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AI build

A full quote in the customer's home, and evenings back for the best salesperson

A UK double glazing company Home improvement, four self-employed salespeople 3 to 4 weeks to build, then a trial

At a glance

Their best salesperson capped himself at four appointments a day, because every quote meant hours of paperwork at night. Now he prices and produces the full proposal in the customer's home, his evenings are free, and he's taking 30% more appointments a week.

“This is so good, it's caught a number of measurement mistakes I've made, pricing errors, and I have all of my evenings free. Sales are up, time spent is down, and accuracy has improved too.”

Lead salesperson
+30%
appointments a week
+20%
sales
2 to 3 hours
of paperwork gone each evening

Figures for the lead salesperson, who is trialling the app before the rest of the team.

Sounds like you if

  • Your best people cap their own output because of the admin around each sale
  • Quotes or proposals get built by hand after the visit, often in the evening
  • A price at the table relies on quick sums and hoping nothing is missed
  • Details can go astray between the people who sell and the people who deliver

The challenge

The company's top salesperson brought in around £150,000 of revenue a month, but wouldn't take more than four appointments a day. Each one meant 2 to 3 hours that evening: writing up notes, filling in paperwork, working out fees and costs, then building it all into the company's proposal template before the customer saw a price.

The salespeople are self-employed and paid on commission, so that cap limited his earnings as well as the company's revenue and profit. A price at the table meant hurried sums and hoping nothing was missing, and quotes built by hand left room for measurement and pricing errors, and for details to go astray between sales, the fitters and the factory.

The brief

Take the evening paperwork away without changing how the team sells. In scope: the full product range and pricing, the sales process, every step needed to quote, and the handover to the fitters and the factory.

What we built together

  1. Mapped the offer

    Every product, price rule, fee and cost, and every step needed to produce a quote.

  2. Mapped the visit

    What a salesperson captures in the home, and what the proposal, the fitters and the factory each need from it.

  3. Built the app

    A web app for his iPad, or his laptop. Notes go in during the appointment, typed or photographed on paper: the camera reads handwritten notes and figures and pulls them in. The app works out the price and produces the full proposal in the company's own template, plus a job sheet with exact measurements for the fitters and another for the factory. It works offline in a customer's home and syncs when it's back online.

  4. Tested and refined it with the lead salesperson

    Scoped, built, tested and improved in 3 to 4 weeks, with the person who would use it every day.

What it took

  • 3 to 4 weeks from first conversation to a working, tested app.
  • Low risk by design: one salesperson trials it first, and the other three follow once it has proved itself. They're self-employed and paid on commission, so it has to earn its place.
  • Nothing changes for the customer except speed and accuracy: the proposal is still the company's own template.

What changed

  • His evenings are his own again: the 2 to 3 hours of paperwork after a day of appointments has gone.
  • An accurate price and proposal in the room, with comfort and confidence in the pricing on the spot.
  • No reason to cap appointments: up 30% a week. Sales are up 20%, with revenue and commission up in line.
  • Fewer mistakes: the app has caught measurement and pricing errors before they reached the customer, and the fitters and the factory get exact measurements.

What's next

Rolling the app out to the other three salespeople.

My role: mapped the offer, pricing and sales process, then designed and built the app.

Some details are simplified to protect client confidentiality. I'm happy to talk through the context in more detail.

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