Structuring Unstructured Data: Making Sense of Sales Activity

Structuring Unstructured Data: Making Sense of Sales Activity

Daniel Hall 27/02/2024
Structuring Unstructured Data: Making Sense of Sales Activity

If you manage an enterprise sales team, you likely grapple with an overwhelming amount of disorganized data.

Between random notes scattered in CRMs, call recordings saved across devices, and deal updates delivered through unstructured email threads and meeting notes - critical customer context lives across too many disparate systems.

This unstructured data overwhelms rather than provides insight. Managers struggle to paint a clear picture of true pipeline health and which deals demand the most attention. Reps lose track of key next steps, playbooks, and objections raised in past deals that could inform current ones. Without a structured approach to curating tribal knowledge, sales cycles stretch longer than required even in qualified deals.

Making sense of this data requires a strong and consistent sales data foundation.

The High Cost of Messy Sales Data


The impact of poorly organized sales data manifests in numerous, seemingly unconnected ways across the sales process:

  • Inconsistent quarterly forecasts with unreliable win/loss predictions

  • Reps constantly need hand-holding coaching despite experience level

  • Best practices are not sustained across teams leading to reinvention of the wheel

  • Long sales cycles caused by scattering critical customer/negotiation context

Behind each of these challenges lies informational spaghetti – with customer insights, playbooks, objections, and key deal milestones buried under layers of unstructured data from different sources. Sales leaders then struggle to cut through the clutter to identify core issues and possible solutions.

Without a scalable way to structure tribal knowledge, sales teams operate inefficiently at great cost to the company.

Unlocking Value in Unstructured Sales Data

Transforming relatively unstructured sales data into organized knowledge delivers four key advantages:

  1. Enables faster onboarding: Structured best practices help new reps get productive quickly by leaning on what works based on past deals.

  2. Improves sales coaching: Managers can zero in on rep strengths/development areas with structured customer engagement and pipeline health data.

  3. Powers reliable forecasting: Structured data on where deals stand against sales methodology milestones provides accurate predictability.

  4. Retains institutional knowledge: Keeps best practices, objections handling, etc. discoverable through the organization instead of siloed in rep heads.

Manual approaches to structuring tribal knowledge simply don't scale across teams and tools. Sales ops leaders need an automated solution that can summarize and structure sales activity without extensive oversight.

Automated Deal Summaries: Structuring Notes at Scale


The right AI sales acceleration tools can automatically structure unstructured sales data by summarizing meeting notes, emails, and call recordings into a consistent framework.

For instance, the MEDDICC methodology tags each customer interaction across key areas, retaining critical context in a structured form:

  • M - Metrics: Captures business value metrics for customers – pricing waterfalls, ROI analysis, and so on

  • E - Economic Buyer: Identifies key budget holders and decision-makers

  • D - Decision criteria: Notes stakeholder motivations – business issues they hope to solve

  • D - Decision process: Milestones for deal approval mapped to the sales process – RFP release, demos scheduled, etc.

  • I - Identify pain: Outlines core business issues the customer seeks to alleviate

  • C - Champion: Call out internal advocate backing the supplier’s solution

  • C - Competition: Lists alternative solutions competitors and customer’s stance.

This structure transforms raw sales notes into a deal progression blueprint that sales managers can quickly parse to gauge pipeline health. By auto-tagging customer interactions, the right sales data foundation makes unstructured data easily searchable while retaining key context that is often lost.

Now reps can instantly check objections raised in the past to refine current pitches without extensive note review. Managers get an impartial heat map of where teams spend time and how effectively – instead of selective data reporting. Automated deal summaries boost sales productivity, forecasting accuracy, and knowledge retention enterprise-wide.

Move Beyond Data Clutter for Sales Excellence

In the modern distributed workplace, sales teams struggle with constant information overload. Automated approaches can reliably structure tribal knowledge instead of losing it to employee churn or misplaced notes.

The right sales acceleration solutions automatically structure sales activity into consumable nuggets – helping managers get decision-ready insights in seconds instead of hours parsing through unstructured data. Sales leaders seeking excellence through scalable oversight finally have the tools to cut through informational clutter.

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Daniel Hall

Business Expert

Daniel Hall is an experienced digital marketer, author and world traveller. He spends a lot of his free time flipping through books and learning about a plethora of topics.

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