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A Guide to Predictive Analytics

Predictive analytics is a set of metrics and data that is forward-looking, helping sellers and marketers prioritize their activities, create personalized campaigns, and increase customer retention.


Chapter 1


Chapter 2

The Components of Predictive Analytics

Chapter 3

Examples of Predictive Analytics

Chapter 4

The Benefits of Predictive Analytics

Chapter 5


Table of Contents

Chapter 1


Most sales and marketing data points are rearward-looking. They only reveal past actions, engagements, and activities of your buyers.

The real benefits of analytics come when you can use them to help plan your future activities and campaigns with confidence. Knowing which messaging will resonate with which audiences at the exact right time is a huge advantage in any industry.

Predictive analytics is a forward-looking application of data that can decipher what your buyers are doing in real-time, and the likelihood of them becoming a customer.

It can help you:

  • Prioritize the best accounts and buyers to target
  • Align messaging to the topics buyers care about most
  • Understand if you are reaching and influencing the entire buying team
  • Know where accounts are in their buying journey

This guide will help you understand the foundations of predictive analytics, what metrics are considered predictive, and how leveraging these insights can help you boost your revenue

Chapter 2

The Components of Predictive Analytics

Ideal-customer Profile

Your ideal-customer profile (ICP) is the unique set of characteristics that make a company a perfect match for your products or services. An ICP reveals the accounts and buyers that are the right fit and would make the best partners.  

The types of information that go into an ICP include:

  • Company size
  • Industry
  • Number of employees
  • Locations
  • Revenue

Your ICP is an important part of predictive analytics because it identifies companies that are a fit for your products. From there, predictive analytics uses real-time buying signals to determine the best accounts to target.

Intent Data 

Intent data is one of the most important inputs for predictive analytics. It reveals who is showing interest in your products.  

Today’s buyers remain anonymous as long as they can. That’s especially true when they’re just trying to do preliminary research. Often, information that can help buyers is hidden behind forms. But buyers avoid filling out forms because they know what follows: a tsunami of phone calls and email solicitations.

We’ve trained buyers to avoid us! In fact, only 3% of your visitors will fill out a form on your website. Intent data is the key to capturing the signals your buyers are giving out while they perform anonymous research. 

By collecting various signals you can uncover the topics they’re interested in, the channels they interact with most, and the content they engage with.

There are three types of intent data:


Channels owned by your organization. This includes your website, email marketing, sales engagement, events, and other resources that you control. This data will generally be tracked through platforms like a CRM and MAP.


The sites that you don’t own, but which discuss your company and products. For example, sites like TrustRadius and G2 offer users the ability to compare and review different services, and can provide a rich source of information about which companies are comparison shopping.


The activities and research done elsewhere on the internet that signal a buyer’s interest in products or services like yours.

Capturing these signals requires technology that can detect this type of activity across different channels and then match that activity to the accounts performing the engagement.

A strong foundation of intent-data capturing will lead to more insightful and accurate predictive analytics.

Historical Data

Your company’s past performance provides a great blueprint for which accounts are most likely to buy.  

The history of your sales and marketing activities is a treasure trove of data that includes:

  • The titles of your typical buyers
  • The campaigns that drive the most engagement
  • The length of your buying cycles
  • The top industries of your prospects and customers

The difficult part in using all of that historical context is the difficulty in sorting through massive troves of data to find meaningful trends. This is an area where AI is invaluable.

By ingesting the mountains of data your company owns, AI systems can process the information and spot patterns. When you combine analysis of past deals with ‌intent data about current prospects, you get an AI that can forecast:

  • Which prospects are most likely to buy
  • How much they are likely to spend
  • When they are likely to sign a deal

Even better, these recommendations improve with time as the AI ingests more information and learns more about your buyers’ behaviors.

Chapter 3

Examples of Predictive Analytics

Account Fit

Account fit is the metric that measures how well an account or buyer matches what you sell. Within your Target Account List, there will always be some accounts that are extremely similar to your past buyers, and other accounts with weaker resemblances.

Use Case: Quickly and efficiently prioritize your team’s outreach to focus on the accounts most likely to be a good fit for your business. This reduces time wasted on bad leads and increases your chance of reaching a buyer who’s likely not only to buy, but also to be a happy customer with strong lifetime contract value.

Account Reach

This datapoint reveals how likely your teams are to open an opportunity with a given account. It evaluates whether your messages are being seen, and whether accounts and buyers are responding by engaging in research.

The probability of creating an opportunity relies heavily on the timing and type of outreach. Using this predictive data helps your teams understand the effectiveness of their engagement and hone their strategies for future campaigns.

Use Case: Identify accounts that are strong fits but haven’t yet been reached or influenced by your revenue team. Do you need to enroll those accounts in different campaigns or engage in direct outreach? You should also track how different efforts impact the behavior of accounts.

Buying Stage

Understanding an account’s buying stage is crucial for making sure your messaging and offers are aligned. Reaching out to someone that is just starting to research your industry and offering them a full demo is unlikely to result in meaningful engagement.

Similarly, if someone is well-versed in your product and wants a deep dive into your offering, you shouldn’t be messaging them with the Basics 101 version of your solution. Your outreach won’t compel the buyer; it will annoy them.

Understanding the buying stage of the accounts in your pipeline also helps you understand the potential deal value currently in play, as well as when deals are likely to close.

Use Case: Understand the distribution of accounts in your pipeline across the various buying stages. Investigate each stage of the buying journey to understand how accounts are moving through the process, the effect your campaigns have, and where you should prioritize your efforts.

Account Coverage

Modern buying teams are large. Gartner places the average buying team at 14 to 23 members. Closing a B2B deal requires buy-in from the entire committee. Understanding the engagement of buying team members is critical.

Predictive analytics can combine the engagement scores of key buyers at your target accounts to determine your level of engagement across an entire company.

Account coverage is most helpful when displayed in a visual format like a persona map, which plots key contacts at an account by department, level, and function. Within the persona map you can quickly drill down into which buying team members are most engaged, which have only lightly engaged, and those where no headway has been made.

Use Case: Identify the entire buying team at key accounts and determine where gaps exist. Use that information to prioritize your activities and increase engagement with key stakeholders.

Chapter 4

The Benefits of Predictive Analytics

Reveal Your In-Market Audience

As a B2B seller, you’ve probably built an Ideal Customer Profile (ICP) that defines the characteristics that make a company a good fit for your services. But, your ICP is a static rubric that doesn’t capture important considerations like timing, budget, market conditions, or other dynamic factors.

Predictive analytics fold in those real-world data points to hone your ICP further into an in-market ideal customer profile (IICP). The value of an IICP is the ability to focus your efforts on accounts and buyers you know are researching solutions you can offer.

The ability to narrow down your target audience to only likely buyers reduces time and money wasted on other accounts. With your resources and efforts targeted where they’ll make the most impact, win rates and revenue see a boost.

Improve Engagement with Personalized Experiences

Seventy-three percent of B2B buyers want a personalized buying experience. Delivering those personalized experiences requires a deep understanding of your audience so you can create messaging that resonates with them. A crucial component of this is delivering that messaging through the correct channels at the right time.

Predictive analytics gives you a huge boost in achieving that in-depth understanding of your buyers by:

  • Giving you access to the real-time topics they’re engaging with
  • Revealing the channels they use most
  • Identifying the content they’re reading, and
  • Pinpointing where they are in their buyer’s journey

An AI platform that combines predictive analytics with marketing automation capabilities can provide a huge advantage: You can set up automated orchestrations that enroll accounts in marketing campaigns that match buyers’ current interests.

This way, your messaging can move in lockstep with the buying journey.

Reduce Customer Churn

Acquiring a new customer can be five times as expensive as keeping existing ones. The high cost of acquiring new customers makes it imperative that you retain your current customers — and even find opportunities to expand your relationships with them.

Predictive analytics give you insights into the minds of your customers throughout the lifetime of their contract. While your customer success and account management teams likely communicate regularly with your customers, they won’t always know when a customer is starting to research other solutions.

But you can capture intent data and signals to spot when your customers may be searching for new solutions. By creating alerts and tracking topics, you can quickly reach out to the customer to offer an upsell or work to increase customer satisfaction.

Prioritize Your Activities and Outreach

Sellers spend only 30% of their time on selling activities — the rest is spent prospecting, cold calling, and administrative tasks.

Predictive analytics shine a light on your revenue driving activities and highlight the way forward. Instead of taking a shot in the dark on which account to target next — based on arbitrary account lists or static demographic information — predictive analytics guide you toward your best move. By…

  • Analyzing how a buyer fits your offerings
  • Uncovering the anonymous activities they’re performing
  • Determining where they are in their buying stage, and
  • Measuring your influence throughout an account

…it becomes straightforward to prioritize your activities.

Chapter 5


Predictive analytics is a new frontier of insights powered by AI and machine learning. By capturing the signals your buyers are performing across the internet with your mountains of data about previous opportunities and deals, your teams can unlock proactive data that helps them prioritize their activities, deliver a personalized experience, and keep customers happy.

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The 6sense Team

The 6sense Team