Sales Enablement

Turn win/loss patterns into data-backed playbooks

Analyze every deal outcome against the conversations that preceded it. See which patterns correlate with wins, which with losses, and generate playbooks automatically.

Won
Deals
42
Pipeline
$4.2M
Lost
Deals
28
Pipeline
$1.8M
Win rate by factor
Content Draftv3Ready

Enterprise Win Playbook

Play 1: Secure exec sponsor before proposal 89% win rate with sponsor vs 14% without. Intro champion's VP on the second call. Play 2: Multi-thread by discovery 3+ contacts across 2 functions by end of discovery. Single-thread wins at only 22%.
Tone: Direct
Email Draftv1Draft

Deal Save Sequences

Play 1: Surface pricing early Late pricing objections correlate with 78% loss rate. Present ballpark in the first call. Play 2: Re-engage after 14 days of silence Deals that go dark for 14+ days close at 9%. Trigger a case study drop on day 10.
Tone: Consultative

The problem

Win/loss analysis at most companies is a quarterly project. Someone on product marketing interviews five to ten buyers, writes a slide deck, and presents it to sales leadership. By the time the deck ships, the competitive landscape has shifted and half the findings are stale. The sample size is always too small. Five interviews cannot represent a pipeline of hundreds of deals. The buyers who agree to interviews skew toward people who liked you or hated you. The middle of the distribution, where most of the actionable signal lives, never gets captured. Meanwhile, every deal that closed or died left behind a trail of recorded calls, emails, and CRM updates. That data tells you exactly which talk tracks, objections, personas, and deal motions correlate with winning. Nobody has time to analyze it manually, so it sits untouched while sales teams keep running the same plays that lose.

How Amdahl solves it

Amdahl ingests every deal outcome from your CRM alongside the full conversation history from Gong, Fathom, Chorus, and Circleback. It analyzes won deals and lost deals separately, then compares them to find the patterns that actually matter. The output is not a slide deck. It is a continuously updated playbook. You see which factors correlate with the highest win rates, from exec sponsor involvement to multi-threading to how early pricing is introduced. You see which objections appear disproportionately in lost deals and which talk tracks show up in wins. Playbooks update automatically as new deals close. Sales reps get guidance based on hundreds of deals, not a handful of interviews. The analysis is always current because it runs on live data, not on a quarterly snapshot.

What we offer

  • Win rate breakdown by deal factor, persona, segment, and talk track

  • Auto-generated win playbooks with specific plays ranked by impact

  • Loss pattern reports showing which objections and deal motions correlate with losses

  • Deal save sequences triggered by early warning signals from conversation data

  • Competitive comparison showing win rates against specific competitors

  • Onboarding guides for new reps built from the patterns of top performers

Workflow

Step 01

Connect CRM and call data

Link Salesforce or HubSpot for deal outcomes, plus Gong, Fathom, Chorus, or Circleback for the conversations behind each deal. Amdahl joins outcomes to conversations automatically.

Step 02

Amdahl analyzes won vs lost deals

Every closed deal is compared against its full conversation trail. Amdahl extracts factors like exec sponsor presence, number of contacts engaged, pricing timing, and objection types, then correlates each with win/loss outcomes.

Step 03

Review patterns and win rates

See a ranked list of deal factors by win rate. Filter by segment, deal size, or competitor to find patterns specific to a market you care about.

Step 04

Generate and distribute playbooks

Amdahl produces ready-to-use playbooks with specific plays, ranked by impact. Export them to your sales enablement tool or share directly with reps. Playbooks update as new deals close.

Frequently asked

How many deals does Amdahl need to find meaningful patterns?
You start getting useful signal with as few as thirty closed deals, split roughly between wins and losses. The patterns get more reliable as volume increases. Most teams with six months of CRM history and call recordings have more than enough data. Amdahl will flag when a pattern is based on a small sample so you know which findings to treat as directional versus definitive.
How often do playbooks update?
Continuously. Every time a deal closes in your CRM and the associated calls are ingested, Amdahl reruns the analysis. You do not need to schedule a quarterly review or re-run a report manually. If a new competitor enters your market and starts appearing in lost deals, the playbook will surface that pattern within days of the first losses.
Can I filter by competitor or segment?
Yes. You can scope the analysis to deals involving a specific competitor, a deal size range, an industry vertical, or any CRM field you track. This lets you build segment-specific playbooks instead of one generic deck. A playbook for enterprise financial services deals will look different from one for mid-market SaaS, and Amdahl keeps them separate.
What factors does Amdahl track?
Amdahl extracts factors from both CRM data and conversation content. CRM factors include deal size, sales cycle length, number of stakeholders, and stage velocity. Conversation factors include exec sponsor mentions, multi-threading signals, pricing discussion timing, specific objection types, competitive mentions, and talk track usage. You can also define custom factors based on keywords or topics relevant to your sales motion.
How is this different from the reports my CRM already generates?
CRM reports show you what happened. They can tell you that deals above $100K close at a higher rate. But they cannot tell you why, because the why lives inside the conversations, not the structured fields. Amdahl joins the conversation data to the deal outcomes. Instead of knowing that deals stall, you know that they stall because pricing was introduced too late and the champion lost internal momentum. That is the difference between a dashboard and a playbook.

See this use case running on your own customer conversations.

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