Your Booking Rate May Not Mean What You Think It Means - DataTurk.ai - Done-For-You Business Analysis for Home Service Business Owners

This is Part 2 of a six-part series on using AI to make better business decisions. We’ll look at how to define your numbers, ask better questions, decide what deserves your attention, think about forecasts, and use AI without handing over your judgment.

Ask AI a simple question about your business: “What’s my booking rate?”

That should be easy. You have calls coming in, your CSRs book some of them, and the computer does a little division. A few seconds later, you have your answer.

Except there’s a problem. Before anyone can calculate your booking rate, someone has to decide what a booking rate actually is.

Do you divide booked calls by total calls? Do you divide them by the number of answered calls? Or should you divide booked calls only by calls from customers who had a real opportunity to book an appointment?

Those aren’t small differences.

Let’s Ask Suzie

Suzie runs your call center. She knows that not every phone call gives one of her CSRs a chance to book an appointment.

Mrs. Johnson called because she forgot what time the technician was coming. Mr. Davis called about a warranty issue. Three people called looking for jobs. One customer called twice about the same problem—a vendor called looking for someone in accounting.

Then there are the calls nobody answered. Should all of those count when you calculate Suzie’s booking rate?

Suppose your company received 1,000 calls last month and booked 600 appointments. If you count every incoming call, your booking rate is 60%.

But after removing calls that never represented a real chance to book an appointment, you discover that your CSRs actually handled 750 opportunities. Your booking rate is now 80%.

Nothing about the business changed. The same people made the same calls, and your CSRs booked the same 600 appointments.

Only the definition changed.

The Math Isn’t the Hard Part

This is where AI can create trouble for a business owner.

We’re being told that we can connect AI to our company data and start asking questions. Want to know how the call center is doing? Ask AI. Want to compare CSRs? Ask AI. Want to know whether your booking rate improved? Ask AI.

Great. But what definition is AI using?

If your phone company defines a call one way, your CRM defines it another way, and your call center manager uses a third definition, then AI doesn’t have a single version of the truth to work with. It has three different versions, and unless you’ve told it which one to use, you may not know which answer you’re getting.

The problem isn’t bad math. The problem is that nobody agreed on what should go into the math.

Before You Ask AI, Teach It Your Business

Every home service company has its own language, even when the owner doesn’t realize it.

What counts as a lead? What counts as an opportunity? When does an opportunity become a booking? Is a booked call the same as an appointment? When does an estimate become a sale? Do you count revenue when you sell the job, complete the job, or collect the money?

Ask those questions around your office, and you may discover that your people already have different answers.

AI won’t fix that disagreement for you. In fact, connecting AI to more systems can make the problem harder to spot because the answer may arrive quickly, neatly, and with enough confidence that nobody thinks to question it.

So before you start asking AI to tell you how your business is doing, write down the definitions behind the numbers you use to run the company.

Start with calls, opportunities, bookings, appointments, estimates, sold jobs, completed jobs, cancellations, marketing costs and revenue. Decide what each one means, what gets included, what gets left out, and which system provides the number.

Now when you ask, “What’s my booking rate?” you aren’t asking AI to make up the rules while it calculates the answer.

You’ve already given it the rules.

But the Right Numbers Can Still Lead You the Wrong Way

Getting the numbers right only solves the first problem. You can have clean data, clear definitions and accurate calculations and still make a bad decision because you asked the wrong question.

Suppose revenue falls and you ask AI why. It finds a real problem, gives you an accurate answer, and you get to work fixing it.

What happens if something else in the business deserves your attention more?

That’s the subject of “AI Can Give You a Good Answer to the Wrong Question.” We’ll look at why a correct answer can still send a business owner in the wrong direction, and how to figure out which problem deserves your attention first.