Unstructured data—emails, notes, transcripts, LinkedIn messages—is spread everywhere across companies. Particularly in sales, it holds the key to understanding customer needs, spotting risks, and making predictions. But extracting meaningful insights feels impossible. Critical details stay trapped in conversations, scattered across platforms, or buried in messy logs.

This isn’t just frustrating—it’s a roadblock for revenue teams trying to optimize their processes and forecasts. This article aims at breaking down the problem, the opportunity, and how solutions like automated data enrichment can turn unstructured data into a competitive advantage.

The Challenge: Valuable Insights Stay Out of Reach

We’ve spoken to numerous Sales, RevOps and Customer Success Leaders on the problem and discovered a pattern in the pain points they mentioned.

Scattered and Inconsistent

Unstructured data exists in email threads, call transcripts, legacy software, and spreadsheets, each with its own format. Bringing it together is time-consuming and error-prone. Pranay Walia, RevOps specialist at Cremanski & Partners, describes how mismatched systems leave companies scrambling to compile information, often losing insights along the way.

Manual and Time-Intensive

Without the right tools, analyzing unstructured data takes hours of manual effort—searching, reviewing, and guessing. This approach introduces bias and inconsistency. Jeet Thakkar from the fast-growing game tech company Lysto highlights this frustration, mentioning that his product team struggles to make sense of large volumes of transcripts and notes to fully grasp what they should be focusing on next.

Trapped in Conversations

There is another category that must be considered: Information in people's heads. The VP Revenue Operations of a large ad tech company points out: "Much of the most important information never makes it into the CRM. Notes, mental recollections, and informal conversations hold details like decision-maker names, pain points, and next steps." This is a curse for processes upgrades and automation, and we will get back to this matter towards the end.

The Opportunity: What Unstructured Data Can Deliver

Having painted a dark picture, let's turn to the land of dreams. In short, unstructured data holds immense potential if used correctly. Unlocking it provides:

• Deeper Customer Understanding: Unstructured sources capture nuance—how customers describe pain points, their sentiment, and subtle hesitations. Tommy Saeys, RevOps expert at the software firm Equalture, emphasizes the importance of understanding this context to truly grasp what customers need.
• Improved Sales Enablement: Calls and emails reveal successful tactics, objections, and areas where sales scripts or training need adjustment. Gerald Steinhaus, revenue mastermind at the consumer-focused proptech firm Certa, mentions the value of analyzing these patterns to make data-driven improvements in sales processes.
• Enhanced Forecasting and Visibility: Transcripts often mention hidden buying signals, like stakeholder involvement or budget approval processes. Nicholas Reid from Customer Alliance shares how identifying additional decision-makers through transcript analysis helps refine predictions and address risks earlier.
• Personalized Interactions: Reviewing past interactions helps tailor communication. Customers expect companies to remember their preferences, and unstructured data makes this possible.

Bridging the Gap: Structuring the Chaos

Transforming unstructured data into actionable insights requires intelligent automation that works without adding burdens to the sales team. With lots of nuances taking place in between, it would be wrong to promise to automate everything. That said, we have been able to address a few common pain points raised by our customers.

Turning Messy Data into Structured Insights

Unstructured inputs like notes, emails, and transcripts are processed to fill CRM fields and enrich data with minimal manual effort. Examples include:

• Extracting information (e.g., decision-maker names, next steps) from notes and linking them to deals.
• Mapping roles (like CEO or Legal Counsel) from email participants, even if they aren’t explicitly logged.
• Analyzing actions, like follow-ups, to evaluate deal momentum.

This ensures data isn’t just captured but organized into a complete view of your pipeline.

Building Connections Between CRM Objects

Graph-based modeling links contacts, deals, accounts, and activities. Missing relationships are identified and addressed. For example:

• Emails mentioning decision-makers are automatically tied to the correct deals.
• Activities related to high-value deals are cross-referenced with other CRM data to ensure no interactions are missed.

Intelligent Feedback Loops

Manual input is reserved for when it matters most. Orphaned, low-value gaps are deprioritized, while significant gaps prompt minimal clarification only when automatic resolution fails. This efficiency reduces team workload while maintaining data quality.

Practical Benefits for Revenue Teams

This structured approach delivers real-world advantages:

• Better Forecasting: Filling data gaps and connecting objects leads to more accurate predictions.
• Risk Reduction: Highlighting inconsistencies, like missing champions or decision-makers, helps sales teams address risks proactively.
• Improved Data Hygiene: Automated updates keep CRM data accurate without adding extra tasks.
• Better Prioritization: Identifying high-priority deals based on enriched data ensures sales efforts are spent where they’re most effective.

Bringing It to Life: Real Examples

Imagine an email chain referencing a “CEO” not listed in the CRM. The system identifies the CEO, connects them to the relevant deal, and infers their role, all automatically. Similarly, call notes hinting at budget-related topics are flagged as part of the deal’s decision-making process.

These and many similar processes run quietly in the background, enhancing efficiency without disrupting workflows.

Making Sales Frameworks Work (With Humans)

To make this worth your while, we have to come back ton one critical part that we haven’t covered above: What’s with the information in people’s heads?

The reason why sales cannot be fully automated is twofold: Whenever trust plays a role (which it almost always does for high-ticket B2B deals), people don’t want to speak with machines but to a real person. And because of that, the sales person often has knowledge that is not captured in data. Let’s illustrate this with two examples:

• The sales lead knows that the decision maker Mike is friends with influencer Monica and although Monica was never part of the conversation he knows her well enough to just give her the nudge when needed.
• Although all signs of the deal scream “yes”, the sales lead overheard the decision speak about a buying freeze at a conference.

To the critical observer, both examples add a clear direction in favor or against winning a deal but such insights might not immediately reflect in the raw data attached to the CRM system. As a consequence, both are opportunities for a salesperson unwilling to adopt a new method to claim “this will never work”. And if taken at face value, they would be right.

Having said that, even if your team doesn’t use a qualification framework like MEDDPICC in their own work, extracting information from unstructured data nonetheless effectively aligns with modern B2B sales strategies:

• Identifying pain points in call transcripts supports the “Identify Pain” step.
• Recognizing decision-makers in email threads strengthens “Economic Buyer” tracking.
• … and so on.

Leveraging a sales framework ensures your pipeline gains structure, no matter how your sales team operates. And any risks, open loops, or potential red flags can always be countered with knowledge that has not been captured in the data (or by the system analyzing it).

If you want to see this in action, check out our new Deal Insights.

Conclusion

Unstructured data represents both a challenge and an opportunity for revenue teams. While valuable insights often remain trapped in emails, conversations, and scattered systems, modern automation tools can now extract and structure this information effectively.

By combining intelligent data processing with human expertise, companies can enhance their forecasting accuracy, reduce risks, and make better-informed decisions. The key lies in finding the right balance between automated data enrichment and human judgment, ensuring that critical information doesn't slip through the cracks while maintaining efficient workflows.

This approach not only improves data quality but also empowers sales teams to focus on what they do best: building relationships and closing deals.