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Predictive Playbooks

How to Sharpen Targeting with Micro-Segmentation

This 50-page playbook will help any marketer exceed pipeline expectations.

All B2B marketers would agree the sweet spot for demand generation programs is to target the buyers who are most likely to buy, tying product capabilities with their customers’ needs.

But in order to find the sweet spot of precision AND scale, marketers struggle to magnify their ability to segment their markets and customers while also maximizing campaign reach and personalizing every campaign. Predictive segmentation is here to help.

Check out this playbook to learn how marketers can use Predictive segmentation to access the insights they need for more targeted, personalized outbound marketing.

This 50-page playbook will help any marketer exceed pipeline expectations.

By reading this playbook, you will be able to:

  • Understand the impact of better segmentation on strategy and product marketing
  • Sharpen segmentation to hypertarget demand generation campaigns
  • Maximize campaign reach to every account that fits your ideal customer profile
  • Visualize a more powerful, streamlined segmentation workflow

How To Sharpen
Targeting With
Micro-Segmentation

Marketing to everyone is synonymous with marketing to no one. The challenge is being able to sharpen your focus to improve pipeline and revenue performance.

The most common challenges facing B2B marketers today is knowing who your customers are, understanding what makes them different, and figuring out how you can get them to convert.

In short, B2B marketers need to find the sweet spot - targeting buyers who are most likely to buy; given the capabilities the products enable in relation to their customers’ needs. Yet, as intuitive as this may seem, many marketers are afraid they’ll miss opportunities if they exclude anyone from their demand generation efforts.

That’s where segmentation and targeting can help. In this interactive playbook, we’ll take a look at segmentation, understand what role it plays in marketing today, and highlight key strategies to help you find success.

01What is segmentation and targeting?

Segmentation in B2B marketing is defined as:

A strategic approach to finely tune groups of target markets based on commonalities representative of your ideal customers.

Put simply, segmentation allows you to divide your markets or customers into distinct groups with commons needs and characteristics. Once they’re broken into smaller groups, you can get a better understanding of both current and potential customers, using the insights you discover to effectively target the latter.

By addressing a segment of buyers who have common pain points and needs, as well as attributes and buying signals, you can engage buyers and accounts with the highest propensity to convert. So, rather than wasting marketing dollars on broad campaigns that yield low conversions, you can target your best prospects that are highly receptive to your messages and more likely to purchase.


Role of target segments in marketing

One of the most common misconceptions with segmentation is that marketers will “miss out” on opportunities that might be available outside of target segments. But that’s not entirely true.

Here’s an analogy by Gartner that highlights how you should think about segmentation.

Gartner, Tech Go-to-Market: A Practical Guide to Market Segmentation, Hank Barnes, August 19, 2015

Think of the segmentation targeting model as an archery target:

  • The bullseye is your target segment or ideal customer profile (ICP), which is where marketers will primarily focus their efforts.
  • The next ring is prospects that fall into segments that have similar characteristics to the core segments.

  • Lastly, the outer ring is buyers that find your business.

The purpose of segmentation is for marketers to focus their efforts primarily on the core target segment, or the bullseye. Of course, there will be opportunities that are worth pursuing outside the target segments, but, marketing and sales will not invest significant time or resources into campaigns that attract these prospects.

If your business follows an account-based marketing strategy, think of your ‘target segment’ as your Tier A accounts.


Why is segmentation important?

The growing need for segmentation is largely driven by the lack of demand generation performance in most B2B companies.

79%

of CROs don’t know where their revenue will come from
Source: Xactly, May 2014

66%

of CMOs aren’t hitting targets
Source: 614Group, July 2014

Over 80%

More than 80% of companies without effective demand gen blame data quality for ineffective marketing campaigns and sales flops.
Source: Demand Metric, April 2016

42%

Yet, only 42% of companies with rich data are happy with their demand gen.
Source: Demand Metric, April 2016

The key takeaway: data quality is a critical issue for marketers today, but it’s not enough to just have high-quality data. Marketers still need insights into their prospects, customers, and market to create more targeted campaigns that yield better results.

The solution is intelligence-driven segmentation built on an accurate and comprehensive dataset.

Leveraging a combination of Data and Intelligence offers two core capabilities for marketers:

1. It delivers insights from large datasets that cannot be derived manually
2. It leverages a larger external data source that provide more buying signals

This unique combination allows B2B marketers to identify key buying signals (beyond what’s available in their CRM) to get a true representation of each account. Tapping into a larger source of data truth also helps uncover the most ideal prospects that have the highest propensity to convert.

The end result is a customer-centric Omnichannel campaign strategy built on an unprecedented level of granularity - targeting the right audience with the highest likelihood to convert.

Learn more about the Network of Record

02How marketers can use segmentation

Let’s look at a marketing scenario:

Imagine you’re the CMO of a B2B tech company and your team shares information on your company’s top accounts. You have $1 million in budget and your goal is to decide where you want to allocate it. To make a knowledgeable decision, you have asked your team to work with operations to append data to your CRM and run reports that identify segments with the highest likelihood to convert based on past conversions from Lead-to-Opportunity.

Your team is likely to include basic information about each account including the industry, location, and number of employees. After a long and difficult process, your team has identified two primary segments –Finance and Insurance – in the Bay Area with 250 to 500 employees and $50 to $100 million in revenue. The team also provided two sample accounts, as seen below.

Now armed with this information, you must make a decision.

How will you allocate your $1 million budget? Probably a 50/50 split across Account 1 and 2, right?

What if you had a Data and Intelligence solution that could provide deeper insights from your data and also use external datasets to share more buying signals?

Try re-running the numbers with the Radius Advanced Signals. Does your decision change?


As you can see from the table, your original bet of Account 1 and 2, while valid based on the original data, was not truly representative of the actual success rate of both accounts.

Using Radius, your marketing team could have identified key buying signals that gave you a true representation of each account, which would have helped you correctly identify Account 1 and Account 3 as the best accounts with the highest likelihood to convert.

Radius would have also helped you uncover the total number of accounts that exist in the entire segment – regardless of whether they exist in your CRM and/or marketing automation systems. This would enable you to make decisions not only based on likely success, but also on the segment opportunity size.

Without this level of granularity, marketers are gambling with their company budget.

03What are buying signals?

What makes up a segment?

In order to understand how segments can help deliver better customer insights, it’s important to take a closer look at the data points (signals) that make up a segment.

Let’s start by looking at firmographic signals, since they set the baseline i.e. they are required characteristics for your segment. Firmographic refers to data about a business, or firm.

Firmographic refers to data about a business, or firm.

This data is most often basic, traditional, and static, which means it’s not likely to change frequently. Some examples of firmographic signals include:

  • Company Name
  • Location
  • Phone Numbers
  • Number of Employees
  • Annual Revenue
  • Industry
  • Number of Locations

Applying firmographic signals is a great starting point, and almost all segments you create will include them. But while they are helpful, firmographic signals are just the tip of the iceberg.

Advanced Signals

More advanced signals tend to expose richer business motivations and challenges, which are usually not available when you’re just using firmographic data. You can examples of these below.

Product Technologies

List of product technologies used by the business (e.g. CRM, Support / Help Desk)

Business Attributes

Headquarters, Number of Locations, Chain Type, Accepts Credit Cards

News & Events

Recent events relevant to the business surfaced through news articles (e.g. IPO, Corporate Relocation)

Social

Presence across Twitter, Facebook, Website, Yelp, Google+, OpenTable, etc.

Intent

Likely interest of a business in specific topic areas or products

Web Technologies

Web Analytics, Advertises online, SEO technologies, WebCart technologies, etc.

Firmographics

Location, Industry, Number of Employees, Annual Revenue

Contact Info

Contact name, Title, Job function, Phone number, Email Address, etc.

What can signals reveal?

Here’s what incorporating richer signals in your segmentation can tell you about your target customer and market.

Success Rates

Find out which industry or market has the highest conversions. Access to richer signals can help you understand how well you perform in particular segments based on historical success rates. This allows you to determine where to allocate marketing resources.

Identify Need

Signals, or lack thereof, can show a need for your particular product or offering. For example, if your business builds websites, you may want to target businesses that have robust social media pages but do not have a website.

Surfaces "In-market" Buyers

Intent data is variable in time, meaning it can surface buyers that are expressing heightened interests. If a company appears in the segment, that means they have a current interest in the intent signals the segment includes.

Channel Targeting

Presence of a business across the web allows you to target them where they spend their time. Segmentation can help you find your target accounts that are on social networks and target contacts that work at those accounts.

Competitive Insights

With thousands of product and web technologies being tracked, many companies can gain visibility into the customers of competitive products and services. You can use these insights to run competitive marketing campaigns.

Integrations & Partnerships

Many companies have integrations that enable product functionality or go-to-market partners. Signals can help you target segments that have specific technologies as an install base already or are currently working with your existing or potential partners.

04How to discover your ideal audience with segmentation?

An important first step with segmentation is building out your target audience. But, it can be tempting to say “everyone can use our products” i.e. overestimate the size of your bullseye.

While your addressable market may be very large (the outer rings), your target segment has to be tightly defined. This means identifying your audience using key buying signals.

4 out of 5 market entries fail primarily because businesses don’t tightly define their target segments. ( Source: McKinsey)

Platforms like Radius offer many paths to build and discover an audience – some of which involve very little predictions. In order to be successful with segmentation, you need to understand where you fit within the Audience Discovery Spectrum (shown below).

  • “Very AI-driven” features require little to no human intervention, making way for machine learning to programmatically and in some cases automatically uncover the top audiences
  • “Non-AI” features give users full control to build whatever segment they’d like.
  • There are also several capabilities in-between that blend a user’s input with the machine’s intelligent recommendations.

Whether you’re building your own segments or leveraging recommendations, use the Audience Discovery Spectrum to help you build better, more targeted segments for your marketing efforts.

Control No Control
Manual

Manual segment building is applicable when you know exactly who you want to target. In this case, you build your lists using any signal.

Scenario: We’re heading to the Marketo conference. I want to target all my tier A & B prospects that have Marketo by each sales rep’s territory in order to run a call-down campaign.

Guided Insights

Guided insights help you explore where you’ve had success and build from there. You can view relevant insights and select key signals to build segments based on historical success.

Scenario 2: New enhanced product features are shifting focus on acquiring e-commerce businesses that advertise online. Within this segment, I need to find the industries & revenue bands where we have the most existing pipeline to build nurture campaigns. I also want to know if there are other signals we didn’t consider that are showing a lift.

Context-Driven Recommendations

Context-driven recommendations provide a more customized segmentation process wherein you select specific signals that must be included in segments. The goal here is to find the best prospects, but include a few attributes that are absolutely necessary.

Scenario: We have several customer advocates and case studies within the payment processing industry. I know there are several tier A accounts, but want to identify the top 200 mid-market accounts and top 20 enterprise accounts within the payment processing industry so we can develop and run targeted vertical-based campaigns.

Weighted Recommendations

Weighted recommendations allow you to rank particular model criteria based on importance. Leaning towards the more AI-driven part of the spectrum, you can specify that certain attributes are more/less important for success than others, yet still let machine learning run its due course.

Scenario: We have invested in a new research-backed white paper and need to identify several segments that have a high likelihood to convert for outbound and nurture campaigns. Since the research applies to any business in the US, location doesn’t matter, but we do consider industry and company size as very important.

Fully Automated Recommendations

Lastly, fully-automated recommendations let the platform tell you exactly who you should target. Marketers can use this option to automatically surface and rank their top recommended segments.

Scenario: Our Q4 campaign calendar has several openings and we want to develop an account-based campaign that is crafted specifically for one of our top segments.

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Conclusion

B2B marketers are tasked with the challenge of capturing their buyers’ attention, and with an ever-increasing amount of content and campaigns, personalization is key to success.

Adopting segmentation and targeting strategies are important methods for engaging your prospects and motivating their intent to buy.

With segmentation built on data and intelligence, marketers can define campaigns based on audience insights, leverage larger datasets and machine learning to identify buying signals that they would otherwise miss, and tailor their programs to all the right accounts that meet their segment criteria.

The outcomes from a customer-first demand generation program will far exceed what you will achieve if you focus on everyone as your segment.