The B2B Customer Data Index

A customer-centric approach to building marketing and sales stacks

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Who Should Care about This Index?

As B2B buying behavior continues to evolve, businesses must also adapt and make it easier for their buyers to buy. CRMs, marketing automation, and a multitude of predictive analytics platforms have created a tremendous opportunity to understand more about who’s buying from your company than ever before. But without the right data--and the right systems bringing it all together--even your best efforts are unlikely to succeed. Gathering the right data requires building a tech stack aligned not just with your business needs, but also with the demands and behaviors of your target customers.

Having spent years obsessing over customer data, CaliberMind has developed a unique perspective of marketing and sales tech stacks and the data that powers them. We strive to help our customers uncover the most valuable data sources and then leverage that information to improve marketing and sales outcomes, artfully integrating the various tools in our customer stacks.

If you’re a marketing, sales or an ops person thinking of adding another point tool to your stack or buying data from another data vendor, this table will be a helpful guide in avoiding the disjointed approach most companies take regarding customer interactions to build the most efficient, effective, and customer-centric tech stack possible.

Why Do We Need Another Marketing/ Sales Landscape Map?

This is by no means a landscape map. We actually used the four landscape maps under the Resources section below to put this table together. Given the vastness and diversity of today’s martech and sales tech market, the table only includes a sampling of the key players in the mid-market/ enterprise space and is sorted based on the pillars of the B2B Customer Data framework. Many companies in this table offer a wide range of solutions and services, but to demonstrate how each technology adds to the disjointed nature of customer data, we categorized them based on primary customer data type.

If you’d like to learn more about why we put this table together read our CEO’s post B2B Buying is Broken. Here is How to Fix It


We have organized the B2B Customer Data into four categories, doing our best to place each company into the most relevant group.

  1. Technology
    • MarTech
    • Sales Tech
    • Customer Success Tech
  2. Data Sources
    • 1st Party Customer Data Collected and Owned by the Company
    • 2nd Party Customer Data Shared Between Partners
    • 3rd Party Customer Data Sourced and Purchased from Data Vendors
  3. Data Types
    • icon_Firmographic.pngFirmographic
      (What Accounts)
      This data defines the universe, but is lacking any mapping of the buying center and / or the buyer personas.
    • icon_Technographic.pngTechnographic
      (What Tech Accounts Use)
      This data allows you to rank buyers on martech maturity and tailor your offering to specific technology / platforms (for example Salesforce Customers).
    • icon_Intent.pngIntent
      (What Accounts Are Reading About)
      Data collected by publisher networks, either at the IP level or through user registration and cookies, that show the articles companies read, content they download, and even their site searches. Almost always 3rd party.
    • icon_Engagement.pngEngagement
      (What Buyers Do)
      This data allows you to time the interaction and engagement. Almost always 1st party.
    • icon_Psychographic.pngPsychographic
      (How Buyers Think)
      This data lets you win deals, it includes your buyer mindset, pain points, decision making style, and even personality.
    • icon_Predictive.pngPredictive
      (What Would Happen)
      Blended data offered by predictive vendors, mostly at the top of the funnel for lead scoring and look-alike audiences.

Your Feedback is Welcome

We hope that our categories provide a good taxonomy for the ongoing evolution of the market and welcome any feedback or suggestions for the next iteration of the table.

Think there’s a key category missing? That there’s a better way to collect data or that we misrepresented a product? Let us know. We would greatly appreciate if you’d submit all of your comments and feedback directly via this TypeForm (as opposed to Twitter, LinkedIn or Email) so that we can easily aggregate and use them to improve the table for future years.