What is the one-page playbook and what does it include?

A single one-page playbook that states the customer problem, the outcome metric, and the minimum viable proposal. It includes a 60-second qualification checklist (budget signal, timing window, decision-maker access) and a 3-signal template for mapping buyer moves.

How does the 60-second qualification checklist signal a week-ahead close?

Use the checklist to check budget, timing, and decision-maker access. If two signals are live and the decision maker is engaged, the rep should ask for a concrete next step within seven days.

What are the three signals in the 3-signal template?

Example signals are prior interactions (email or site visit), common objections (price or implementation), and the core value line (for example, reduce downtime by 30%). These signals move buyers through interest -> pilot -> ROI -> scale.

What is the Daily Runbook and how is it used?

The Daily Runbook is a daily ritual: load the one-page playbook before calls, run the 60-second qual, update close probability, start meetings with an outcome question, use a short-decision template, and log a short coaching note (pass or needs work). Maintain a library of A/B tested scripts.

How should teams debrief losses?

Complete an after-action within 48 hours with three fields: what happened, why, and what next. Use a psychological safety prompt (describe one root cause, not a person), make entries taggable and searchable, and review loss trends quarterly to prioritize experiments.

What are short-cycle protections and common triggers?

Run a rapid-risk snapshot at each touchpoint checking client need, feasibility, and timeline. Typical triggers: unclear client need (send clarifying one-page), unknown feasibility (schedule technical check), timeline slips (offer phased delivery or pilot), and compliance or cyber flags (pause commitments until mitigated). Run the snapshot in under 10 minutes.

Which KVIs should we track to decide what to scale?

Track a small set of KVIs: win rate after debrief, time to close, average time to resolve post-loss insight, and rapid-risk snapshot coverage. Report weekly, pilot on small cohorts, and require measurable lift before full roll-out.

How do decision analytics and usable AI fit into this playbook?

Use simple Bayesian updates or fixed-point scoring to adjust close probability as signals appear. Provide plain-language AI prompts for in-call probes, for example: "What would change this week to make a pilot useful?"

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