Sales & Revenue Prompts to Make Better Decisions

You are a sales manager reviewing the current pipeline for forecast accuracy and deal quality. Pipeline data: [PASTE: Deal name | Account | Stage | Amount | Close date | Days in current stage | Last activity date | Owner] Analyze: 1. Stage distribution — are deals spread across stages or bottlenecked in one stage? 2. Stalled deals — any deal with no activity in >14 days or stuck in same stage >30 days; flag with days stalled 3. Close date realism — deals with close dates in the next 30 days; do stage and activity level support that timeline? 4. Pipeline coverage — total pipeline value ÷ quota; flag if below 3x coverage 5. At-risk deals — deals where close date has passed or activity has gone cold; recommend: pursue / reprice / close lost Output: Pipeline health report. Stalled deal list with recommended actions. Coverage ratio. Top 5 deals requiring manager attention this week.

You are a revenue operations analyst scoring deals in the pipeline for forecast inclusion. Deal data: [PASTE: Deal name | Stage | Amount | Close date | Champion identified? (yes/no) | Economic buyer engaged? (yes/no) | Compelling event? (yes/no) | Competitive situation | Last meaningful activity | Mutual action plan in place? (yes/no)] Score each deal on: 1. Engagement quality — are the right stakeholders involved and active? 2. Timeline justification — is there a real reason the customer needs to decide by the stated close date? 3. Competitive risk — is there an active competitor involved? What is our differentiation? 4. Process alignment — is there a mutual action plan or are we just waiting? 5. Overall forecast category: Commit (high confidence) / Best case (likely but not certain) / Pipeline (early stage) / At risk (stalled or at-risk) Output: Deal scoring table. Forecast category for each deal. Deals reclassified from Commit to At risk with reason. Total commit, best case, and pipeline values.

You are a sales operations analyst reviewing deal progression rates. Pipeline data (last 6 months): [PASTE: Deal name | Start stage | End stage | Amount | Time in each stage (days) | Won/Lost/Open] Analyze: 1. Stage conversion rates — % of deals advancing from each stage to the next 2. Average time in each stage — where do deals slow down? 3. Drop-off stage — which stage has the highest deal loss rate? 4. Win rate by deal size — do larger deals win at the same rate as smaller ones? 5. Velocity — average days from first stage to close for won deals Output: Pipeline funnel analysis. Conversion rates by stage. Average time per stage. Drop-off analysis. Recommendations to improve conversion at the weakest stage.

You are a revenue operations manager identifying pipeline generation gaps. Data: [PASTE: Rep | Quota | Current pipeline | Pipeline coverage ratio | New pipeline added this month | Average deal size | Win rate % | Sales cycle length (days)] For each rep: 1. Required pipeline = Quota ÷ Win rate — how much pipeline is needed to hit quota? 2. Coverage gap — current pipeline vs. required; gap in $ 3. Pipeline generation rate — new pipeline added this month; is it sufficient to maintain required coverage? 4. Burn rate — pipeline being closed (won + lost) faster than it's being added? 5. Recommendation: pipeline generation coaching / deal quality review / quota adjustment discussion Output: Pipeline gap analysis by rep. Total team pipeline vs. required. Reps requiring pipeline generation coaching vs. those with pipeline but low conversion. Action plan.

You are a sales manager reviewing deal risk related to stakeholder engagement. Deal data: [PASTE: Deal name | Account | Amount | Stakeholders engaged (name and title) | Last contact date per stakeholder | Champion strength (strong/neutral/weak) | Economic buyer status (engaged/not engaged/unknown)] For each deal: 1. Single-threaded risk — deals where only one contact is engaged; if that person leaves or goes cold, deal is at risk 2. Economic buyer gap — deals where the economic buyer is not engaged; these rarely close 3. Champion strength — weak champion = deal is at risk even if economic buyer is engaged 4. Stakeholder map completeness — are all key buying roles identified (technical buyer / champion / economic buyer / end users)? 5. Recommended action per deal: expand contacts / re-engage cold stakeholder / escalate executive sponsor Output: Deal stakeholder risk assessment. Single-threaded deals highlighted. Economic buyer gap list. Actions to reduce deal risk through better multi-threading.

You are a sales director reviewing late-stage deals for forecast risk. Late-stage pipeline: [PASTE: Deal | Stage | Amount | Close date | Days in current stage | Last customer activity | Outstanding legal/procurement steps | Any known competitors] For each deal: 1. Procurement/legal risk — is there a contract, legal, or procurement process that could delay close? 2. Budget risk — has budget been confirmed and approved? Or is it verbal only? 3. Timing risk — does the customer have a real deadline or is the close date wishful thinking? 4. Competitive risk — is a competitor still actively engaged at this stage? 5. Overall risk classification: low / medium / high — and the single most important action to derisk Output: Late-stage deal risk register. High-risk deals with specific derisking actions and owner. Forecast adjustment recommendations.

You are a sales operations analyst tracking deal velocity trends. Data (last 12 months of closed deals): [PASTE: Deal | Won/Lost | Amount | Stage 1 entry date | Close date | Total days to close | Number of activities | Number of stakeholders engaged] Analyze: 1. Average sales cycle length by deal size tier (small/mid/large) 2. Velocity trend — are deals closing faster or slower than 6 months ago? 3. Activity correlation — do deals with more activities close faster or slower? 4. Won vs. lost velocity — do lost deals drag on longer than won deals? 5. Fastest-closing deals — what do our fastest-closing won deals have in common? Output: Deal velocity analysis. Cycle time by deal size. Won vs. lost comparison. Top 3 factors that correlate with faster close. Recommendations.

You are a sales manager preparing a competitive intelligence brief for a deal. Deal context: [DESCRIBE: Customer, deal size, stage, our solution being proposed, known competitors in the deal, any competitive information gathered from the customer] Build the competitive brief: 1. Competitor overview — strengths and weaknesses relevant to this specific customer's needs 2. Where they will attack us — likely objections or FUD the competitor will raise about our solution 3. Where we win — our genuine differentiated strengths for this customer's use case 4. Traps to set — questions to ask the customer that highlight competitor weaknesses without naming the competitor 5. Landmines to defuse — customer concerns about our solution that need to be addressed proactively Output: Competitive deal brief. Talking points for next customer conversation. Questions to ask. Objections to prepare for.

You are a revenue operations manager reviewing a non-standard deal request from a sales rep. Deal request data: [PASTE: Deal name | Customer | Standard pricing | Requested discount % | Justification provided | Deal size | Strategic importance | Competitive pressure claimed | Rep's win probability with/without discount] Evaluate: 1. Discount justification — is the competitive or strategic reason compelling? 2. Precedent risk — does approving this discount set a precedent with this customer or in this segment? 3. Margin impact — deal value at requested discount vs. standard; gross margin impact 4. Alternative options — could we offer non-price concessions (extended terms, additional services, phased payment) instead? 5. Recommendation: approve / approve with conditions / counter-offer / decline Output: Deal desk decision with rationale. Any conditions attached to approval. Counter-proposal if not approving as requested.

You are a customer success manager scoring account health. Account data: [PASTE: Account | ARR | Product(s) used | Login/usage frequency | Support tickets (last 90 days) | NPS score | Last exec engagement date | Contract renewal date | Expansion opportunities identified] Score each account across: 1. Product adoption — usage frequency vs. expected for their contract tier 2. Support health — ticket volume and severity trend; escalations? 3. Relationship depth — exec sponsor engaged, multiple contacts, or single-threaded? 4. Financial health — any payment delays, downgrade requests, or usage below contracted minimums? 5. Overall health: Green (healthy/growing) / Yellow (at risk indicators) / Red (churn risk) Output: Account health dashboard. Red and Yellow accounts requiring immediate action. Recommended intervention per at-risk account. Renewal risk exposure ($).

You are an account manager reviewing your book of business for expansion opportunities. Account data: [PASTE: Account | Current products | ARR | Employees | Industry | Products they don't have yet | Last upsell discussion date | Any signals of new needs (new hires/new projects/usage spikes)] For each account: 1. Whitespace — which products or modules do they not have that they would logically benefit from? 2. Usage signals — are they using current product heavily enough to justify expansion? 3. Growth signal — headcount growth, new office, acquisition, or new initiative that creates new need? 4. Relationship access — do we have the relationships needed to have an expansion conversation? 5. Recommended next action: expansion conversation now / build relationship first / not ready yet Output: Expansion opportunity list ranked by likelihood × value. Top 5 accounts for immediate expansion outreach. Recommended approach for each.

You are a sales operations manager reviewing account segmentation for resource allocation. Account data: [PASTE: Account | Industry | ARR or ACV | Employee count | Product(s) | Region | Growth rate | NPS score | Support cost] Segment accounts by strategic value: 1. Tier 1 (Strategic) — highest ARR, highest growth potential, strongest reference value; deserve dedicated resources 2. Tier 2 (Growth) — mid-size, good expansion potential; covered by account management team 3. Tier 3 (Long tail) — small or flat, primarily retention focus; covered through digital or pooled CSM 4. Flag misalignments — accounts receiving Tier 1 resources but should be Tier 2/3; vice versa 5. Investment recommendations — where to add resources and where to reduce Output: Account segmentation table. Tier assignment for each account. Misalignment flag list. Resource reallocation recommendation.

You are a customer success manager preparing for upcoming renewals. Renewal data: [PASTE: Account | ARR | Renewal date | Health score | Last QBR date | Champion strength | Economic buyer relationship | Any open issues | Usage trend (up/flat/down)] For each renewal in the next 90 days: 1. Risk classification: low / medium / high based on health signals 2. Key risk factors — what specifically could cause churn or downgrade? 3. Required actions before renewal conversation — fix issues, re-engage stakeholders, demonstrate value 4. Expansion opportunity — is there a natural expansion conversation to have alongside renewal? 5. Internal escalation — any renewal requiring VP or executive involvement? Output: Renewal pipeline by risk level. At-risk renewals with specific action plan and owner. Total ARR at risk. Expansion opportunities to bring into renewal conversations.

You are an account manager responding to a key stakeholder change at a customer account. Situation data: [DESCRIBE: Account, ARR, which stakeholder left or changed roles (title, their relationship with us — champion/economic buyer/neutral), new person in role (if known), any risk this creates, relationship with the remaining contacts] Build the response plan: 1. Immediate risk assessment — how dependent was our relationship on this individual? 2. Transition outreach — draft a message to the departing contact (if appropriate) and to the new person 3. Internal contact mobilization — who in our organization has a relationship that could facilitate introduction to the new stakeholder? 4. Account mapping update — who now owns the champion or EB relationship? Need to rebuild? 5. Timeline — how long can we operate without the new relationship established before renewal or expansion risk increases? Output: Stakeholder change response plan. Draft outreach to new contact. Timeline for relationship rebuild. Risk level: low/medium/high.

You are an account executive building a 12-month account plan for a strategic account. Account data: [PASTE: Account | Current ARR | Products in use | Key stakeholders and their priorities | Whitespace opportunities | Known competitor presence | Customer's stated strategic priorities for the year | Relationship strength by stakeholder] Build the account plan: 1. Account objectives — what do we want to achieve in this account in the next 12 months? (revenue target / relationships to build / products to expand) 2. Customer objectives — what does the customer want to achieve? How does our solution support that? 3. Opportunity map — specific expansion opportunities with estimated value and timeline 4. Relationship plan — who do we need to know better? Who should we involve from our executive team? 5. Risks — what could prevent us from achieving the plan? Output: 12-month account plan. Revenue target. Quarterly milestones. Relationship map. Top 3 risks and mitigation.

You are a VP of Sales building an executive sponsor program for top accounts. Account data: [PASTE: Account | ARR | Our assigned exec sponsor | Customer exec sponsor | Last exec-to-exec interaction date | Key account risk or opportunity that warrants exec involvement] For each strategic account: 1. Sponsor alignment — is our exec sponsor the right level and function match for the customer exec? 2. Engagement frequency — how often should exec sponsors interact for this account tier? 3. Agenda for next interaction — what business topic or relationship goal should drive the next touchpoint? 4. Value exchange — what can our exec offer that the customer exec would genuinely value? 5. Relationship health — strong / developing / hasn't started Output: Executive sponsor engagement plan. Contact schedule. Agenda recommendations for next touchpoint per account. Gaps requiring immediate action.

You are a sales operations analyst reviewing account characteristics that predict wins vs. losses. Closed deal data (last 12 months): [PASTE: Account | Industry | Company size | Deal size | Won/Lost | Sales cycle length | Stakeholders engaged | Product sold | Region | Competitive situation] Analyze: 1. Win rate by industry — which industries do we win in most consistently? 2. Win rate by company size — are we better at SMB, mid-market, or enterprise? 3. Win rate by deal size — does our win rate change as deal size increases? 4. Competitive win rates — against which competitors do we win most and least often? 5. Ideal customer profile signals — what combination of characteristics predicts a win? Output: Win/loss pattern analysis. Ideal customer profile refinement based on data. Segments to prioritize. Segments to qualify out of more aggressively.

You are a revenue operations manager auditing the sales process for consistency and effectiveness. Process data: [DESCRIBE: Your current sales stages and definitions, entry/exit criteria for each stage, required CRM fields per stage, last time process was reviewed, any known rep behaviors that deviate from the defined process] Audit for: 1. Stage definition clarity — are exit criteria specific enough that two different reps would stage the same deal the same way? 2. CRM field compliance — are required fields being completed at each stage? Sample 10–20 deals. 3. Stage skip behavior — are reps jumping stages? Does skipping correlate with lower win rates? 4. Activity requirements — are key activities (discovery call / demo / proposal) consistently happening in the right stages? 5. Manager inspection — are managers reviewing deals at each stage or only at late stages? Output: Sales process audit findings. Compliance rates. Stage definition gaps requiring clarification. Recommended process updates. Training implications.