The Importance Of Ethics Marketing Segmentation

Four practical segmentation tips for B2B marketers

This article was originally published on MarketingProfs on February 1, 2016


Congratulations! You have invested in a customer relationship management (CRM) and a marketing automation platform (MAP). You are capturing leads and running campaigns. Ready for more? Your next mission, if you choose to accept it, is B2B market segmentation.

In this article we’ll explore four ways segmentation can improve demand generation, along with four practical tips on what and how to segment.

Segmentation Is Key to Demand Generation

B2B customer segmentation is the foundation of…

  1. Your ideal customer profile and target accounts lists: The best prospects are often those who resemble your existing customers. Segmentation enables you to build a target account list based on data, not wishful thinking.
  2. Account-based marketing (ABM): B2B marketing is a highly account-centric activity, but MAPs are designed to market to individual leads. Use job level and job function segmentation to find gaps in account penetration and coverage.
  3. Targeted campaigns and personalized engagements: MAPs can enable personalized engagements via targeted campaigns, but only if you have good segmentation.
  4. Easier suppression: Knowing whom not to target is as important as knowing whom to target. Don’t ruin your engagement rate, KPIs, and sender score/reputation by marketing to non-receptive leads.

Now that we know why segmentation is important, here are four practical ways to go about it.

1. Job Level

Is your lead C-level, an executive, a manager, or an individual contributor? Job level segmentation reveals the role a lead plays in the buying process.

Job level segmentation can be readily inferred from job title; for example, a “VP” is an executive and a “Director” is a manager. However, how a job title translates to job level varies among industries; for example, a “VP” is usually more senior than a “Director,” but not in financial services.

A person’s role in the buying process also depends on the product sold. For example, a security architect may be a decision-maker for a security product, but he or she is considered only an influencer for an application management solution.

The most effective approach is to create customized B2C market segmentation logic that fits your business.

2. Job Function

Which department does your lead work in? Job function also can be inferred from job title. For example, a CFO is in finance and a Demand Gen Manager is in marketing.

Most B2B products target users in a specific business function. Sometimes the department making the purchase decision (e.g., IT) is not the same as the department using the product (e.g., finance), so it is important to identify the lead’s job function and role in the buying process.

For a scalable segmentation scheme, consider a two-level strategy:

  1. Use coarse-grain segmentation to sort leads into major business functions, such as Finance, Sales, and IT. It’s sufficient for certain purposes, such as list suppression.
  2. Use fine-grain segmentation for targeted job functions. For example, if you sell to IT, you can further segment that into networking, architecture, and security, among others.

3. Company Size

Few B2B vendors can sell to companies of every size. Even if you do, there are usually different product lines targeting different-size customers. Company size is typically measured using annual revenue or number of employees; that data can come from your sales team or a data service such as Bombora.

Size needs to be segmented into ranges, such as “$100-$500 million” to be meaningful. However, size range definition is unique to your business. For example, a company with $50 million annual revenue is likely considered a small enterprise by SAP, but a large enterprise by Intuit.

4. Industry

Many B2B products are sold to specific industry verticals. Even for “horizontal” products, some verticals are better than the rest. Therefore, your marketing strategy should always have an industry focus.

Industry data based on standards such as the North American Industry Classification System (NAICS) and Standard Industry Classification (SIC) codes are readily obtained from a data service. However, a standard industry list usually contains a very granular set of a few thousand verticals. Additionally, data from different sources uses different standards. In contrast, most B2B marketers have a list of fewer than 15 industries that matter.

The key is remapping the standard and third-party industry data to your custom list.

The Tools You Need

How do you segment hundreds of thousands, if not millions, of records and keep up with constant changes from multiple data sources?

That’s where many marketers roll up their sleeves and start building filters and smart campaigns in their marketing automation platform in an attempt to segment job levels and standardize revenue ranges. Sound familiar?

Newsflash: you don’t need to reinvent the wheel; there’s a much easier way. You can segment with a data automation tool, and you need only two things: logic, and reference data.

First, determine how you want to segment. Define the revenue ranges and ideal job title. Then build it in a data automation solution that will apply your logic consistently across your database to clean, normalize, and segment records.

Second, you’ll need a set of reference data, such as a list of keywords commonly found in job titles, so your logic knows how to process and group the records (for instance, “California” and “Calif” both refer to “CA”).

Look for a data automation solution that is…

  1. Built for marketing and sales, not IT (translation: no coding is required.)
  2. Easy to customize (you can create rules that fit your business.)

Although data services can enrich your data with details related to job title, company revenue, number of employees, and industry, you must transform raw data into segmented data that is useful for your company. The challenge is how to do it in a scalable and manageable manner.

A little investment in data automation technology and know-how can save you a lot of frustration—and make the difference between success and failure.

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