What Is Data Segmentation? A Complete Guide (2024)

Data has established itself as the perfect ally for B2B companies. As years go by, data-driven businesses keep on proving that present-time customers prefer a tailor-made experience over a generalized sales pipeline follow-up. And who wouldn't?

Data-based decision-making is allowing businesses to target their most-wanted prospects with a higher success rate through effortless practices. According to HubSpot, surveyed marketers who segment customer data for their campaigns noted as much as a 760% increase in revenue.

Mailchimp also confirmed that segmented email marketing campaigns result in 23% higher open rates and 49% higher click-through rates than unsegmented campaigns. However, there is so much more to segment data than just email marketing effectiveness.

Segmentation isn't only about emails; it's about refining your entire marketing strategy. By understanding and dividing your customer base into specific segments, your marketing becomes more tailored and hence more impactful. Adding granularity to your marketing through market segmentation ensures your strategies resonate more with your target audience. 

The following guide will help you cover all the basics of the data segmentation concept and how to apply it to your business.


What Is Data Segmentation?

A practical data segmentation definition would refer to the dissection of account-related data through different criteria and its organization into specific categories to make it more actionable for marketing and sales strategies.

At its core, data segmentation is about adding granularity to your marketing approach. It's akin to defining personas, where each persona represents a unique customer segment.

Unsegmented data has the potential to throw new business development teams into havoc. B2B businesses must be wary of the massive amounts of incoming raw information (also known as data exhaust) for they could mislead their marketing and sales reps into poor decision-making, budget waste, time burn, and effort consumption.

Nevertheless, data segmentation makes it easier to gather, qualify, and integrate the customer data that can be used to craft accurate ideal customer profiles (ICP), which are considered the fuel that propels the B2B account-based marketing engines.

Segmented customer data and targeting share a similar root, but they have very different purposes. Targeting is used to determine the best techniques for promoting your company, brand, and product to your ideal customers. Segmenting comes before, and can even be referred to as the machinery behind efficient targeting.

Benefits of customer data segmentation 

Some actions your team members will be able to deploy once they integrate data segments into their daily lead generation game plan are:

  • Create lead sources (data sources) that follow specific segmentation rules
  • Create customized message templates for specific audiences.
  • Work with deeper insights for customized follow-ups.
  • Craft target account lists (TAL) with different tiers.
  • Plan campaigns specifically to those Target Account Lists
  • Use nurture steps with different personas for new opportunities.
  • Reduce the cost of mass-personalized campaigns.
  • Improve customer satisfaction due to personalized management.
  • Raise your return on investment (ROI) by targeting the right contact at the right time.
  • Leverage channels of marketing -- especially advertising (ROAS) more effectively by segmenting more
  • Advanced: Capture all data in a single CDP for future segmentation 
    Try CIENCE's CDP Now!

    CDP = Customer Data Platform

    CIENCE offers a robust CDP with multiple components that help with advanced data segmentation: 

    CIENCE GO Data is a sales intelligence platform that offers over 200 million accurate records applicable for any industry, paired with a custom approach to provide you with a 360-degree view of prospects. All of the records get verified daily and match your ideal customer profile (ICP) perfectly. 

    CIENCE GO Flow gives you the ability to capture event data from any website and harness that information in a 360-degree view of all your interactions. The GO Flow source can also transport real-time data streams to almost any destination (100+ integrations).


    Challenges of customer data segmentation

Likewise, B2B companies that are still adapting themselves to a more data-based culture might encounter the following struggles when it comes to managing their data segments:

  • Gaining meaningful insight quickly enough
  • Having too much or not enough data to work with
  • Trusting inaccurate data sources
  • Missing the right tools to segment data
  • Integrating modern processes into traditional workflows

Thankfully, a vast number of customer data segmentation techniques, approaches, and tools have emerged over the years to make companies more adept to segment data. 

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Types of Data Segmentation for B2B Companies

The success of customer data segmentation practices highly depends on selecting the perfect criteria to classify the information bulk that shares significant traits. 

While there may be other types of segmentation that dive even deeper into understanding the bonding characteristics that link each contact inside a particular batch, the most common (and practical) ways to segment customer data are the following:

Lead attribution

This focuses on the most essential lead-related features, such as location, title, department, business details, and other important factors that can be used to create a rich customer profile.

This first filter helps your team to determine what attributes of a particular customer or prospect are most meaningful to your lead generation process, like the decision-making authority, industry experience, and even availability. 

While a very standard practice, narrowing down the demographic, geographic, and firmographic data segment sets (among others) can help you define a customer profile with high conversion rates in an industry your team has determined as profitable.

A fine set of lead attributes you may want to consider for this early stage are the different types of market segmentation based on qualified data:

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Behavioral attribution

While behavioral analytics can dig down into the psychology behind every decision made by a prospect, the most actionable take on this segmentation type is to use it to learn about the tendencies, habits, and susceptibility levels to segment data.

In other words, the goal of this class is to recognize the relationship between your leads and the digital platforms your company uses to communicate with them. Every single action taken by a digital user leaves a data footprint that can be used to understand how, when, and where your contacts are more and less engaged by your digital efforts.

The more high-quality information your B2B company can gather, process, and exploit, the more possible it will be to create tailored outreach campaigns that lead to higher conversion rates and more satisfied customers.

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Buyer persona vs Ideal Customer Profile

The buyer persona classification is extremely useful to segment your audience data according to the most profitable contact traits. To build up a solid buyer persona, a certain amount of customer data segmentation must have already been conducted to designate what criteria must be considered in the first place.

A buyer persona is a representation of your customer that is used to understand its profile, create a customized environment for communication, and produce the perfect inbound and outbound strategies that will result in a meaningful interaction with your company.

It should not be confused with an ideal customer profile. This concept refers to a detailed description of a company that is most likely to become your client, along with the titles of its decision-makers, follow-up guidelines and insights, and its position inside the tiers of a target account list.

The difference between the two is that the ICP explains what companies and titles to target. The buyer persona describes how to target them effectively. 

By implementing all the customer data segmentation types explained above, your marketing and sales teams will be able to conduct their engagement strategies with a clear view. 

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4 Data Segmentation Techniques for Businesses 

Data segmentation marketing can be quite fluid. It all comes down to what type of audience data is truly valuable for your business and what you choose to use it for. 

Even though some companies benefit from several or all of the segmentation techniques defined below, it is important to understand the applications and implications of each one before deciding to integrate them into your processes.

1. Customer profiling

Customer profiling is the simplest technique on this list. It heavily relies on analyzing the demographics and behavioral traits to create a grounded customer profile. It takes the most basic data you can collect from your target (e.g., age, gender, location, income, etc.) and pushes it through analytical and operational profiling.

Analytical profiling studies the customer performed activities (mainly digital) and matches them with the gathered demographic information. Once the outcome of the interaction a company had with the customer is registered, analysts can confirm what customer data segmentation tactics were the most relevant.

On the other hand, operational profiling focuses on creating a step-by-step guide on how to target and engage that specific customer profile for future reference. The mix of these couple profiling approaches can provide B2B sales and marketing teams with a standard yet useful manual on what profiles tend to respond well to a certain type of stimulus.

2. Predictive modeling

The second technique involves connecting different layers of data points that already show a behavioral pattern and analyzing the possible outcomes of several decision-making scenarios over a particular time.

Customer data segmentation will be able to define the variables that are responsible for a change in an outcome. Even when technology is not yet fully capable of creating flawless predictions, it is possible to determine at what time a prospect would be more open to answering a phone call, opening up an email, or accepting a meeting.

An internal data specialists team or an outsourced data partner would be required to partition the dataset into train and test sets and perform high-end information analysis, but if your company is aiming to work with the finest source of decision-bound data, this is a great way to go.

3. Customer state vector

This data segmentation technique promotes a 360-degree coverage of all the customer’s interactions with the products and services of the company and centralizes that information gathered from the involved customer acquisition departments.

The state vector crosses the audience data sources while assembling the most relevant insights in specific actionable batches like:

  • Transactional activity
  • Campaign response data
  • Suppression information
  • Acquisition channel
  • Communication history
  • Customer journey touchpoints

By processing the centralized data out of the complete interactions with a brand, a data-based organization would be able to design sophisticated models that could predict statistical outcomes from similar ICPs inside a certain target account list.

4. Event-driven marketing

This segmentation approach involves the analysis of particular moments that happened inside a sales funnel, learning about the positive and negative effects they had along the way, and consciously triggering such events in favor of the conversion.

Such moments can be divided into blatant events and latent events. The blatant type refers to one-time anomalies, an apparent random action, or unrelated decisions which can be easily pointed out by transactional systems and detection engines.

Latent events are more trend-based. The repetition of these actions may not be so simple to identify (especially for B2B high-end decision-makers) depending on the amount of processed data and the wideness of a time frame, but more complex data detection engines can be used to discover correlated event-driven patterns.

Popular Data Segmentation Tools

A segmentation strategy is as good as the data segmentation tool that supports it. There are a number of options when it comes to customer segmentation software.

Selecting the right combo (just one tool is usually not enough) also depends on what channels are picking the most data, the number of profiles your company works with every day, and what is your goal with the segmented data.

Here are a few examples of the top data segmentation platforms which could help your business to filter the best customer profiles:

CIENCE GO Platform (Data + Flow)

CIENCE GO Data is a sales intelligence platform that offers over 140+ million lead records from all industries to its users. GO Data validates the quality, freshness, and accuracy of the segments in databases while providing hand-picked insights for data segmentation processes.

CIENCE GO Flow is a web-event capture and data transport CDP for businesses. Utilizing sources of data and destinations, GO Flow is able to "see" all web events and accurately record every contact that visits your web property. From there, having the ability to analyze, manipulate (move), and segment this data offers value to businesses.


Key features:

  • Introduces CRM data analytics pattern-matching
  • Assembles ICP targeting research and insights 
  • Submits daily entries of new contacts
  • Validates records, email accounts, and active phone numbers daily
  • Connects account, persona, tech, social, and partner level knowledge
    Try CIENCE GO Data Now!


A well-known CRM, HubSpot segments customer data with static and active contact lists, which provides a contact scoring system to enrich your sales funnels. 

Key features:

  • Performs event-based segmentation
  • Allows a free CRM alternative with limited access
  • Gives a wide variety of segmentation features 
  • Provides marketing, sales, and service hubs


The Experian tool helps you leverage targeted marketing data and provides features that facilitate the finding, acquiring, and retaining of customers while allowing your team to track your marketing efforts from the very first contact with the lead.

Key features:

- Offers a 360-degree point of view

- Facilitates the download, upload, and enhancement of client files

- Connects multiple touchpoints, contact channels, and devices

- Supports cross-channel remarketing, site tagging, and data networks


Mailchimp is a marketing and email automation service that includes a customer segmentation tool. It helps you organize and manage segmented email marketing campaigns that target specific groups of contacts with personalized criteria.

Key features:

- Provides a strong filter set to separate customer profiles

- Connects audience data, marketing channels, and insights to CRM

- Includes advanced email marketing automation features

- Offers AI-powered creative assistant for custom designs

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How to Improve Your B2B Data Segmentation

No guide would be complete without actionable advice. By integrating the following data-driven segmentation tips into your marketing and sales funnels, you can secure that your lead generation campaigns are targeting the right prospects through the perfect channels:

1. Prevent data decay.

Data decay refers to the unavoidable process of data deterioration through time. Inaccurate, incomplete, and outdated information may snowball out of control if your sales and marketing teams are not careful enough to keep your CRM fresh. The most important data quality characteristics you need to keep in check are:

  • Accuracy: How real is your data?
  • Completeness: How rich is the information in your pipelines?
  • Consistency: How precise are your data sources? 
  • Timeliness: Is your data updated? 
  • Validity: Is your data truly helping what you aim to measure? 
  • Uniqueness: How exclusive is the data you work with?

Software malfunctions, human errors, or the inability to keep up with the flow of the incoming information are elements that jeopardize the effectiveness of data-based decision-making. 

Set up data quality standards, organize all existing data in one place, and be sure to form a data-friendly culture in your organization to keep your databases healthy and sound.

2. Work with first-party data.

First-party data is information that was directly shared by a prospect through an application form, direct message, or blog subscription. This means that the contact is truly interested in your product and has a higher potential of becoming a client if managed correctly.

Second-party data is the type you can acquire from a business partner. These databases still hold a pretty high level of accuracy and they were obtained with customers’ consent at most times. 

On the other hand, third-party data is purchased from data aggregations and its source is usually hard to confirm, so its reliability and privacy regulations are highly questionable.

Even when third and secondary data parties may seem solid, nothing compares to working with first-hand information, and while it may require more time and effort to process, employing first-party data in your data segmentation tactics will prove its worth.

3. Integrate a customer data platform.

A customer data platform (CDP) is an interactive database that automatically collects, segments, and enhances the sales and behavioral data of your customers. This tool is almost obligatory for every B2B company that aims to create and deploy authentic ideal customer profiles to nurture their target account segmentation lists.

Such software can filtrate data from all your active digital channels like websites, apps, digital assistants, and marketing clouds. It collects demographic, psychographic, behavioral, firmographic, and transactional data in real-time. 

CIENCE GO Data is a great example of a customizable CDP that allows your team to dissect vast databases through clear, easy-to-click, relevant criteria while attaching notes, insights, and advice from our real-world sales professionals.

4. Prioritize your data segments based on value.

The main goal of data segmentation marketing is to help your team to craft down-to-earth customer profiles that could enter your sales pipelines so they can be targeted with hyper-personalized engagement actions. 

For this reason, your sales teams may find it useful to assemble your data segments according to the following tiers:

  • Top-tier: These customer profiles would be the most profitable and have shown significant potential for a fast-paced conversion.
  • Mid-tier: This segment has shown a decent amount of interest in your product. The sale could occur within the next twelve months, and the decision-makers will need to be reached out at a monthly rate.
  • Bottom-tier: These ICPs would gain value from your product, but they do not have the intent to purchase at this moment.

Utilize Data Segmentation for Business

Data not only grows larger and more complex as time flows, but it also demands more sophisticated methods to fully wring out all the value it holds. For this reason, B2B data-driven companies will be forced to assemble their alike profile accounts in more reduced, customized groups.

On the bright side, there's no doubt that upcoming profiling technologies will be able to provide businesses with near-perfect buyer personas. This means that new business acquisition teams will have an easier time engaging with their ideal customers due to the mutual realization that the product and the need should make a perfect match.

Marketing and sales reps should be able to understand the importance of segmenting, analyzing, and taking care of their current data, for the knowledge they hold today will become the foundation of a solid deal-closing campaign tomorrow.

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