How to measure your practice's enquiry response time in one week

The study everyone half-remembers is "The Short Life of Online Sales Leads", published in Harvard Business Review in March 2011 by James Oldroyd, Kristina McElheran and David Elkington. It found that firms which tried to contact a lead within an hour were nearly seven times as likely to qualify it as firms that waited longer.

The "21 times" and "100 times" figures that often appear next to it come from an earlier vendor presentation, and are usually misattributed to the HBR study or to MIT. We have stopped using them.

The direction is clear: interest fades quickly. But the study is old, American and about sales teams, not patients choosing a practice. It tells you speed matters. It cannot tell you how fast your practice is, or how much a slow week costs you. Only your own log can.

Count new enquiries only: someone who is not yet booked, asking about an appointment, a treatment or a price. Leave out existing patients confirming or moving appointments, suppliers and anything internal. Mixing them in makes the front desk look faster than it is, because routine admin is quick.

For each enquiry, write down four things:

1. 01When it arrived, to the minute.
2. 02The channel: phone, voicemail, web chat, WhatsApp, email, website form, Instagram or Facebook.
3. 03When the first reply that actually answered the question was sent.
4. 04Whether it ended in a booking, by the end of the following week.

For phone calls, "arrived" is the time the call rang out or went to voicemail, and "replied" is the call back. A call answered live counts as a reply in zero minutes. That is fair: it is the best outcome a patient can get.

Pick an ordinary week: no bank holiday, no staff shortage, no marketing push. Tell the team you are measuring the system, not them. If people know their own replies are being timed, they reply faster that week and you learn nothing.

Ten minutes at the end of each day is enough. Go through the call log, the inbox, the website form notifications and each messaging app, and copy new enquiries into the sheet. The first day takes longest because you discover where enquiries actually land. That discovery is useful in itself: look for any channel nobody was clearly responsible for.

Sort the minutes-waited column and take the middle value. That is your median: the experience of a typical enquiry. Then look at the slowest tenth. That is where patients most likely gave up and booked somewhere else.

Avoid the average. Ten replies in five minutes and one reply after two days give an average of more than four hours, which describes nobody's experience. The median would be five minutes, and the slowest tenth would point straight at the two-day reply, which is the one to fix.

Then split the numbers twice: by channel, and by whether the enquiry arrived during opening hours. One pattern to look for is a quick median on the phone during the day and a long tail on messages that arrive in the evening. If yours looks like that, the gap is coverage, not effort.

If the slowest tenth is mostly out-of-hours messages, look at who covers messages in the evening and at weekends, or what the first automatic reply promises. If it is spread across the day, look at ownership: which channel has no named person.

The booked column lets you compare enquiries answered quickly with those answered slowly, but be careful. One week is too small to prove cause, and people who message at midnight may differ from people who ring at ten. Treat it as a reason to look closer, not as proof.

ReplyOS keeps this log automatically across connected channels, which is the reason we built it. But the method above works with a spreadsheet, and it is worth doing before you buy anything, including ReplyOS.