytics can enable marketers to develop marketing messages and offers that are highly relevant for individual customers and prospects. 
• Second, relevance alone is not enough to ensure that a personalized marketing program will be successful. It takes sound human judgment to evaluate factors that data and analytics simply cannot capture. 
• And third, sometimes it is more effective to make marketing messages less personalized, particularly when the subject matter touches a highly personal or otherwise sensitive topic.

(Note:  Mr. Duhigg's article is fairly long. The beginning and ending portions of the article contain the material that pertains directly to personalized marketing. The middle part of the article focuses on the power of habit in human decision making. The entire article is well worth reading.)

Image courtesy of Josh Hallett via Flickr CC.

The Economics of ABM Account Selection

From all indications, account-based marketing is quickly becoming a core marketing strategy for many B2B companies. The growing popularity of ABM is largely the result of a widespread perception that it can produce a higher ROI than any other approach to marketing.

There's also a tremendous amount of hype surrounding ABM, and this hype can obscure or minimize some of the challenges associated with ABM. To put it bluntly, some of the tasks required for ABM are more complicated and/or require more work than many marketers anticipate.

One of these tasks is selecting and prioritizing ABM target accounts. Most ABM experts agree that account selection is the most critical component of any ABM program. Companies typically choose their target accounts by identifying businesses that closely resemble their existing customers, a technique that's known as look-alike modeling. The basic idea behind look-alike modeling is that companies that closely resemble your existing customers will be more likely to purchase your products or services.

It's important to recognize, however, that buying potential alone doesn't make a company an attractive account for ABM. The other necessary attribute is that the prospective target account must have the potential to be a highly profitable customer for your company. For ABM purposes, the best measure of potential profitability is customer lifetime value (CLV), which can be defined as the present value of the total profits that you expect to earn from the prospective account over the full duration of the customer relationship.

Estimating the CLV of prospective ABM target accounts is a critical part of the account selection process because of basic economics. New customers will contribute to profitable growth only if the profits they create exceed the costs you incur to acquire them. Therefore, the estimated CLV of a prospective customer establishes the ceiling for how much you should invest to acquire that customer.

The implementation of ABM may or may not require an increase in your overall demand generation budget, but your customer acquisition spending - on a per account basis - will be higher for ABM accounts. You need reasonable CLV estimates in order to effectively manage your ABM customer acquisition costs.

The CLV of prospective target accounts becomes even more important if you are implementing more than one variety of ABM. Most ABM thought leaders recognize three varieties of ABM. Strategic ABM involves a very small number of target accounts and is very resource intensive. ABM Lite focuses on groups of identified accounts that share similar business attributes and needs. It involves more accounts, but is less resource intensive than Strategic ABM. Programmatic ABM emphasizes the use of new technologies to apply ABM-inspired techniques to a large number of accounts, and it is the least resource-intensive variety of ABM.

As you might expect, the costs associated with the three varieties of ABM will differ significantly. For example, over the course of a year, you may well spend more on your Strategic ABM program - which might involve, say, 10 or so accounts - than you spend on your ABM Lite program - which might involve 50 to 100 accounts. Having a reasonably accurate estimate of the CLV of your prospective target accounts helps you place each account in the right ABM tier.

Many ABM technology solutions leverage predictive analytics to both streamline and enhance the look-alike modeling aspect of ABM account selection. Mastering the economics of ABM account selection still requires some manual work and a good deal of human judgment.

Illustration courtesy of Emillo Kuffer via Flickr CC.

Why It's Time to Reset Expectations for Content Marketing

Mark Ritson, a marketing professor at the Melbourne Business School in Australia, recently published a column at Marketing Week titled "Is content marketing a load of bollocks?" As you might expect, the column created quite a stir in the content marketing world.

Professor Ritson makes two arguments in his column. First, he contends that content marketing isn't really different from "regular" marketing communications. And second, he argues that most of the content produced by content marketers is ineffective. He writes, "A study by software firm Beckon recently revealed that although the amount of content being marketed has tripled in the past year, there has been no increase in engagement. Just 5% of the total content produced generated 90% of the customer engagement meaning that 19 out of 20 pieces of content marketing have little if any impact."

It shouldn't be surprising that most content marketing advocates don't share Professor Ritson's view. Most proponents of content marketing will readily acknowledge that content marketing efforts are often ineffective, but they argue that's because many companies aren't doing content marketing correctly. They contend that many companies are still producing bad content, or are haphazardly creating content without having a sound content marketing strategy. Supporters argue that content marketing is effective when it's done the right way.

Recent research provides support for both points of view. The Beckon study cited by Mark Ritson echos the findings of earlier research by TrackMaven, which also found that content volume is increasing while content engagement is decreasing.

On the other hand, the 2017 B2B content marketing survey by the Content Marketing Institute and MarketingProfs (published a few weeks ago) found that doing the right things in the right ways will have a major impact on content marketing success. In this research, survey respondents who rated their company's content marketing efforts as extremely or very successful were also more likely to say that their company:

• Has a documented content marketing strategy
• Is extremely or very committed to content marketing
• Is clear on what an effective/successful content marketing program looks like
• Measures content marketing ROI

The reality is, marketing leaders can do a great deal to improve their odds of achieving success with content marketing. But it's equally true that the ability of any company to achieve content marketing success is also affected by competitive forces that are beyond the company's control.

In a post published in January of last year, I argued that content marketing success would be harder to achieve in 2016 for three reasons:

• The amount of content available to potential buyers has increased dramatically, and the competition for buyer attention has become more intense.
• The growing use of content marketing best practices tends to make content marketing programs look alike, which makes differentiation more difficult.
• While companies are still producing a lot of bad content, there's also a growing volume of good content available in the marketplace, which allows potential buyers to be more choosy about the content they consume. This makes it more challenging to consistently produce content that will win mindshare.

These competitive forces are more potent today than they were a year ago, which means that achieving content marketing success will only become more challenging.

It addition, increasing competition in the content marketplace means that marketing and business leaders must have realistic expectations for what content marketing can achieve. The important question for B2B marketers is:  Will a well-conceived and well-executed content marketing program be more effective at driving profitable revenue growth for my company than alternative marketing methods? For most B2B companies - particularly those that offer complex and/or expensive products or services - the answer to this question will almost certainly be yes.

The 2015 version of Gartner's Hype Cycle for Digital Marketing showed content marketing on the decline - having passed the peak of inflated expectations and beginning the slide toward the trough of disillusionment. The 2016 version of the hype cycle put content marketing even closer to the trough of disillusionment, which suggests that content marketing is in for a couple more years of rough sailing.

But what's really important is that once content marketing passes through the trough of disillusionment, it will emerge onto a slope of enlightenment and move toward the plateau of productivity where marketers will have a realistic view of what content marketing can accomplish and what is truly required to build effective content marketing programs.

Illustration courtesy of DigitalRalph via Flickr CC.