Pipeline performance breakdown: 62.0% win rate across 1,341 decided deals, with €3,967,265 in revenue won.
Pipeline performance breakdown: 62.0% win rate across 1,341 decided deals, with €3,967,265 in revenue won.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Sales leads, MSP owners, and account managers tracking pipeline health
How often: Weekly for pipeline reviews, monthly for forecasting, quarterly for strategy
Pipeline performance breakdown: 62.0% win rate across 1,341 decided deals, with €3,967,265 in revenue won.
High-level pipeline performance at a glance.
EVALUATE ADDCOLUMNS(SUMMARIZE('BI_Autotask_Opportunities','BI_Autotask_Opportunities'[status_name]), "Count", CALCULATE(COUNTROWS('BI_Autotask_Opportunities')), "Amount", CALCULATE(SUM('BI_Autotask_Opportunities'[amount]))) ORDER BY [Count] DESCEvery opportunity grouped by its current status, with total value per segment.
| Status | Count | Value | Share |
|---|---|---|---|
| Closed | 720 | $3,443,385 | 49.1% |
| Lost | 509 | $7,059,325 | 34.7% |
| Active | 124 | $3,938,803 | 8.5% |
| Implemented | 112 | $523,880 | 7.6% |
EVALUATE ADDCOLUMNS(SUMMARIZE('BI_Autotask_Opportunities','BI_Autotask_Opportunities'[status_name]), "Count", CALCULATE(COUNTROWS('BI_Autotask_Opportunities')), "Amount", CALCULATE(SUM('BI_Autotask_Opportunities'[amount]))) ORDER BY [Count] DESC509 lost opportunities, categorized by reason. The top factor: Testing purposes (34.3% of all losses).
| Loss Reason | Count | Lost Revenue | Share of Losses |
|---|---|---|---|
| Testing purposes | 122 | $1,594,213 | 34.3% |
| Duplicate - Another opportunity is already active | 67 | $461,683 | 18.8% |
| Don't need our product | 46 | $100,709 | 12.9% |
| Not a good time | 41 | $830,017 | 11.5% |
| Customer is not responding | 35 | $189,744 | 9.8% |
| They are going for a competitor | 23 | $2,557,849 | 6.5% |
| Too expensive | 14 | $407,468 | 3.9% |
| Not remotely interested | 5 | $6,445 | 1.4% |
| Product of service is missing features or capabilities | 3 | $19,717 | 0.8% |
EVALUATE TOPN(10, CALCULATETABLE(ADDCOLUMNS(SUMMARIZE('BI_Autotask_Opportunities','BI_Autotask_Opportunities'[loss_reason_name]), "Lost_Count", CALCULATE(COUNTROWS('BI_Autotask_Opportunities')), "Lost_Amount", CALCULATE(SUM('BI_Autotask_Opportunities'[amount]))), 'BI_Autotask_Opportunities'[status_name] = "Lost", NOT(ISBLANK('BI_Autotask_Opportunities'[loss_reason_name]))), [Lost_Count], DESC) ORDER BY [Lost_Count] DESC832 won opportunities, categorized by win reason. The strongest factor: Like our company.
| Win Reason | Count | Won Revenue | Share of Wins |
|---|---|---|---|
| Like our company | 130 | €1,386,676 | 67.4% |
| Love the product | 38 | €512,555 | 19.7% |
| Trust our reputation | 15 | €37,807 | 7.8% |
| Couple of features we really like | 8 | €17,613 | 4.1% |
| See the value | 2 | €5,577 | 1.0% |
Win/loss split per sales rep. Only reps with decided deals are shown.
| Sales Rep | Won | Lost | Win Rate | Won Value | Lost Value |
|---|---|---|---|---|---|
| James Mitchell | 175 | 94 | 65.1% | €727,313 | €128,048 |
| Sarah Thompson | 115 | 97 | 54.2% | €145,851 | €43,862 |
| Michael Rodriguez | 131 | 75 | 63.6% | €963,519 | €745,750 |
| Emily Chen | 120 | 27 | 81.6% | €383,221 | €57,536 |
| David Williams | 94 | 40 | 70.1% | €367,393 | €127,615 |
| Rachel Martinez | 18 | 56 | 24.3% | €41,563 | €247,840 |
| Kevin Anderson | 45 | 21 | 68.2% | €192,376 | €987,510 |
| Lisa Park | 53 | 8 | 86.9% | €254,893 | €238,573 |
| Robert Taylor | 11 | 41 | 21.2% | €385,497 | €3,612,043 |
| Amanda Foster | 22 | 16 | 57.9% | €137,513 | €362,048 |
| Christopher Lee | 22 | 6 | 78.6% | €239,363 | €103,922 |
| Jennifer Davis | 8 | 8 | 50.0% | €63,468 | €178,356 |
| Daniel Brown | 9 | 2 | 81.8% | €60,478 | €2,453 |
| Stephanie White | 4 | 5 | 44.4% | €4,160 | €20,552 |
The top 10 lost opportunities by deal value. These represent the biggest missed revenue.
| Deal | Amount | Loss Reason | Stage |
|---|---|---|---|
| Cloud Migration Project | €1,890,911 | They are going for a competitor | Qualification |
| Network Overhaul | €580,054 | Testing purposes | Qualification |
| Compliance Audit Package | €291,686 | Testing purposes | Qualification |
| Security Assessment | €255,143 | Too expensive | Qualification |
| Support Expansion | €218,823 | Not a good time | Qualification |
| Infrastructure Upgrade | €183,437 | They are going for a competitor | Qualification |
| Managed Services Proposal | €182,911 | They are going for a competitor | Qualification |
| Staff Training Program | €162,635 | Not specified | Qualification |
| Disaster Recovery Setup | €161,443 | Duplicate: Another opportunity active | Qualification |
| Endpoint Protection | €137,890 | Customer is not responding | Qualification |
The data points to a few clear patterns. At 62.0%, the overall win rate is solid but leaves room for improvement. The gap between won revenue (€3,967,265) and lost revenue (€7,059,324) is significant: you are losing almost twice the dollar value that you close.
Testing purposes accounts for 34.3% of all losses, though many of these may be data-hygiene artifacts rather than true competitive losses. The more actionable categories: "Duplicate: Another opportunity is already active" at 67 deals, and "They are going for a competitor" at 23 deals worth €2,557,849. That competitor bucket alone represents the single largest revenue loss.
On the positive side, "Like our company" is the leading driver behind successful closes (130 wins, €1,386,676 in revenue). The top-performing rep, Lisa Park, maintains a 86.9% win rate across 61 decided deals.
The largest single lost deal (€1,890,911) went to a competitor. Combined, competitor losses in the top 10 total €2,257,259. These are the deals where a targeted win-back strategy or earlier competitive positioning could have changed the outcome.
Based on the win/loss data, here are the highest-impact steps to improve your close rate.
122 opportunities are marked as lost for "Testing purposes," accounting for €1,594,212 in reported lost revenue. These skew your real win rate. Audit and re-classify them, or exclude test records from production reporting.
23 deals worth €2,557,849 were lost to competitors. For a fraction of that revenue, you could invest in competitive analysis, battle cards for sales reps, and early-stage competitor identification during qualification.
41 deals (€830,016) were lost because timing was wrong. Implement a structured nurture sequence for these prospects and set automated re-engagement triggers at 90 and 180 days.
Win rates range from 86.9% (top) down to below 25% for some reps. Pair lower-performing reps with Lisa Park for deal strategy sessions. Focus on qualification discipline and early objection handling.
Your top win reason is "Like our company" (130 wins). This signals that relationship-first selling works for your market. Make sure every proposal includes proof points that reinforce this strength.
Win rate equals won deals (Closed + Implemented) divided by all decided deals (won plus lost). Active and other open statuses are excluded because those deals have not reached a final outcome.
Any opportunity with status "Closed" or "Implemented" in Autotask PSA. Both statuses indicate a successful sale that has been finalized or delivered.
A small number of very large deals were lost (including one worth over 1.8 million). High-value losses pull the total up even though more deals were won by count. This highlights the importance of protecting large deals through the pipeline.
These are likely test or duplicate records that were closed out during system cleanup. They should be excluded or re-classified to get an accurate picture of real sales performance.
Monthly reviews work well. Win/loss patterns shift over time, and catching a trend early, such as a spike in losses to a specific competitor, gives you time to adjust before it grows.
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