Double glazing case study · Cloudy2Clear£639,350 pipeline from double glazing leads a national franchise had written off. 673 appointments booked.
How we turned thousands of ignored enquiries into booked appointments for a UK misted double glazing repair franchise, without ever touching their fresh leads.
At a glance
- Client
- Cloudy2Clear, a UK misted double glazing repair franchise
- Sector
- Double glazing (misted glass and window repairs), sold to homeowners
- Problem
- Quoted customers and unanswered enquiries sat in awaiting decision once the office team's follow-up ran out
- What we did
- AI SMS re-engagement of 6,769 old quotes and enquiries, with postcode routing to around nine franchise owners
- Result
- 4,321 replies (64%), 673 appointments, £639,350 pipeline, £107,000+ closed revenue
- Timeframe
- Pilot on months-old leads, now running every week
- Pricing basis
- Performance-based. The client puts the cost at around 10% of the sales value it brings in
Watch the client talk through the numbers.
Unedited. Recorded with permission.
“If you have a big awaiting decision list, there is probably a lot of money still sitting in it.”
Good quotes, sitting in awaiting decision.
“Before Setterly, our office team were following up customers after we had sent their quote, but it wasn’t always as consistent as it needed to be. When we were busy, some customers would only be called once or twice and some wouldn’t get called at all. They could then sit in awaiting decision without being followed up again.”
“We decided to try Setterly because we had a large number of customers who had already received a quote but hadn’t made a decision. I did hesitate at first because I wasn’t sure how customers would react to an AI message or whether it would annoy them.”
A low-risk pilot on their older leads first.
We started with a pilot on their older leads, data that was already a few months old, deliberately not touching their fresh leads. We never want to walk into a business and bolt an unproven system onto their live sales flow. Old leads sitting untouched in the CRM are the perfect low-risk test bed, and the learnings sharpen the system before it ever goes near fresher data.
We built an AI agent that knew everything about Cloudy2Clear. It reached out to every lead, whether they’d had a quote or never engaged in the first place, qualified them, and rebooked a call for the Cloudy2Clear team to attend. What we learned is that they really have two buckets of leads, and they need to be handled differently.
“No answer” leads
People who submitted a form the business could never get hold of. The AI reaches out, confirms it’s them, checks they’re still looking, then books a call for an in-person quotation.
“Quoted, not proceeded” leads
People who got a quote but didn’t go ahead. The AI reminds them of their exact quote value and asks if they’re happy to proceed at that figure. Only if they confirm does it book the call.
That extra step added friction on purpose. It booked fewer calls, but a far higher proportion of them converted. Every month we sit down with them for an ROI review: what they’ve paid us, how many calls we booked, and how much revenue they generated.
A postcode routing engine for 9 franchise owners.
Because Cloudy2Clear is a UK misted double glazing repair franchise split across owner territories, we built a custom postcode routing engine. Over 6,000 UK postcodes are owned by around nine different franchise owners. The client only sends us the customer’s postcode, so the system reads it, matches it to the correct franchise owner for that area, and then to the specific person who’ll be making the call.
When the AI hands the lead off, it tells the customer exactly who to expect a call from and from what number. It works every single time and runs completely on autopilot. That’s the difference between a generic bot and a system engineered around how a business actually operates.
673 appointments from leads they had already written off.
“What surprised me most was how many customers were still interested. They hadn’t necessarily rejected the quote, they had just been busy or hadn’t got around to making a decision. The AI allows the customer to arrange a call at a time that suits them, which gives us a much better chance of actually speaking to them.”
Pipeline = appointments booked x the client's average order value. And it keeps going. Every week the system contacts people, books new calls and brings in new work.
Around 10% of the sales value it brings in.
“The return is good, providing the sales are a decent value. We do have some jobs that are only our minimum charge of £120, so I wouldn’t say those give us a particularly good return. However, the higher value jobs are good value for money. Overall, each month the cost of Setterly is around 10% of the sales value it brings in.”
The difference is timing, persistence and a system built around them.
What the franchise did
- Generated great enquiries through strong marketing
- Followed up for up to 7 days
- Too busy fitting jobs to chase further
- Leads went cold and were forgotten
What Setterly did
- Reached out to every forgotten lead
- Qualified genuine intent and timeframe
- Confirmed the quote value before booking
- Routed each lead to the right franchise owner and named caller
- Ran 24/7 on autopilot
“Give it a try and look at the actual results.”
Setterly works for any business that generates 1,000+ enquiries a month, sells to homeowners, has a sales team, and can’t follow up on leads beyond 3 to 10 days. If that’s you, you’ve got the same problem Cloudy2Clear had, and we can fix it. If we don’t book you any qualified appointments, you pay nothing.
“I would say give it a try and look at the actual results. It won’t get every conversation perfect, but it gives you consistent follow up and stops good quotes from being forgotten. If you have a big awaiting decision list, there is probably a lot of money still sitting in it.”
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