The state of B2B data 

B2B marketing has a data problem. 

And no, I am not talking about scarcity of data (in fact there’s an abundance of it). I’m talking about the gap that exists when it comes to connecting data for B2B go-to-market motions. 

The problem is as B2B data continues to explode, this gap continues to grow. What’s happening is vast reservoirs of data are getting wasted as they remain hidden and unused in unstructured, disconnected, and siloed states across the organization.  

Basically, our data is getting unused, resulting in a less than pleasant buyer’s experience. And things aren’t getting better. 61% of tech marketers in Foundry’s 2023 Role & Influence of the Technology Decision-Maker report agree that the purchase process is becoming increasingly complex. 

Connected data in marketing

Anytime we hear “connected data” for B2B marketing; we normally hear it in the context of attribution and ROI measurement.  

While that is certainly true; there are several other use-cases where we need connected data, such as:  

  • Customer experience 
  • Audience targeting 
  • Campaign optimization 
  • Personalization 
  • Attribution (no doubt) 
  • Pipeline Forecasting 

In B2B marketing, “connected data” refers to the integration and utilization of data from various sources and touchpoints to create a unified and comprehensive view of a company’s target audience or customer base. It involves gathering, analyzing, and leveraging data from multiple channels and platforms to gain insights, make informed decisions, and optimize marketing efforts. 

In contrast to B2B, B2C has been quick to adopt connected data infrastructures by leveraging CDPs and building a 360-degree view of the customers. In fact, most major B2C brands have a unified customer profile. This single view of the customer results in a highly personalized consumer experience through personalized digital campaigns. For example: you buy a T-shirt at GAP, almost instantly you find your inbox filled with emails showcasing their upcoming promotions. Or, when you step into a shopping mall, you might receive push notifications via SMS or in-app alerts, perfectly tailored to your preferences. Even a visit to their website yields personalized content and recommendations, all thanks to this connected data ecosystem. 

B2B certainly doesn’t have that level of sophistication… However, there are valid reasons for that.  

With 6+ month long sales cycles, 25 people making a decision, and more revenue at stake, the B2B buying journey is complex. And to add a layer of obfuscation, data is coming in from dozens of channels and an average of 12 martech tools used by marketing teams today. The problem is, as helpful as these tools are, they often don’t do a great job of talking to each other, or even speaking the same language. 

Challenges in B2B data integration

Instead of connected data, B2B companies typically rely on their CRM as the single source of truth for all customer-related things.  

Even with connectors from marketing automation platforms, ABM tools, and sales engagement platforms don’t have the robustness of a CDP. One of the key issues being that most activation and buyer engagement happen outside of the CRM and are almost impossible to store and track within the CRM systems. This disjointed data landscape poses significant challenges for B2B enterprises, especially those striving for integrated marketing. 

Integrated marketing has been flavor of the day for a lot of B2B enterprise marketers for a while now. A quick search with ChatGPT and this is the definition we get: “Integrated Marketing is a holistic approach to marketing communication. It ensures that all forms of communications and messages are carefully linked together. At its core, it’s about aligning all communication channels, from advertising and PR to direct marketing and digital, to work in unison.”  

This leaves us with one large issue as marketers… 

You simply cannot run a properly integrated marketing campaign without a connected data infrastructure.  

Building a connected data infrastructure

Consider the following scenario: you’re a B2B cyber security company running an integrated campaign targeting the top 500 Financial Services companies globally. Let’s say you’ve crafted a whitepaper and intend to promote it through a multi-channel strategy, encompassing display ads, paid social, content syndication, and webinar series. 

The first challenge you’ll face is how to create audience segments for each of these channels. Do you use a list of URLs or IP addresses? What about the buying teams within these orgs, how do you target them? And once you build an audience, how do the different channels talk to each other and optimize targeting based on engagements on other channels? 

Here is where you run into the data problem. When dealing with fragmented and outdated data, it becomes nearly impossible to pinpoint the precise individuals to target within various buying teams. Additionally, it’s challenging to discern which channels are resonating with your audience and igniting meaningful engagements. 

Instead, connected data might look something like this…  

The CIO at Big Bank has seen your ad five times in the past week. Because of this, an automated response sends her an email invitation for your upcoming webinar. However, if she already registered for the webinar, the system would intelligently exclude her from the content syndication program to avoid redundancy. This is exactly what B2B companies should be doing. It’s a perfect example of what a truly connected data infrastructure might look like. 

Companies, like SAP, have taken substantial strides through initiatives like the Crystal Ball Project. They’ve assembled teams of data scientists to build extremely sophisticated models by ingesting both 1st and 3rd party data across multiple sources. The outcomes include a 300% increase in deal size and a 2-3X increase in conversion rate from pipeline to revenue.  

But let’s be realistic… not every B2B marketing organization can build a data science team. However, there are still actionable steps you can take towards creating a connected data system.  

Account and contact level data

Connected data systems mean account and contact level data must be connected. 

Account level data encompasses information about the organizations or companies you are targeting. But without contact data, it’s impossible to truly understand the context behind your accounts and who key decision makers are. 

Contact-level data focuses on the individuals within your target organizations. However, when it comes to contact-level intent, it’s not just about knowing that “Jane,” a marketing manager, downloaded a piece of content. It’s about understanding Jane’s place in the buying team for a martech solution. Is Jane a decision maker, an influencer, or an individual contributor in the buying process? What’s important is having a deep understanding of a contact’s role, seniority, and function within their organization.  

Imagine you’re a marketer for a B2B company, and you’ve identified that “Jane” holds a high-ranking position that gives her decision-making authority. Knowing this, you can tailor your engagement accordingly. Since she’s the decision maker, focus on presenting in-depth product information and value propositions.  

This deep understanding of contact-level data, tied to accounts, enhances your overall context of an organization’s dynamics. This not only empowers you to engage individuals more effectively but also enables you to grasp how their role aligns with the overarching objectives of their organization, allowing you to map contacts to specific buying teams. 

Data unification 

Your data must be set up in a way that allows you to monitor all the account and contact behaviors within your sphere, connecting where data is coming from to how it moves through your system. This means that from the moment you receive data, such as a form submission, you can pinpoint its source, and track lead progression as they interact with various touchpoints. This thorough monitoring continues until you hand it over to your sales team, who, in turn, can follow the lead’s interconnected path. 

For example, you’re a B2B software company receiving a form submission on your website. To ensure a streamlined data flow, you set up a direct data pathway: 

  • Instant data capture: when a contact submits their info, your system grabs details like company name, industry, and how they found you. 
  • Tagging and integration: leads get unique tags (e.g., “LI2023” for LinkedIn) in your CRM. 
  • Progress tracking: your system tracks their interactions, like downloading e-books or attending webinars. 
  • Sales alert: high-intent leads trigger sales notifications with comprehensive profiles. 
  • Personalized engagement: sales tailors their approach based on lead engagement, boosting conversion chances. 
  • Data analysis: after conversion, you analyze the journey to pinpoint top-performing channels. 

Another way to ensure this is by having all contacts and accounts that come in follow standardized procedures, so data integration flows seamlessly.  

Without a proper process for normalizing and collecting data, achieving connected data is considerably more challenging. For instance, say you encounter because of the way your Salesforce communicates with HubSpot, HubSpot now has approximately 40 distinct Adobe accounts. This hinders your ability to measure Adobe’s performance as a unified entity. Rather than analyze one Adobe account, you’re forced to analyze 40 separate instances of Adobe. 

A well-structured process ensures that all Adobe accounts and contacts are accurately categorized and connected within your database. This provides a unified view of Adobe’s engagement and behavior, allowing you to easily access a single, consolidated Adobe account profile.  

Adaptable infrastructure 

You need to be adaptable. What I mean by that is having the ability to optimize your strategies in real time based on what’s happening. 

Say you’re a B2B cyber security company running an ad campaign. During the campaign, you start noticing significant interest in data encryption content. This suggests that your audience is particularly interested in data encryption, and they’re responding positively to ads shown alongside such articles.  

With a flexible, connected data infrastructure, you would react to this in real time by contextually optimizing for placements within data encryption related articles. You might even adjust the ad copy and imagery to further align with the audience’s interests.  

Or say you spot an account displaying significant intent visiting your booth at an event. And at the event, your sales team logged information saying “Hey, we spoke to Jane at Pfizer during SaaStr.” A connected data infrastructure seamlessly funnels this data point into your system. From here, instead of having Jane in the audiences she started in, she is targeted with ads built specifically for her heightened interest level. 

This level of adaptability and granular targeting is precisely what a connected data system provides, allowing you to respond dynamically to the ever-evolving landscape, even drilling down to specific geographic regions as your strategy and audience demands. 


We’ve all heard the phrase “data is the new oil.” While this certainly is true, as B2B marketers we run the risk of misusing this precious commodity without a connective tissue tying the pieces together. The foundation of every marketing campaign should be built upon a connected data layer that ties firmographic, behavioral, and campaign engagement data together into one consolidated view. If done right a connected data layer should enable precise targeting, personalized engagement, and measurably better campaign outcomes.

So, as we continue to refine our marketing strategies and adopt new tools, let’s prioritize building a connected data infrastructure as the cornerstone of our marketing excellence.