CASE FILE 10CLIENT GENERALISEDALL DATA MOCKED

A clear view of the sales pipeline.

A sales team used Pipedrive but ran on gut feel — deals went quiet, losses were unexplained, and there was no fair way to compare advisors. This project made the pipeline measurable.

CLIENTSALES TEAM
MODELCRM FUNNEL → CLOSED DEALS
STACKPIPEDRIVE · LOOKER
ENGAGEMENT5 PHASES · FULL SYSTEM
STATUSLIVE · AUTOMATED
THE BOARD — OVERVIEWAI-BUILT · LIVE HTML
EXHIBIT A
1

The board mirrors the team’s pipeline: six stages with real deal cards.

2

Tags on each card show which deals are hot and which have gone quiet.

3

The strip at the bottom shows the key funnel numbers.

The Brief

Deals went quiet and nobody noticed.

The CRM had the data, but the team worked from memory. Deals sat untouched for weeks, lost deals were marked “other”, and advisor performance was a matter of opinion.

We rebuilt the funnel as a measured board: conversion between each stage, days a deal sits idle, real loss reasons, and a fair comparison of advisors.

THE GOAL: see where deals get stuck, why they are lost, and who closes them.

✗ THE PROBLEMS

  • ✗ Stage conversion rates unknown
  • ✗ Idle deals invisible until lost
  • ✗ Loss reasons logged as “other”
  • ✗ Advisor comparison was based on memory
  • ✗ Lead quality vs execution never separated
The System

How the system works.

Pipedrive data is pulled daily. SQL calculates stage conversion, deal age and loss reasons, and Looker Studio shows it as a board in the team’s own stage names, with alerts for stuck deals.

Pipedrive API

Deals, stages, activities, owners, loss reasons.

→

Models

Stage conversion · idle-days · velocity · loss taxonomy.

→

Looker Studio

Kanban truth board + advisor, sources and loss pages.

→

Action Layer

Stale-deal (30d+) and SLA alerts to Slack.

The Process

The process, step by step.

01

Audit

  • Measured real stage conversion for the first time
  • Rebuilt the loss-reason taxonomy from “other”
02

Measurement Plan

  • Idle-day thresholds per stage, agreed with the team
  • Advisor fair metrics the team agreed on
03

Implementation

  • Pipedrive API → modelled stages + velocity
  • Campaign-source joins for lead-quality splits
04

Dashboard Build

  • Kanban layout in the team’s own stage names
  • Loss donut + advisor share views
05

Automate & Optimise

  • Stale-deal alerts at 30 days
  • Weekly pipeline digest to Slack
The Build

Advisors and stages, in detail.

Two more pages: the advisor table and the alerts page. Built in HTML with sample data.

PAGE 02 / 06 — ADVISORSLIVE HTML · MOCK DATA
EXHIBIT B
Advisor Deep-Dive

Each advisor, by the numbers.

  • ■ The table shows leads, follow-ups, bookings, wins and deal time per advisor.
  • ■ The loss chart shows the top reasons deals are lost.
  • ■ The list names deals that have sat idle for 30+ days, with a next step.
PAGE 06 / 06 — ALERTS + AILIVE HTML · MOCK DATA
EXHIBIT C
Automation Layer

Stuck deals raise their hand.

  • ■ Rules flag deals that pass 30 idle days and leads that miss the response-time target.
  • ■ The Monday summary separates lead-quality problems from follow-up problems.
  • ■ Each alert arrives with a list of deals and the suggested move.
Deliverables

What was delivered.

The Results

The results.

STUCK DEALS, SEEN.

Deals that sit too long are flagged before they quietly die.

LOSSES, EXPLAINED.

“Other” was retired. Real loss reasons now drive real fixes.

FAIR COACHING.

Advisor reviews use stage-by-stage numbers, not memory.

NOTE: CLIENT DETAILS GENERALISED · ALL NUMBERS ARE SAMPLE DATA · DASHBOARDS ARE HTML RECREATIONS

Next Steps

Want this for your business?

If your CRM knows things your Monday meeting doesn’t, this board closes the gap.

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