Rev-Lens: go-to-market intelligence

Who this is for

Three teams. Three versions of one problem.

Each team’s data provided a different perspective and resulting set of corrective actions, without addressing the real execution issues.

Sales leadership

Where and how do we win?

How do I model the engagement behavior of our best reps to improve overall conversion and win rates? How much of my funnel has the characteristics where we win?

Marketing leadership

Which pipeline is actually turning into revenue?

Which campaigns, segments, and lead sources produce deals that close, not just MQLs that get accepted? What language and objections are showing up on real sales calls, and does our messaging match what customers care about?

Product leadership

Is the market hearing what we built?

Which features and use cases drive deals forward, and which ones stall them? What are buyers asking for that we don't have, and where are competitors beating us on the call?

This is not a sales analytics challenge

Sales, Marketing and Product each hold part of the problem, and each was solving their own version of it. One shared dataset is what lets all three work on the same one.

The fragmentation problem

Five datasets of the same opportunity. Nothing that reads them together.

Every system captures specific engagement data on the opportunity, but none of them were designed to be joined to the others. The picture only ever exists in fragments, and the CEO is left to find the truth.

CRM

Stage, amount, close date, and whatever a rep had time to type.

Recorded calls

What was actually said, by whom, and what was promised in the room.

Email

The quiet half of the deal: quotes, redlines, procurement, and silence.

Slack / Teams

The internal decisions that never reach a system of record.

Intent signals

Marketing automation, site traffic, third-party intent and product usage.

Each of these systems wants to be the analytical consolidation platform within your tech stack, but they don't provide the historical analytical insights across your GTM.

Where the reporting cycle breaks down.

Sales, marketing and product each arrive with a different view.Three functions, three data sets, three sets of manually collected anecdotes.
The answer to the important question is always a request into RevOps.Anything spanning two systems becomes a RevOps request and a manual piecing-together of data sets.
Reporting describes what happened, not why.The dashboard shows the number moved. Explaining it still depends on anecdotes and manual inspection of the opportunity.
The reps are closest to the deal, but don’t have the broader perspective on why they won or lost.The good reps leverage experience and intuition and know when a deal is dead; the rest hold on hoping it reactivates.

Where the differentiation is

Incorporating GenAI within a current application will not provide the broad GTM intelligence.

An agent inside an application can only see that application. Comparing across systems is not an automation problem. It is a data problem, and it has to be solved one level up.

Every component of your tech stack is incorporating GenAI

They can pull data from other apps on a specific opportunity, but cannot analyze your complete GTM data set. You should take full advantage of the app’s AI enhancements, but you need a solution for broader GTM insights.

What in-application agents do well: workflow

They remove friction where the work happens and give insight on a specific opportunity: summarizing a call, drafting a follow-up, surfacing the next task. Genuine time saved.

What we leave behind: agents that are not trapped

The agents your vendors ship are trapped inside their own applications. The Rev-Lens agents run across the joined data and they become yours.

What we do

Consolidate, join, analyze.

Rev-Lens is a services firm. We build inside your infrastructure, on the GenAI platform you already run, and expose the deep insights on your GTM execution. The data lake and the agents stay with you.

1

Consolidate

One governed copy of the engagement data your teams already generate, drawn from the systems in your tech stack. Tool agnostic. We work with whatever you run.

2

Join

Calls, records, threads and activity matched to the same accounts and opportunities, so a question can cross a system boundary without becoming a project.

Analyze

We work the joined data against the questions the business is actually asking, and return findings with the evidence attached.

How an engagement runs

Five weeks to deliver deep insights on your GTM execution.

You leave with a prioritized set of findings on where your GTM execution is costing you conversion and win rate, each traceable to its data, each with a recommended action and a named owner.

First

Access and scope

We agree on the components of your GTM to analyze, establish read access to the sources you name, and stand up one governed copy of the data within your infrastructure.

Then

The analysis

We deploy prompting agents tuned to the Sales, Marketing and Product questions and run them against the joined data. They are built within your infrastructure and stay there.

To close

Findings and a decision

Identified insights presented to the business. Findings you can challenge, each traceable to its sources, with an explicit set of recommendations for moving forward.

Data is consolidated within your secure infrastructure, scoped to the sources jointly defined. Access is read-only. Your data is never pooled with another customer's and never used to train models. We put the specifics in writing before access is granted.

What you keep

The data lake, inside your own infrastructure. The agents, tuned to your business and GTM functions. Your team can run them after the engagement ends.

Nothing is licensed. The data lake is in your infrastructure, the models are in the LLMs you have licensed, and the agents are yours when we are done.

Case study #1

What our engagement identified for a customer: a need for sales tactic improvements when trying to fit a budget.

The four tests

Nothing reaches you as a finding until it answers all four. Anything that fails one is a signal, not a finding, and stays out of the report.

1 · What changed

A verifiable data point — not an anecdote or gut feeling.

2 · Why it matters

A demonstrated link to revenue outcome, stated in win-rate terms.

3 · Recommended action

One decision, with a named owner, that can be taken this quarter.

4 · Sources

Every claim traceable to the records it came from, so it can be checked.

62qualifying deals above $50,000 across a trailing 180-day window

14where the configuration was cut down to fit a budget

26where configuration was never attempted at all

0none of the 40 closed

          14 descoped to fit a budget
          48 other qualifying deals
          0 of the 14 closed
        

One account, five sources, one finding

Illustrative of the pattern across all 62 deals

CRM

Opportunity openedABOVE $50K THRESHOLD

Amount revised downNO REASON FIELD SET

Closed lostREASON: PRICE

Recorded calls

Budget ceiling statedDISCOVERY

Configuration removed to fitPRICING CALL

Email

Revised quote sentREDUCED SCOPE

Thread goes quietNO REPLY · 21 DAYS

Slack / Teams

Internal descope agreedNEVER REACHES THE CRM

Intent signals

Account engaged configuration contentPRE-CALL

Evaluation activity stopsPOST-DESCOPE

The finding · Competitive win rate

Deals that were cut down to fit a budget did not become smaller wins. They became losses.

Fourteen deals followed this shape across the window. Every one of them was descoped in a conversation, and none of the descoping decisions were recorded anywhere the forecast could see.

How many of the opps in your funnel share this characteristic?

What changedConfiguration was removed mid-cycle in 14 of 62 qualifying deals.

Why it mattersNone of the 14 closed. The descope was a loss indicator, not a concession.

Recommended actionChange the sales tactic, or defocus these opps quickly.

SourcesCRM records, recorded calls, email threads, internal chat, product activity.

Case study #2 sample output

What the Rev-Lens engagement provided.

Identify opps which included the sale of a specific product feature. One topic, one year of recorded calls at a network-security vendor: the scan returned every deal the topic touched, what are the sales friction points which need focus, and the call behind every claim. This is a real report with the account names changed.

2,470Calls scanned

29Opportunities found

15Still open

$2.09MOpen pipeline

1h 54mRun time

What is blocking it

Technical validation

Of the third-party detection engine. SE scoping calls to prove TLS and clustering behavior.

3 calls

Budget approvals

On the largest deals, needing phased rollouts to pass finance.

3 calls

Cluster migration mechanics

Expanding without breaking the policy namespace.

2 calls

Your revenue data is spread across your tech stack.