This article was developed by Guilherme Carmo, based on his experience setting up and evaluating solar lead supplier tests across UK solar campaigns. "Most installers test lead suppliers the wrong way," Guilherme explains. "They buy 20 leads, get two conversions, conclude the supplier is brilliant, and commit budget. Or they get zero conversions, conclude the supplier is terrible, and move on. Neither conclusion is statistically valid. Twenty leads doesn't tell you anything. A properly structured 30-day test does."
Testing a solar lead supplier is not complicated, but it requires discipline: a minimum viable sample size, clean tracking from day one, consistent follow-up across every lead in the test cohort, and a set of specific metrics measured at the right intervals. Without that structure, you end up with data that confirms your existing assumptions rather than data that tells you the truth about solar lead quality. This article gives you the exact framework Guilherme uses when evaluating any new solar lead source for ImperioLeads clients.
Why Most Solar Lead Supplier Tests Fail Before They Start
The most common solar lead test in the UK looks like this: an installer buys 30 to 50 leads from a new supplier, works them alongside their existing lead volume, tracks nothing separately, and forms a gut-feel opinion within two weeks. That opinion is then used to decide whether to continue with the supplier or not.
This approach fails for three predictable reasons. First, 30 to 50 solar leads is not a statistically meaningful sample for a product with a 6 to 14% conversion rate and a sales cycle of 9 to 22 days. You need at least 80 to 100 leads to draw valid conversion conclusions, and ideally 120 to 150 for reliable CPA data. Second, mixing new leads with existing volume makes it impossible to attribute performance correctly. If your existing leads are converting at 10% and the new supplier's leads are converting at 4%, blended tracking hides that gap entirely. Third, gut-feel assessments favour the most recent experience. Two good conversions in week three erase the memory of poor contact rates in week one.
"The installers who come to us having already tested three or four suppliers and found them all disappointing are almost always the ones who ran bad tests," Guilherme notes. "When we go back through their data, we usually find the suppliers weren't necessarily poor quality. The test conditions were. That's a very different problem with a very different solution."
The Pre-Test Setup: What You Must Have in Place Before Buying a Single Lead
Before purchasing any solar leads from a supplier under test, four things must be in place. Skipping any of them invalidates the test data before it starts.
A dedicated CRM tag or pipeline stage for test leads. Every lead from the supplier under test must be tagged or placed in a separate pipeline from your existing leads. If your CRM doesn't support this, create a separate spreadsheet that tracks only test leads. The non-negotiable requirement is that test lead performance can be measured in complete isolation from your other solar lead sources.
A committed follow-up protocol for the test period. The test must operate under your target follow-up conditions, not your average conditions. If the goal is to evaluate lead quality, your follow-up speed and consistency must be controlled as a constant. Call every test lead within 15 minutes of receipt. Execute a full 7-day multi-touch sequence on every lead that doesn't answer on the first attempt. If you can't commit to this for the test period, the data you generate will measure your follow-up process, not the supplier's lead quality.
A clear definition of conversion for this test. Decide in advance what counts as a conversion: lead to survey booked, lead to proposal sent, or lead to signed installation agreement. All three are valid depending on your sales cycle length, but you must choose one definition and apply it consistently across the entire test. Changing the definition mid-test is how results get rationalised rather than measured.
A written record of what the supplier has claimed. Before the test starts, document the supplier's stated benchmarks: promised conversion rate, expected contact rate, lead freshness (time from submission to delivery), and any quality guarantees. These claims become the benchmark against which you measure actual performance. Without written documentation, supplier claims are conveniently forgotten when results disappoint.
The 30-Day Test Framework: Week by Week
| Week | Focus | Metrics to Track | Decision Point |
|---|---|---|---|
| Week 1 | Data quality and contact rate | Valid phone numbers, contact rate on first attempt, lead delivery speed | If contact rate below 35%, flag supplier immediately |
| Week 2 | Qualified interest rate | % of contacted leads showing genuine interest, survey booking rate | If qualified interest below 20% of contacts, review lead source |
| Week 3 | Pipeline progression | Survey completion rate, proposal sent rate, early conversion signals | Compare survey booking rate against supplier benchmark |
| Week 4 | Conversion and CPA | Lead to close rate, cost per acquisition, revenue generated | Compare CPA against your break-even threshold |
| Days 31 to 45 | Lagging conversions | Late conversions from leads still in pipeline at day 30 | Final CPA and ROI calculation with full cohort data |
The 30-day window is a minimum, not a maximum. Solar sales cycles regularly extend beyond 30 days, particularly for battery-inclusive installations where the homeowner is evaluating a larger financial commitment. The cleanest way to run the test is to close the lead intake at day 30 and then track conversions from that cohort through to day 45 or even day 60 before drawing final conclusions. Leads that arrive in week 4 of a 30-day test haven't had time to complete a normal sales cycle by day 30. Excluding their conversion potential from your assessment understates true supplier performance.
The Five Metrics That Actually Measure Solar Lead Quality
Not all metrics are equally informative when evaluating a solar lead supplier. These five, tracked in sequence, give you a complete picture of whether the supplier is delivering genuine value.
1. Contact rate. The percentage of leads where you successfully reach the homeowner within three contact attempts across at least two channels (call and SMS). A healthy contact rate for exclusive fresh solar leads is 65 to 75%. Below 50% indicates stale data, invalid numbers, or non-homeowner submissions slipping through qualification. Contact rate is the first indicator of data quality and should be tracked from day one.
2. Qualified interest rate. Of the leads you successfully contact, what percentage express genuine interest in solar and agree to proceed to the next step (survey booking or further information)? A healthy qualified interest rate for exclusive solar leads is 55 to 70% of contacts. If you're contacting homeowners who have no recollection of submitting a solar inquiry, or who confirm they already purchased, qualified interest rate collapses and reveals either stale leads or misleading capture methods.
3. Survey booking rate. The percentage of all leads (not just contacts) that book a survey or home consultation. This metric integrates contact rate and qualified interest into a single actionable number. For exclusive solar leads, a survey booking rate of 25 to 40% of total leads received is a strong benchmark. Below 15% indicates a systemic problem with either lead quality or follow-up execution.
4. Survey-to-close rate. Of the surveys or consultations completed, what percentage result in a signed installation agreement? This metric is more influenced by your sales team than by lead quality, but significant variance from your baseline rate (measured on your existing lead sources) suggests the supplier's leads have different intent characteristics. If your normal survey-to-close rate is 55% and it drops to 30% on the test cohort, the leads attracted a less committed audience than your usual source.
5. Cost per acquisition. Total spend on the test cohort (lead spend plus sales time and survey costs) divided by number of signed installations. This is the only metric that determines whether the supplier relationship is commercially viable. Compare it against your break-even CPA threshold. If the supplier's CPA is below your threshold, the relationship works. If it's above, the leads are not converting efficiently enough to justify the cost regardless of any other positive signals.
What to Do When Results Are Mixed
Pure successes and clear failures are rare in solar lead supplier tests. Most tests produce mixed results: good contact rate but low qualified interest, or strong survey booking but weak close rate. Interpreting mixed results correctly determines whether you correctly identify a solvable problem or incorrectly abandon a viable supplier.
Good contact rate, low qualified interest rate. The leads are real people with valid numbers, but they don't remember inquiring about solar or are not genuinely interested. This points to misleading capture methods on the supplier's side, possibly a lead form that doesn't make the nature of the follow-up call clear, or leads generated from broad campaigns with low intent specificity. Raise this with the supplier and request information about the campaign creative and form used to generate the leads.
Good qualified interest, low survey completion. Homeowners express interest on the phone but don't follow through to the survey. This is usually a sales process problem rather than a lead quality problem. The lead is interested but not yet committed enough to book a home visit. Adjusting the sales conversation to reduce friction around the survey appointment (offer flexibility, explain what the survey involves, confirm it's free and non-obligatory) typically improves completion rate without changing the lead source.
Good survey completion, low close rate. Surveys are happening but quotes aren't converting. This is almost always a sales execution or pricing problem. The lead quality is sufficient to get the homeowner to engage, but something in the proposal or close process is failing. Review your survey-to-close rate on existing lead sources to confirm whether this is test-specific or a broader sales challenge.
"Mixed results are actually the most useful test outcome," Guilherme observes. "A clear success or failure doesn't tell you much about where the value is in your process. Mixed results tell you exactly which stage is underperforming and whether the fix is on the supplier's side or yours. That's actionable intelligence. That's what a good test is designed to produce."
The Supplier Conversation After the Test
Once you have 30 to 45 days of clean data, you have leverage in the supplier conversation that most installers never develop. Instead of a gut-feel negotiation, you have specific numbers: your contact rate against their promised rate, your CPA against your break-even threshold, and your survey booking rate against the benchmark they quoted. This data structure changes the conversation entirely.
If results are below benchmark, present the data and request one of three things: a partial refund on underperforming leads, an extension of the test period with improved lead quality, or a revised price that reflects the actual conversion economics rather than the promised ones. Reputable suppliers will engage with this conversation because they want long-term clients, not one-off test buyers. Suppliers who refuse to acknowledge the performance gap or become defensive when presented with clean data are telling you something important about how they'll handle quality issues at scale.
If results meet or exceed benchmarks, use the data to negotiate volume pricing. A supplier that has proven performance over 100 leads at controlled conditions will negotiate on per-lead price for a 3 to 6 month volume commitment, because they now have a client whose conversion data validates their product claims. That's a client worth retaining.
The 30-day test is not just a quality check. It's the foundation of a commercial negotiation. The data you produce has value beyond the test period if you use it correctly.
The Minimum Viable Test: If You Can Only Do One Thing
If the full framework above isn't immediately feasible, Guilherme's non-negotiable minimum is this: buy 100 solar leads from any new supplier, tag them separately in your CRM, call every single one within 15 minutes of receipt for the first two weeks, track contact rate and survey bookings daily, and calculate CPA at day 45. Everything else in the framework improves precision. This minimum produces data that is at least statistically meaningful enough to make an informed decision.
"One hundred leads, clean tracking, fast follow-up, 45 days," Guilherme summarises. "That's the minimum. Anything less and you're not testing the supplier. You're testing your ability to make decisions on insufficient data. Most bad supplier decisions in this industry come from exactly that."
Want help structuring a solar lead supplier test or benchmarking a current provider's performance? ImperioLeads works with UK solar installers to design test frameworks and evaluate lead source economics before significant budget is committed.
