Sales & Revenue Prompts to Understand Your Business Better

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 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 manager reviewing contact coverage in key accounts. Contact data: [PASTE: Account | Contact name | Title | Department | Our relationship (strong/neutral/cold/unknown) | Last contact date | Role in buying decision (champion/economic buyer/technical buyer/blocker/neutral)] For each account: 1. Decision-making team coverage — are all key roles identified and mapped? 2. Relationship gaps — economic buyer not engaged or relationship is cold 3. Single point of failure — all our relationships through one contact 4. Blocker identification — any contact actively opposing our solution 5. Recommended actions — who to add, who to warm up, who to use as a bridge to reach others Output: Contact map risk assessment by account. Priority contacts to engage. Suggested outreach approach for cold or unmapped contacts.

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.

You are a sales operations analyst reviewing CRM data quality. Data sample: [PASTE: Sample of 20–50 CRM records including: Deal/Account | Key required fields | Completion status for each] Required fields to audit: [LIST: Your required fields by object — e.g., Account: Industry, Size, Owner / Deal: Amount, Close date, Stage, Next step / Contact: Title, Email, Phone, Last activity] Analyze: 1. Completeness rate per field — % of records with this field populated 2. Worst fields — which required fields are most frequently blank? 3. Owner pattern — are certain reps consistently leaving fields blank? 4. Impact assessment — which blank fields most affect forecasting, reporting, or routing accuracy? 5. Enforcement options: required field validation / stage gate / manager review Output: Data completeness report. Completeness % by field. Reps with lowest completion rates. Top 3 enforcement recommendations.

You are a revenue operations manager reviewing forecast accuracy. Forecast vs. actual data (last 6 periods): [PASTE: Period | Forecast submitted (commit/best case) | Actual revenue | Variance $ | Variance % | Notes on large variances] Analyze: 1. Forecast accuracy % = 1 − |Variance| ÷ Actual; calculate for each period 2. Bias direction — are we consistently over-forecasting or under-forecasting? 3. Variance by source — are large misses coming from specific reps, regions, or deal types? 4. Best case conversion — what % of best case deals typically close? Is this predictable? 5. Improvement recommendations — process changes that would improve forecast accuracy Output: Forecast accuracy analysis. Bias assessment. Variance attribution. Recommended changes to forecasting process or methodology.

You are a sales operations manager reviewing territory design for equity and coverage. Territory data: [PASTE: Rep | Territory (region/vertical/named accounts) | Total addressable accounts | Current pipeline | Quota | Win rate | Months to hit quota | Any coverage gaps or overlaps] Review for: 1. Territory equity — are territories balanced by opportunity, not just by geography? 2. Coverage gaps — segments or geographies with no rep assigned 3. Overlaps — accounts or segments where multiple reps claim coverage; creates conflict 4. Quota alignment — is quota proportionate to territory opportunity? 5. Recommended adjustments — territory changes that would improve balance and reduce conflict Output: Territory design review. Equity assessment. Coverage gaps. Overlap conflicts. Recommended realignments with rationale.

You are a revenue operations manager reviewing the sales compensation plan. Plan data: [DESCRIBE: Current compensation structure (base/OTE/commission rate/accelerators/SPIFs), what behaviors the plan is designed to incentivize, any known plan gaming or unintended behaviors, attainment distribution last period] Review for: 1. Alignment with strategy — does the plan reward the behaviors the business needs right now? 2. Attainment distribution — what % of reps hit 100%? Ideal is 60–70%. Too high = quotas too low. Too low = plan is demotivating. 3. Accelerator effectiveness — do accelerators actually change rep behavior or do they just reward reps who were going to overperform anyway? 4. Gaming risk — are reps doing anything to maximize comp that doesn't serve the customer or business? 5. Simplicity — can a rep calculate their commission on any deal in under 2 minutes? Output: Comp plan review. Alignment assessment. Attainment distribution analysis. Gaming risks identified. Recommended adjustments.

You are a revenue operations manager reviewing lead routing rules. Routing data: [DESCRIBE: Current routing rules (by geography/company size/industry/product interest), who reviews routing logic, last update, known issues (leads falling through cracks/wrong rep/slow routing)] Review for: 1. Coverage completeness — are there combinations of attributes that result in a lead not being routed? 2. Routing accuracy — are leads landing with the right rep based on territory and ICP alignment? 3. Speed to route — how long does it take from lead creation to rep assignment? Target: under 5 minutes for high-priority leads 4. Round-robin fairness — if using round-robin, is distribution actually equal? 5. Exceptions and overrides — who can override routing? Is it audited? Output: Lead routing audit. Coverage gaps. Accuracy issues. Speed analysis. Recommended routing rule changes.

You are a revenue operations analyst reviewing the lead-to-opportunity conversion funnel. Funnel data: [PASTE: Period | Leads created | MQLs | SQLs | Opportunities created | Conversion rate at each stage | Average time between stages | Lost/disqualified at each stage] Analyze: 1. Conversion rates at each funnel stage — where is the biggest drop-off? 2. MQL quality — what % of MQLs become SQLs? Low rate = marketing/sales alignment issue 3. Time between stages — how long does it take for a lead to become an opportunity? Where are the delays? 4. Disqualification reasons — why are leads being disqualified? Top 3 reasons 5. Source quality — which lead sources convert at the highest and lowest rates? Output: Funnel conversion analysis. Bottleneck stage. MQL quality assessment. Source quality ranking. Recommendations to improve conversion.

You are a revenue operations manager reviewing the sales and marketing alignment on pipeline generation. Data: [PASTE: MQL volume by source | SQL conversion rate by source | Pipeline generated by marketing | Pipeline generated by sales | Marketing-sourced pipeline win rate | Sales-sourced pipeline win rate | Any SLA (lead response time / feedback on lead quality)] Review: 1. Pipeline contribution — what % of pipeline comes from marketing vs. sales-generated? Is this the right balance? 2. Lead quality debate — are sales reps rejecting marketing leads? Analyze the rejection rate and reasons 3. SLA compliance — is sales following up on marketing leads within the agreed time? 4. Win rate comparison — does marketing-sourced pipeline win at the same rate as sales-sourced? 5. Feedback loop — is there a formal mechanism for sales to give marketing feedback on lead quality? Output: Alignment review. Pipeline contribution analysis. SLA compliance. Quality assessment. Recommendations to improve collaboration.