Why Account-Based Marketing Changes How We Define Marketing Success

The adoption of account-based marketing (ABM) involves fundamental changes in marketing objectives, priorities, and practices. Therefore, ABM requires a different approach to defining marketing success and different metrics for measuring marketing effectiveness. Content is vitally important to the success of any ABM program, but it plays a different role in ABM than it does in traditional demand generation. Therefore, ABM also requires marketers to think differently about what constitutes effective content and to use different standards for evaluating content performance.

ABM focuses most marketing and sales efforts on a relatively small number of high-value prospects. When a company implements ABM, it identifies a group of target accounts and the relevant "buyers" within those accounts. Because accounts and contacts are identified in advance, marketing won't be "generating" many "new" leads. So, traditional measures of lead generation and "marketing-sourced pipeline" aren't particularly useful.

ABM also demands that marketing and sales professionals adjust their expectations regarding lead conversion rates. To be successful with ABM, a company must win business from a significant percentage of its target accounts, and that means conversion rates need to be substantially higher than are normally considered to be acceptable in traditional demand generation.

The reality is, you need a different set of metrics and standards for ABM. Engagio recently published an excellent e-book - The Clear & Complete Guide to Account Based Marketing - that includes a framework for measuring ABM performance. This framework is based on the fundamental principle that the primary objective of ABM is to cultivate engagement and create influence with the people that matter.

This principle also dictates how we should evaluate the effectiveness of content resources that are used in an ABM program. Most systems for measuring content performance include consumption metrics, engagement metrics, and sharing metrics.

Content Consumption in ABM

It's obviously important to measure the consumption of the content that's used in an ABM program, but how you use this metric differs somewhat from the traditional approach. What you really need to measure is the consumption of your content by the identified contacts at your target accounts.

It's also important to determine whether all (or most) of the contacts at each target account are consuming your content. If they are, it's more likely that your content is effectively advancing your cause with the account.

In traditional demand generation marketing, content effectiveness is usually associated with high levels of consumption, but absolute consumption numbers matter less in ABM. What really matters is whether the right people are consuming your content.

Content Engagement in ABM

Measuring content engagement is especially important when you're using ABM because it provides an early indication of how well your ABM program is working. In the Engagio measurement framework, engagement with your company is one of the "Big 5" ABM metrics. Engagio recommends measuring engagement by using the time that contacts at target accounts spend interacting with your company and your content.

Content Sharing in ABM

Most content marketing experts view content sharing on social networks as an important indicator of content effectiveness. When you're using ABM, however, content sharing is less important because you will be proactively communicating about your content with the relevant contacts at your target accounts. In addition, the content that you use in ABM is customized for a very specific target audience - sometimes for a single account - so it's less likely that the content will be shared on a "public" social network.

The bottom line is that account-based marketing demands a new definition of marketing success and new ways of measuring marketing performance.

Illustration courtesy of Richard Matthews via Flickr CC.

What Separates Top-Performing Marketers from the Field?

The most basic of all business questions is:  What drives high performance? For years, the effort to find the "secret sauce" for achieving high performance has probably consumed more brainpower than any other single business topic. It's been, and still is, the modern-day business equivalent of the quest for the Holy Grail.

The desire to understand what drives high performance is widespread in the marketing world, and dozens of research studies have been devoted to solving the marketing performance puzzle. Earlier this year, Salesforce published a research report that includes several interesting insights on the practices of high-performing marketing teams.

The 2016 State of Marketing report is based on a worldwide survey that produced 3,975 responses from marketing leaders. Twenty-six percent of the respondents were affiliated with B2C companies, 29% with B2B companies, and 45% with B2B2C companies.

For this report, Salesforce divided survey respondents into three cohorts:

• High performers (18% of the respondents) were those who were "extremely satisfied" with the current outcomes realized as a direct result of their company's marketing investments.
• Moderate performers (68% of the respondents) were those who were "very or moderately satisfied" with the outcomes produced by their marketing efforts.
• Underperformers (14% of the respondents) were those who were "slightly or not at all satisfied" with their marketing results.

As you might expect, the Salesforce survey revealed some major differences between high-performing marketing teams and underperformers.

High Performers Focus on the Customer Journey

High performers were 8.8x more likely than underperformers (65% vs. 7%) to indicate that their company had adopted a customer journey strategy as part of its overall business strategy. For this research, Salesforce defined "customer journey" as all interactions that customers have with a company's brands, products, or services across all touchpoints and channels.

Salesforce also found that high-performing marketers were 9.7x more likely than underperformers to be actively mapping the customer journey, and 7.7x more likely to be leading customer experience initiatives across the business. 

These findings indicate that top-performing marketers are highly focused on delivering outstanding customer experiences across the entire customer lifecycle.

High Performers Leverage Technology

Another significant difference between high performers and underperformers relates to the use of marketing technologies. The Salesforce research found that high-performing marketers were:

• 7.6x more likely than underperformers to be "heavy" adopters of marketing tools and technologies
• 10.7x more likely than underperformers to be extensively using predictive intelligence technologies in their marketing efforts
• 6.7x more likely than underperformers to be extensively using marketing automation
• 2.8x more likely than underperformers to be substantially increasing spending on marketing tools and technologies

There's no longer any doubt that marketing has become deeply entwined with technology. So, it shouldn't be surprising that top-performing marketing teams are leveraging technology tools to enable and support their marketing activities and programs.

The Salesforce report includes several findings regarding the effectiveness of specific marketing channels, and it includes a breakdown of the major survey findings for eight individual countries. If you're a marketer, it will be well worth your time to read the entire report.

Illustration courtesy of Yoggl Innsbruck via Flickr CC.

How Millennial Buyers are Changing B2B Marketing

In 2015, the US Census Bureau estimated that millennials outnumbered baby boomers and have become the largest generational cohort in the US population. As millennials enter the labor force, many will work for B2B companies, and as they advance in their careers, millennials will become increasingly involved in B2B buying decisions. So, it's safe to say that millennial B2B buyers are poised to have a significant impact on B2B marketing and sales.

Sacunas recently published the results of a study that provides important insights regarding the attitudes and preferences of millennial B2B buyers. While there is an abundance of research regarding millennials, the Sacunal study is one of the few studies that have focused specifically on millennial B2B buyers.

For this research, Sacunas surveyed 2,000 millennials across the United States. Sacunas defined millennials as adults between the ages of 20 and 35, and the survey results are based on the responses of 1,444 individuals who were employed, either full-time, part-time, or self-employed.

One surprising finding of the Sacunas research is that millennials are already having a significant impact on B2B buying decisions. Seventy-three percent of survey respondents said they are involved in buying decisions at their companies, and 34% said they are the sole decision-maker for their department.

Channel Preferences

Some of the findings of the Sacunas research confirm what many of us have suspected about the preferences of millennials. For example, it's not particularly surprising that millennials have a strong preference for digital communication channels.

• 56% of survey respondents said that digital channels - search engines, vendor websites, and social media - are the most important channels for researching B2B products and services.
• 82% of respondents said that mobile devices are somewhat or very important when researching new products and services.
• 85% of respondents said they use social media when researching products or services for their companies. The most popular social network for millennial B2B buyers is Facebook - 40% of respondents said they use Facebook to research B2B products and services. LinkedIn, which most research shows is the premier B2B social network, came in fourth in popularity among millennials, behind YouTube and Google+ as well as Facebook.

Content Preferences

The table below shows the content formats that millennial buyers prefer when researching products and services for their companies. The clear preference for videos indicates that millennial buyers - even more than buyers in other age groups - prefer engaging visual content.

Implications

The Sacunas research shows that millennials are already involved in making at least some B2B buying decisions, and their influence will grow as they advance in their careers. So, what does this mean for B2B marketing?

Other recent research has painted a somewhat confused picture regarding the role of social media in the B2B buying process. Most studies indicate that a significant majority of business buyers are using social networks, but some studies indicate that social media is playing a relatively minor role in B2B buying decisions. The findings of the Sacunas study indicate that millennial buyers are using social media more than older buyers, particularly when they are researching potential vendors.

The Sacunas study also shows that millennial buyers have a strong preference for video content. Video content has been playing a significant role in B2C marketing for some time. The Sacunas research indicates that B2B companies will need to add video content to their marketing mix if they want to connect effectively with millennial buyers.

Top image courtesy of ITU Pictures via Flickr CC.

When You Need to Look Beyond Look-Alike Modeling to Select ABM Target Accounts

Account-based marketing (ABM) is rapidly becoming the preferred approach to marketing for many B2B companies, and choosing which accounts to target is widely recognized as the most critical step in building a successful ABM program. Most ABM practitioners select their target accounts by identifying businesses that resemble their best existing customers.

Technically, this approach is known as look-alike modeling, and it's an effective way for companies to select ABM target accounts in most circumstances. In some business situations, however, look-alike modeling isn't a good option, and it's important to understand when you need to use a modified approach to select accounts for your ABM program.

For look-alike modeling to be effective, your company needs to have enough existing customers to build a customer model that's reliably predictive. In a webinar earlier this year by Lattice, the presenter stated that you need at least 500 "successes" to build a sound customer data model. Ideally, these "successes" will be existing customers, but you can include late-stage sales opportunities if that's absolutely necessary.

While 500 may not be an absolute minimum, you do need a substantial number of existing customers to build a reliable customer model, and there are two circumstances when this may pose a problem. First, a start-up or young business may not have acquired enough customers to create an accurate profile of the ideal customer.

A similar problem can arise when a mature business wants to use ABM for a new or recently launched product or service. If the new product or service appeals to a different type of customer than the company's other products or services, a customer model based on existing customers may not be useful for the new product or service.

In these cases, the best way to select target accounts for your ABM program is to identify and use the basic attributes that make a business an attractive prospect for your company. I discussed these attributes in some detail in my last post, but at the highest level, attractiveness is a function of the value that a prospect will potentially produce for your business and the likelihood that a prospect will purchase your products or services (buying potential).

Choosing target accounts for an ABM program also becomes a little more complex if your company needs to sell to new types of customers in order to reach growth objectives. For example, suppose that your company has been selling primarily to businesses in a particular industry. To reach your revenue growth objective, you need to begin marketing and selling to businesses that operate in a different industry, and you want to use ABM to focus your marketing and sales efforts on the right prospects.

To use look-alike modeling effectively in this situation, you need to look beyond basic firmographic attributes - things like industry vertical and company size - and identify the key operating characteristics that your best existing customers share.

To use a simplistic example, suppose that your company has been selling distributed marketing automation software to technology companies that sell through independent systems integrators. You want to begin selling your software to manufacturing companies that sell through independent dealers. When you analyze your best existing customers, you find that they usually have more than 500 system integrator partners, and that they rely on their channel partners for a significant percentage of their total revenues. Given these shared operating characteristics, you would probably want to target manufacturers that sell through a large network of independent dealers and rely on dealer sales for most of their revenues.

As I noted earlier, look-alike modeling is an effective way for companies to choose ABM target accounts in most circumstances. But like any business tool or methodology, look-alike modeling has some limitations, and it's important for marketers to understand when a different or modified approach is needed.

Illustration courtesy of Kate McCarthy via Flickr CC.

What Makes a Prospect Attractive for Account-Based Marketing

The basic premise of account-based marketing (ABM) is that a company should focus its marketing and sales efforts on prospects that have a strong likelihood of becoming good customers. So it shouldn't be surprising that account selection is widely regarded as the most critical step in building a successful ABM program.

Most ABM practitioners choose their target accounts by identifying businesses that "look like" their best existing customers. This technique - known as look-alike modeling - is an effective way for most companies to select target accounts in most circumstances. However, like any business tool, look-alike modeling must be used correctly, and in some cases, choosing target accounts based solely on look-alike modeling may not produce optimal results. Therefore, it's important for marketers to understand the underlying factors that make companies good targets for ABM.

The following diagram depicts the factors that make a prospect organization attractive for account-based marketing. At the highest level, attractiveness is a function of value and buying potential. In this context, value simply means that a prospect has the potential to be a large and profitable customer for your company. The best measure of this factor is the estimated lifetime value that the prospect would produce for your company.

Buying potential refers to the likelihood that a prospect will purchase your company's products or services, and as the diagram shows, buying potential is a function of two factors - fit and interest.

Fit is one of those business terms that's hard to define in a precise and formal way. The underlying idea is suitability, and one dimension of fit is whether your company's products or services can effectively address a need, problem, or challenge that the prospect is likely to have. In the diagram, I call this solution fit.

The second dimension of fit is more subtle, but equally important. I call this dimension company fit, and it refers to whether your company can effectively market to, sell to, and serve a particular prospect. Company fit is often a function of geography for small and mid-size companies. For example, if your company is based in Atlanta and primarily serves customers located in the southeastern United States, you may not be able to effectively market or sell to, or serve, a prospect located on the west coast, no matter how well your products or services fit the prospect's needs.

The second component of buying potential is interest, which refers to whether a prospect has shown an inclination to evaluate or purchase the kinds of products or services that your company offers. Interest also has two components - engagement and buying signals. Engagement refers to whether a prospect has had direct interactions with your company. Has anyone affiliated with the prospect visited your website, consumed your marketing content, or met with one of your sales reps? Has the prospect bought from your company in the past?

The other dimension of interest is buying signals, and this refers to prospect behaviors (other than direct interactions with your company) that indicate the prospect may be interested in the kinds of products or services your company provides. Today, most accessible buying signals consist of online behaviors such as website visits and content consumption behaviors. These behaviors are represented as intent data, which is collected and sold by B2B publishers. Some providers of predictive analytics acquire access to this data and incorporate it into their PA solutions. Therefore, as a practical matter, you will only have access to this type of intent data if you are using a predictive analytics solution to support your marketing efforts.

Earlier, I noted that look-alike modeling is an effective way for most companies to select target accounts for their ABM program in most circumstances. However, in some cases, choosing target accounts based solely on look-alike modeling won't produce optimal results. In those circumstances, you'll need to step back and use the factors described in this post. In a future post, I'll describe some of the circumstances that require more than pure look-alike modeling.

How to Pick the Right Accounts for Your ABM Program

Account-based marketing (ABM) is fast becoming the preferred approach to marketing for many B2B companies. Last summer, in a survey of B2B business and marketing leaders by Demand Metric, 45% of respondents said they were testing or already using ABM, and another 26% said they were interested in adopting it.

The defining characteristic of account-based marketing is that it focuses marketing efforts on a specified group of target accounts. Therefore, choosing which accounts to target is an essential step in implementing ABM, and most ABM thought leaders and practitioners agree that account selection is the most critical component of any ABM program. Choosing the right target accounts is not the only thing you need for success, but it will be impossible to build a successful ABM program if you target the wrong accounts.

ABM can be used for acquiring new customers and for marketing to existing customers. In this post, I'm focusing on account selection for new customer acquisition.

Most companies choose target accounts based on how closely those organizations resemble their existing customers. This approach is often called look-alike modeling, and the process is fairly straight forward:

• Companies identify the attributes and behaviors that their best existing customers have in common. For example, firmographic attributes might include company size, industry vertical, number of employees, and location.
• Then, they use these shared attributes and behaviors to create a profile of their "ideal customer."
• Lastly, they will choose their target accounts based on how closely they match the ideal customer profile.

Most companies select target accounts manually, but mature ABM practitioners are increasingly using predictive analytics to support the account selection process. Virtually all predictive analytics solutions use a sophisticated version of look-alike modeling to identify target accounts. They extract data regarding existing customers from your CRM and marketing automation solution and combine that information with external data about those customers to construct a customer data model. The solution provider then runs your customer data model against its database of businesses and/or applies the model to prospects already in your marketing database to identify the accounts that resemble your existing customers.

The advantage of predictive analytics is that it can incorporate and process far more data points than humans can realistically use. Therefore, predictive analytics solutions can enable companies to build and use more comprehensive customer data models and thus do a better job of identifying accounts that most closely resemble their existing customers.

In many ways, look-alike modeling is the essential foundation for effective account-based marketing. But like any business tool or methodology - no matter how powerful it may be - look-alike modeling is not without limitations. Therefore, it's important for marketers to understand what look-alike modeling is really trying to achieve, what assumptions or inferences underlie it, what the limitations of look-alike modeling are, and how to tweak the look-alike modeling process to reduce or eliminate some of those limitations. I'll discuss these issues in my next post.

Illustration courtesy of Emillo Kuffer via Flickr CC.

Yes, You Still Need Product Content

Understanding what types of content will resonate with potential buyers is a core part of every content marketer's job, but a recent study by LinkedIn indicates that B2B marketers and salespeople don't always provide potential buyers the kinds of content they want and need. This research suggests that we may not understand our buyers' needs and preferences as well as we'd like to believe. Or does it?

The LinkedIn study consisted of a survey of 6,375 B2B buyers, marketers, and salespeople at mid-sized to enterprise companies. Survey respondents were from the United States, the United Kingdom, Canada, France, Germany, Australia, and India. Almost nine out of ten respondents were manager level and above.

LinkedIn addressed several issues in this study, and I recommend that you read the full research report. In this post, I'm focusing on what types of marketing and sales content B2B buyers, marketers, and salespeople perceive to be most useful and effective.

In the LinkedIn survey, participants were provided a list of seven types of marketing/sales content and asked to choose the three types of content they thought are most effective. The following table shows the percentage of B2B buyers, salespeople, and marketers who included each type of content in their top three choices.

These results provide several important insights for B2B marketers. First, they indicate that B2B buyers have a strong preference for product-related content. When buyers identified the three types of content they preferred, the two most popular choices were product information, features, functions (35%) and demos (31%). There was a drop of eleven percentage points between the second most popular choice - demos - and the third most popular choice - best practices.

The LinkedIn study also reveals that B2B buyers and marketers don't agree on what types of content are most effective. For example, only 24% of marketers rated product information as effective, which is eleven percentage points lower than the opinion of buyers. And, there is a thirteen percentage point gap between buyers and marketers regarding the effectiveness of demos.

Do these findings indicate that thought leadership and educational content is less important than we have come to believe? I don't think so. In this survey, LinkedIn also asked buyers:  "What are the important factors in your willingness to engage with a vendor?" The top four answers were:

• Understands by company's business model
• Is a subject matter expert/thought leader
• Provides valuable consultation, education, or tools
• Knows my company's products/services

These choices strongly suggest that B2B buyers place a high value of receiving insights from their prospective vendors that will make them smarter and help them improve their business.

What the LinkedIn research really confirms is that B2B marketers and sales reps must have content that's relevant for every part of the buyer's journey. And, it serves as a reminder to marketers that product-related content still matters.

Top image courtesy of Andrea Balzano via Flickr CC.