Scratchpad vs Dooly for a PE Portfolio Company's Sales Team

Pulling the CRM for the board deck reveals the actual state: deals sitting in the same stage for months with no updated close date, reps using stage names inconsistently so "negotiation" means something different depending on who entered it, and several deals that closed weeks ago still marked open. A sponsor reviewing this for the first time will not trust a forecast built on it, and rightly so, since the underlying data was never disciplined enough to support one.

A full bulk cleanup, going through every open deal in a grid view, correcting stale stages, confirming real close dates, and closing out deals that already won or lost, is exactly the kind of one-time, high-volume cleanup Scratchpad is built for. Doing this record by record in a standard CRM interface would take days; in a spreadsheet-style grid, it is realistic to finish in an afternoon, which matters when the next board meeting has a fixed date that is not moving to accommodate a data cleanup project.

Once the historical mess is cleaned up, the harder problem is keeping new deal activity from drifting back into the same state. If the root cause was reps not updating records after calls, a call-capture tool that pushes structured fields, like next step and close date, into the CRM automatically addresses the actual behavior gap rather than hoping reps develop better habits on their own.

Neither tool fixes inconsistent stage usage by itself. Before either goes live, write a one-line definition for each pipeline stage that says exactly what has to be true for a deal to sit there, and require every rep to use the same definitions. This single step, agreeing on what "negotiation" actually means across the team, does more to make a board-ready forecast credible than any software purchase, and it costs nothing but a meeting.

Define these stage rules so the board's forecast holds up:

• Write one clear definition for each pipeline stage, with entry criteria that two different reps would apply the same way.
• Require a next step and a real close date on every open deal, and clean out deals that already closed or died.
• Assign realistic close probabilities by stage, based on history instead of the founder's gut feel.
• Name an owner for pipeline data quality so the standard survives past the first board deck.

A sponsor board usually wants pipeline coverage ratio, win rate by deal size, and average sales cycle length, none of which are computable from a CRM where stages are inconsistent and close dates are stale. Getting the underlying data clean enough to produce those three numbers reliably is a reasonable first-90-days target after an acquisition, and it is a good test of whether the pipeline cleanup and new discipline are actually holding, not just looking better for one board meeting.

A sponsor managing several portfolio companies, each running a different CRM with different stage definitions, cannot cleanly roll up pipeline data across them without someone normalizing the numbers by hand every quarter. That is a real limitation worth naming honestly rather than pretending either tool solves it: Scratchpad and Dooly both operate within a single company's CRM, and cross-portfolio standardization is a separate project the sponsor's operating team typically has to drive directly.

The riskiest pattern after a first cleanup is treating it as a one-time project completed for a single board meeting rather than an ongoing standard. Name a specific owner, whether the head of sales or a dedicated operations hire once the company can support one, who is accountable for pipeline hygiene between board meetings, not just in the week before one. Without that ownership, the same stale-data problem tends to reappear by the second or third board cycle, just as convincingly as the first time.

The first board meeting after a cleanup usually shows a believable but still rough forecast, since new discipline has not yet had time to produce a full sales cycle of clean data. The second board meeting is the more meaningful test, since by then the numbers should reflect deals that were opened, worked, and closed entirely under the new stage definitions rather than partially reconstructed from old habits. A sponsor who expects a perfect number on the first attempt is setting the portfolio company up to look like it failed at something that was always going to take two full cycles to actually prove out.