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.

How Small Businesses Practice Marketing

The overwhelming majority of business organizations in the United States are small companies. In 2013, there were about 5.78 million business firms in the US, according to the US Census Bureau. Only about 18.600 of these businesses had more than 500 employees, and about 5.67 million of these firms (98%) had fewer than 100 employees.

So, how are these millions of small businesses marketing their products or services? What marketing tactics are they using, and what marketing technologies have they adopted? These questions aren't easy to answer in a definitive way, particularly in the B2B space. Most of the freely-available research regarding marketing tactics and marketing technology adoption provides data for large and midsize companies.

Two firms - BIA/Kelsey and Borrell Associates - conduct extensive research regarding the marketing practices of smaller businesses, and both of these firms publish research reports that are available for purchase. If you need current, in-depth information about small business marketing, these two firms are good sources.

From time to time, it is possible to find publicly available research regarding small business marketing. Two recent studies - one by Salesforce Research and one by Infusionsoft and LeadPages - provide some interesting, if somewhat contradictory, insights about how small companies are marketing and what marketing technologies they're using.

The Salesforce Study

In 2015, Salesforce Research surveyed more than 3,800 sales, service, and marketing leaders in small businesses around the world. For this study, Salesforce defined small businesses as those having between 1 and 100 employees. Here are some of the major findings of the Salesforce study:

• Marketing automation - Only 20% of small business marketers are currently using marketing automation software. Another 29% plan to implement some type of marketing automation in the next 12 months. Sixty-seven percent of those marketers who are using marketing automation rate it as very effective or effective.
• Mobile marketing - Fifty-four percent of surveyed marketers said that integrating mobile marketing into their overall marketing strategy is very effective or effective. (Note:  In this study, mobile marketing was defined as SMS, push notifications, mobile apps, and location-based marketing.)
• Analytics - Fifty-three percent of small business sales teams are currently using analytics in some way. In addition, 29% of sales teams are currently using predictive analytics in some way. (Note:  The Salesforce study did not address the use of analytics by small businesses for marketing purposes.)

The Infusionsoft/LeadPages Study

In December 2015, Infusionsoft and LeadPages surveyed more than 1,000 self-identified small business owners in the United States regarding their marketing practices and plans. It doesn't appear that this study used a specific definition of "small business." Here are some of the major findings of this study:

• Nearly half (46.8%) of surveyed business owners said they are personally responsible for marketing at their company.
• The company website is the most widely-used marketing channel. Nearly three-fourths (71.6%) of respondents use their websites for marketing. In contrast, less than half of the survey respondents said they use other digital marketing channels - digital advertising, social media, e-mail marketing, etc. - and less than one-third of respondents use print advertising, direct mail, telemarketing, or in-person events.
• Most small companies aren't using content marketing. Fifty-eight percent of respondents post on social networks, but only 43% send marketing e-mails, only 36% publish blogs or articles, and only 16% offer downloadable content on their websites.

As noted earlier, the findings of these two studies are somewhat contradictory. The Salesforce study suggests that a fair number of small businesses are using relatively sophisticated marketing techniques. The Infusionsoft/LeadPages study, on the other hand, indicates that most small businesses have very limited marketing programs.

Image courtesy of Infusionsoft via Flickr CC.

Two Key Promises of Predictive Marketing

As a B2B marketer, imagine how much more effective your marketing efforts would be if you had the following insights:

• What if you could identify businesses that are likely to have a strong interest in your company's products or services before you market to those businesses?
• What if you could reliably identify which of your current prospects have a strong propensity to buy your company's products or services and thus are ready to have a meaningful conversation with one of your sales reps?

These are two of the most significant promises of predictive marketing solutions. During 2015, predictive marketing was one of the hot technologies in B2B marketing, and it appears that the demand for predictive marketing solutions is poised to grow rapidly. 

Last fall, Everstring published the results of a survey of marketers regarding the use of various marketing technologies. Twenty-five percent of the survey respondents said they were currently using some predictive tools, and another 47% said they were aware of predictive marketing and were investigating how to use it. Two studies by Forrester Consulting - available here and here - reported even higher usage rates of predictive marketing analytics among B2B companies.

Predictive marketing solutions have the potential to dramatically improve the productivity of B2B demand generation by enabling companies to target their marketing and sales activities more precisely. Predictive analytics can be used to address a wide range of business issues, but the two uses that are receiving most of the attention in the B2B marketing world are new prospect acquisition and prospect/lead scoring.

Most predictive marketing solutions employ the same basic approach for both of these use cases. They take data regarding your company's existing customers from your CRM and marketing automation systems and combine that information with external data about those customers - from around the web, social media, and other third-party data sources - to construct a customer data model that describes the attributes and behaviors of organizations that are likely to have a strong interest in your company's products or services.

When predictive marketing is used to identify new prospects, the solution provider will run your customer data model against its (the solution provider's) database of businesses. The result is a list of prospects that resemble - to a greater or lesser extent - your existing customers. The inference is that prospects that closely resemble your existing customers  are likely to be interested in your company's products or services. With this insight, you can target your marketing programs more precisely and use your marketing resources where they are more likely to be effective.

When predictive marketing is used for prospect/lead scoring, the solution provider applies your customer data model to the prospects already in your marketing database and generates a score for each prospect based on how closely the prospect resembles your existing customers. This enables you to qualify prospects or leads using much more data than is typically available in traditional lead scoring systems. In theory, therefore, a predictive marketing solution qualifies prospects and leads more accurately, and it can potentially identify buying signals that are almost impossible to find using traditional lead scoring techniques.

The early indications are that predictive marketing solutions will drive significant business benefits. For example, in a 2015 study by Forrester Consulting, 72% of respondents whose companies were using predictive marketing grew revenues by 10% or more during 2014. Only 33% of non-users achieved the same rate of revenue growth.

While its clear that predictive marketing solutions can provide significant benefits in the right circumstances, there are a few caveats that marketers should keep in mind. For example:

• These solutions rely heavily on data from a company's CRM and marketing automation systems to construct the customer data model. So, if your company is a fairly mature user of CRM and marketing automation, and if your systems contain a significant amount of usable data, predictive marketing could be a sound investment. On the other hand, if you don't have enough reliable CRM/marketing automation data to work with, the value of predictive marketing will be more problematic.
• It's also important to recognize that you need a reasonable number of existing customers to create a customer data model that is reliable and predictive. Put simply, your customer data model will be richer and more reliable if it is based on 500 customers rather than on 50 customers.
• Predictive marketing solutions are not outrageously expensive, but they can require a significant investment. The cost of predictive marketing solutions varies greatly depending on the features of the solution and a variety of other factors. Pricing can always change, of course, but at present, it appears that the starting price for most predictive marketing solutions ranges from around $15,000 per year to over $100,000 per year.

Illustration courtesy of Louise McLaren via Flickr CC.