Back to the on-screen lesson ·
A leading measure counts something that happens before a goal's result and can still be changed; with a conversion rate from the records it predicts the result, sets a weekly target, and warns while there is time to act.
Paper packet. Every task here also exists on screen, where it is checked automatically; answers written on paper are not assessed by Nydus. When you are back at a device, enter your answers there.
You will sort measures into leading and lagging, choose a leading measure for a goal, predict a result through a chain of conversion rates, set a weekly target for the leading measure, and read the first week against it.
A measurable goal has a result to check on its date. By then, though, it is too late to change anything. This lesson adds a measure that gives warning while there is still time, using the funnel and its conversion rates from the customers course.
| Term | What it means |
|---|---|
| Lagging measure | A count of the result — bookings, profit, accounts won — after it has happened. |
| Leading measure | A count of something done or seen earlier that moves before the result and can still be changed. |
| Conversion rate | The share of the leading activity that becomes the result, from the records. |
| Weekly target | The leading activity needed each week for the goal, found by running the goal back through the rate. |
| Projection | The result the current pace would give if it and the rate held. |
Bright Home Cleaning's goal is more move-out cleans. The cleans done this month are the result — a lagging measure. By the time they are counted, the month is over. What comes before a move-out clean? A card handed to a property manager, then a tenant's inquiry, then a walk-through visit, then a booking. Each of those is a leading measure: it happens earlier, and the owner can act on it this week.
The best leading measure is close enough to the result to predict it, and early enough to act on. Walk-through visits fit: from the records, 40 percent of visits become a booked clean. Twenty visits booked this month predict about eight cleans.
$$\text{predicted result} = \text{leading measure} \times \text{conversion rate}$$
Run backward, the same rate says how much leading activity a goal needs: 8 extra cleans at 40 percent needs 20 extra visits, five a week.
Another way: table
Leading and lagging measures.
| Goal | Leading measure | Lagging result |
|---|---|---|
| More move-out cleans | Walk-through visits booked | Cleans done |
| More café accounts | Sample cups delivered | Accounts won |
| More repairs | Estimates sent | Repairs booked |
Another way: steps
Write the chain. List, in order, everything that happens between the business's first action and the result: menu delivered, inquiry, quote, order. Most businesses find four or five steps.
Pick one step to watch. Early steps give more warning but predict less, because many things can happen after them; late steps predict well but give little warning. The step just before the decision — quotes sent, visits booked, samples delivered — is usually the right compromise.
Take the rate from the records. Count, over the last few months, how many of that step became the result. A rate from twenty cases is rough; from a hundred it is a working figure.
Set the weekly target and read against it. The goal divided by the rate gives the month's leading activity; divided by four, the week's. Each week, set the actual figure beside it.
Check the prediction at the month's end: predicted results against actual. If they match within a little, the rate still holds. If the actual keeps falling below the prediction, the rate has changed and something between the leading step and the result needs looking at.
A good leading measure has three properties. It comes before the result, far enough ahead to act on. It is in the business's control — the number of cafés in the country moves nothing Maya does, but the sample cups she delivers do. And it has a known conversion rate from the records, so it predicts the result rather than just feeling encouraging.
Busy-looking numbers often fail the third test. Social-media followers, website visits and flyers printed can rise for months without a single extra booking, because nobody has measured how many of them become customers. A leading measure earns its place by predicting.
The value of a leading measure is the warning it gives in week one. If Neighborhood Kitchen needs 15 quotes a week for its catering goal and sends 9 in the first week, that pace would give about 36 quotes and 18 orders over the month, against a goal of 30. The owner knows on Friday of week one that the goal is slipping by about 12 orders, and that 6 more quotes a week would close the gap.
The response is aimed at the step that feeds the leading measure: more menus delivered to office managers, a follow-up call to last month's inquiries, a faster reply to new ones. It is never to wait for the month's result and hope.
The conversion rate is a figure from the past, and it can change. If visits rise but the share that book falls, the leading measure looks healthy while the result stalls. Watch the rate beside the measure: the month's results divided by the month's leading activity. A falling rate points at the steps between — the quote, the price, the follow-up — and is often the cheaper thing to fix, because improving a rate from 40 to 50 percent gives a quarter more results with no extra activity at all.
The same idea works for any goal. For Northside Repairs' on-time goal, the result is bikes ready on the promised day; a leading measure is parts ordered the day the bike comes in, because late parts cause most misses. For a goal about customers coming back, the result is repeat visits; a leading measure is follow-up messages sent a month after the first visit. In each case the question is the same: what has to happen, earlier, for the result to happen, and can the owner count it?
A leading measure only helps if it is counted the same way every week and put where the owner sees it. The simplest tool is a single sheet, or a whiteboard in the back room, with one row per week and three columns: the weekly target, the actual count, and the difference. Neighborhood Kitchen's sheet has quotes sent; Bright Home Cleaning's has walk-through visits booked; Maya's has sample cups delivered. Filling in the row takes a minute on Friday afternoon, and it is the minute that makes the rest of this lesson work.
The count needs a rule, written down once, for what counts. Is a quote that was sent twice one quote or two? Does a walk-through booked for next month count this week or next month? Is a sample cup left with a café that was already a customer a sample? Without a rule the count drifts, and a week that looks strong may only be a week in which the counting was generous.
Counting works best when it is part of doing the work, not a separate chore. A quote that is sent from a template can be numbered as it goes out; a visit booked in the calendar can be marked with a letter; samples can be logged on the delivery sheet. The owner then reads the count from what already exists instead of trying to remember on Friday what happened on Tuesday.
Finally, one leading measure per goal is usually enough. A business with two goals watches two numbers each week. Adding more — followers, clicks, flyers, calls — makes the sheet look busy and the decision less clear, and the numbers that do not predict anything crowd out the one that does. If a second measure seems necessary, it is usually because the chain has two weak links, and the better answer is to fix the weaker one first and watch the measure just after it.
A landscaping business set a goal of 24 new garden-build contracts between March and May, eight a month. By the end of March it had signed four, and the owner was ready to blame the weather. His partner, who kept the books, had been counting something else: site visits, where one of them walked a customer's garden and took measurements. Over the previous two springs, one site visit in three had become a contract.
Eight contracts a month needed 24 site visits, about six a week. In March they had done eleven — the four contracts were almost exactly what eleven visits at one in three predicted. The problem was not the weather or the closing; it was that too few visits were being booked, because inquiries were answered only in the evenings and half of them had gone to a rival by then.
They set a weekly target of six site visits, answered inquiries within two hours using a shared phone, and read the visit count every Friday. April brought 23 visits and seven contracts; May brought 25 visits and nine. They finished the season at 20 contracts, short of 24 but far ahead of the pace March had set, and entered the next spring with the visit target already written into the plan.
Sales teams in larger firms track a pipeline — the leads, meetings and proposals that come before a sale — for exactly the reason in this lesson: the pipeline this month predicts the sales next month. A small business can keep the same kind of count on one page.
The result is the best thing to watch. It arrives too late to change.
Any early number is a leading measure. It must be in the business's control and linked to the result by a known rate.
More activity always means more results. Only at a steady conversion rate.
A prediction is a promise. It is an estimate from past rates, to be checked.
A slow first week will even out. At the same pace it will not; act on it.
Busy numbers are leading measures. Followers, clicks and flyers printed feel like progress but predict nothing until their conversion into customers has been counted from the records.
The rate never changes. It is a figure from the past; recount it every month, because a falling rate hides behind a healthy-looking leading measure.
A leading measure replaces the goal. It warns about the goal; the result is still what the goal is measured by.
One week's count settles it. A single week can be unusual; read the leading measure against its target over several weeks before changing course.
Write the chain.
$\text{sample} \to \text{tasting} \to \text{account}$
What happens before an account.
Pick the step to watch.
$\text{samples delivered}$
In her control, weeks ahead.
Take the rate from her records.
$1 \text{ in } 4$
Samples that became accounts last year.
Predict from this month's samples.
$12 \times \frac{1}{4} = 3$
Three accounts expected.
Run a goal of six back through the rate.
$6 \div \frac{1}{4} = 24 \text{ samples}$
Six a week.
Read the menus delivered.
$200$
To office managers this month.
Apply the inquiry rate.
$200 \times 0.3 = 60$
Inquiries.
Apply the quote rate.
$60 \times 0.8 = 48$
Quotes sent.
Apply the booking rate.
$48 \times 0.5 = 24$
Orders predicted.
Compare with the goal.
$30 - 24 = 6 \text{ short}$
Known before the month ends.
Find the extra menus needed.
$6 \div (0.3 \times 0.8 \times 0.5) = 50$
Fifty more menus this month.
Read the goal.
$32 \text{ move-outs this month}$
The result.
Run it back through the rate.
$32 \div 0.4 = 80 \text{ visits}$
Four in ten visits become cleans.
Find the weekly target.
$80 \div 4 = 20$
Visits each week.
Read the first week.
$14 \text{ visits}$
Six short.
Project the month at that pace.
$14 \times 4 \times 0.4 = 22.4$
About 22 cleans.
Find the shortfall.
$32 - 22.4 \approx 10$
Cleans the goal would miss.
Act on the channel.
$\text{call the three quiet property managers}$
Before week two.
Read the rate from the records.
$30 \text{ percent of estimates are booked}$
The conversion rate.
Run a goal of 12 repairs back through it.
$12 \div 0.3 = 40 \text{ estimates}$
Ten a week.
Read a first week of 7 estimates.
Bright Home Cleaning's goal is more move-out cleans. Sort each measure.
| Leading | Lagging | |
|---|---|---|
| Cards handed to property managers this week | ||
| Move-out cleans done this month | ||
| Walk-through visits booked | ||
| This month's profit |
Complete the worked solution: Neighborhood Kitchen delivers $50$ menus to office managers in a month. From its records, $20$ percent of menus bring an inquiry, $60$ percent of inquiries get a quote, and $50$ percent of quotes are booked. Predict the month's orders.
Multiply the menus by the inquiry rate.
$50 \times 20 \div 100 =$ i
Inquiries from the menus.
Multiply the inquiries by the quote rate.
$(\text{inquiries}) \times 60 \div 100 =$ q
Quotes sent.
Multiply the quotes by the booking rate.
$(\text{quotes}) \times 50 \div 100 =$ o
Predicted orders.
Choose which figure to watch weekly.
$\text{quotes sent}$
Close to the result, early enough to act on.
Treat the prediction as a guide.
$\text{check it against the month's orders}$
The rates come from the past.
Maya's goal is $11$ café accounts by December. Which is the best leading measure to watch each week?
Put Neighborhood Kitchen's catering chain in the order it happens, earliest first.
Number the steps in order (write the number in the box):
Northside Repairs sends $25$ written estimates a month. From its records, $30$ percent of estimates become booked repairs. How many booked repairs does this month's estimate count predict?
Answer:
Neighborhood Kitchen wants $24$ catering orders this month. One quote in $3$ becomes an order. In the first week it sent $12$ quotes. Fill in the reading, assuming four weeks in the month.
| Amount | |
|---|---|
| Quotes needed each week | |
| Orders projected at this week's pace | |
| Shortfall against the goal | |
| Extra quotes needed each week |
A gym hands out free trial passes. This month it gave out $400$; from its records $50$ percent of passes are used, and $20$ percent of those who use one join. Its goal next month is $20$ new members. Fill in the figures.
| Amount | |
|---|---|
| Passes used this month | |
| New members predicted | |
| Passes needed next month for the goal |
Lesson test: one question per skill, one attempt each, no hints. Your answers are checked when you submit.
Bright Home Cleaning wants $10$ extra move-out cleans a month. From its records, $40$ percent of walk-through visits turn into a booked clean. Complete the sentence.
About w extra walk-through visits a month are needed.
You can pick the number to watch weekly that shows whether a goal is on track. Tell someone why last month's profit is a poor thing to steer by. Next: a forecast that states its assumptions.
17. Your turn: Northside Repairs' estimates, step 3
$3 \text{ short of the weekly target}$
Act on it now.