Configure, Price, Quote by the Numbers
Chapter 1 of 8 · 10 min read
Measure the quoting you have
Before changing how quotes are made, measure their volume, effort, turnaround, accuracy, win rate and discounts, so you know where time and margin are really lost.
Every manufacturer that quotes configurable products believes it knows what is wrong with its quoting. The estimators are too slow, or the prices are inconsistent, or the sales team discounts too freely, or customers go elsewhere while waiting. Usually some of those beliefs are right and some are wrong, and the expensive mistakes come from fixing the wrong one. A configurator that produces quotes in minutes does nothing for a business whose real leak is discounting, and a discount policy does nothing for one that loses half its enquiries to slow replies.
This chapter measures the bench fabricator’s quoting before anything is changed. It counts the quotes, times the work, follows each quote to its outcome, checks prices against cost and finds where margin goes. The result is a baseline that says which problem is worth solving first and lets the firm prove later whether the solution worked. The failure case is a configurator purchase that nearly went ahead on a vendor’s promise, with nothing to measure it against.
What to measure
Five measures cover most of what matters in quoting:
- Volume: how many quotes are produced, by product and by customer type.
- Effort: how long each quote takes, and who does the work.
- Turnaround: the time from enquiry to quote in the customer’s hands.
- Accuracy: how often the price, the configuration or the lead time turns out to be wrong.
- Outcome: whether the quote was won, lost or never answered, and at what final price after any discount.
Most firms already hold the raw data in their email, their quoting spreadsheet and their accounts system. The work is in joining it together for a period long enough to be representative. The bench fabricator used eight weeks.
Volume and effort
This is an illustration. Over eight weeks, the firm issued 240 bench quotes, about 120 a month. Its two estimators timed themselves on 40 of them with a simple log. A quote took about 45 minutes of estimator time on average: about 10 minutes reading the enquiry and clarifying it, 15 minutes finding a similar past quote and adjusting it, 10 minutes checking costs for anything unusual, and 10 minutes typing and sending the quote.
At 120 quotes a month, that is about 90 hours a month of estimator time. At an internal cost of about A$110 an hour for a senior estimator, it is about A$9,900 a month.
Who really does the work
The 45 minutes were not all the estimators’. About 15% of quotes, mostly those with sinks or unusual lengths, needed a question to the production engineer: can we fold that, will that sink fit, how long would it take? Each question took the engineer about 10 minutes, often in the middle of other work. Interruptions like that cost more than their minutes, because they break concentration on design work. They also show where the product knowledge lives. If the estimators need the engineer to confirm what can be built, then the rules of what can be built are not written down anywhere the estimators can use. Chapter 2 writes them down.
The firm also found that the estimators spent a noticeable share of their time on quotes that were never going to be won: tenders where the customer was collecting a third price to satisfy a procurement rule, and enquiries from students and hobbyists. Neither group was large, but both took full effort. A short qualifying question at enquiry, about the project, its timing and who decides, let the estimators give those enquiries a quick indicative price instead of a full quote.
Turnaround and outcome
Time matters to customers, and the firm’s records showed by how much. For each quote, an assistant recorded the date of the enquiry, the date the quote was sent and the outcome. The average turnaround was about 2.5 working days. Some quotes went the same day, when the estimator had a quiet afternoon. Some took more than a week, when the estimators were busy with a large project.
Quotes sent the same day were won about 52% of the time, and quotes sent after three or more days about 31%. The overall win rate was about 40%. The firm was careful not to read too much into this, because the quick quotes were often simpler jobs for existing customers. But the trend held when it looked only at existing customers, and its sales team confirmed that customers in food factories often order from whoever answers first when a line is being installed.
Follow every quote to an outcome
A quote that is neither won nor lost is the most common outcome in many businesses, and the least examined. The firm’s assistant followed up each of the 240 quotes until it had a clear answer, or until it was six weeks old.
About a quarter of quotes received no reply until someone called. Of those, 34 customers answered after one call, and several of them placed orders. The firm had never followed up quotes systematically, because nobody owned the task. A simple rule, a call three working days after every unanswered quote, became part of the new process even before any software changed.
Ask the customers you lost
The records showed what happened but not why. The sales manager called 20 customers who had chosen not to buy, chosen at random from the lost quotes, and asked a single open question: what made you decide? This is an illustration of the answers:
- 7 said another supplier quoted first and they needed to order quickly;
- 6 said the price was higher than an alternative;
- 4 said the bench did not quite match what they needed, usually a size or option the firm could have supplied if asked;
- 3 said the project had been cancelled or deferred.
Twenty calls is a small sample, and the sales manager treated the answers as clues rather than proof. Speed was the most common reason, consistent with the turnaround data. Price was close behind, which is a useful warning against cutting prices to win: some of those customers were comparing with lighter-gauge or imported benches, a different product. The four who said the bench did not match their need pointed to a quoting problem. The estimator had quoted what was asked for without asking what it was for, a gap a configurator’s questions could close.
Accuracy
Next, the firm compared the prices it had quoted with the costs its accounts system recorded for the benches it actually built. For 96 won quotes in the period, the estimators recalculated what each price should have been using current costs and the firm’s target margin.
About 6% of the won quotes were priced on average 8% below what the firm’s own pricing method would have given, usually because a past quote had been reused without updating for a cost change, or because an option had been missed. A few were priced too high, which may have cost sales but did not show in the won-quote sample.
Over a year, with about 576 benches won at an average quote of about A$2,400 excluding GST, the underpricing comes to about 576 × 0.06 × 2,400 × 0.08 ≈ A$6,636. That figure is modest. The firm also found three quotes in the eight weeks for configurations that could not be built as described, such as castors under a bench with a sink, which needed awkward conversations with the customer after the order.
Discounts
The last measure was the one nobody expected to matter most. Comparing quoted prices with final invoiced prices showed that about 30% of won quotes had been discounted after the quote, at an average of about 7%, usually by a sales representative closing the deal by phone. Over a year that is about 576 × 0.30 × 2,400 × 0.07 ≈ A$29,030 of price given away, roughly four times the underpricing from errors.
What the baseline says
The measurements reorder the problems. Estimator time is real, about 1,080 hours a year. Turnaround appears to affect win rate substantially. Discounting gives away more margin than pricing errors. Unbuildable configurations are rare but damaging. A configurator could address the first two and the fourth directly. The discounting problem needs a policy and approval rules, which Chapter 6 covers, and a configurator only helps by making the approved price visible and the approval process quick.
The baseline also gives the firm a way to value improvement. Suppose a configurator brought turnaround to the same day for most quotes and lifted the win rate from 40% to 45%. At 1,440 quotes a year and A$2,400 each, that would be about 1,440 × 0.05 × 2,400 = A$172,800 of extra sales a year. At the firm’s gross margin, that is worth far more than the estimator time saved. Whether it would happen is uncertain, but the firm now knows what to watch.
A failure case: the vendor’s promise
This is an illustration. A year before this study, the firm’s owner attended a trade show where a configurator vendor presented case studies of customers who had increased their win rates “by up to 30%” and cut quoting time “by 90%”. The vendor offered a discount for signing that month. The owner was close to agreeing, at a first-year cost of about A$38,000 including implementation, excluding GST.
The production manager asked a simple question: what is our win rate now, and how long does a quote take now? Nobody knew. The firm had no way to judge whether the vendor’s figures were plausible for its products, and no way to check afterwards whether it had got what it paid for.
The detection came from someone asking for a baseline, which is the point of this chapter. The firm declined the offer, ran the eight-week measurement and then approached two vendors and one local developer with a specification built on the measured problems, discussed in Chapter 8 and in the companion book on commissioning custom software. The vendor’s figures may have been true for its other customers. They told the firm nothing about its own.
The measurement also changed what the firm went on to ask vendors for. Instead of a general configurator with every feature, it asked for four things the baseline showed it needed: same-day quotes for standard benches, prices built from current costs, rules that stop unbuildable combinations, and a visible approval step for discounts. The two vendors’ proposals were easier to compare against those four needs than against each other’s feature lists, and the local developer’s proposal was cheaper because it did not include features the firm would never use.
Keep measuring
The measurements are cheap to keep going. The assistant’s log of enquiry date, quote date, outcome and final price became a permanent column set in the firm’s quoting spreadsheet, filled in as each quote moves on rather than reconstructed later. Once a configurator is in place, Chapter 8 compares the same measures against this baseline. Without the eight weeks of data, the firm would be relying on impressions, which in quoting tend to favour whatever the person reporting them most wants to be true.
How this applies to a small Australian business
Small manufacturers rarely have a dedicated estimating system, so the data is in emails, spreadsheets and the accounts package. Eight weeks of an assistant joining them together, a few minutes a day, is enough to see the shape of the problem. Australian food, pharmaceutical and fabrication customers often buy against tight installation schedules, so turnaround deserves particular attention. If the baseline shows that discounting leaks more margin than errors, start with a discount policy, which costs nothing, before buying any software. The broader principles of pricing for profit are in Pricing your product or service.
Exercise: a smaller firm’s baseline
A smaller fabricator quotes 60 jobs a month at about A$1,800 each excluding GST, wins 35%, discounts 25% of wins by an average of 5%, and underprices 8% of wins by an average of 10%. Calculate the annual discount and underpricing losses, and say which problem to tackle first.
Worked answer. Wins a year: 60 × 12 × 0.35 = 252. Discount loss: 252 × 0.25 × 1,800 × 0.05 ≈ A$5,670. Underpricing loss: 252 × 0.08 × 1,800 × 0.10 ≈ A$3,629. Discounting costs more, so start with a discount policy and approvals, which need no software. Then look at the underpricing causes, usually outdated past quotes, which a simple cost-based price sheet would fix before any configurator.