How to build your client retention rate as a solo beauty pro

June 19, 2026 · 22 min read · back to blog

Most solo beauty pros know which clients come back and which don't. They just can't tell you the rate — the actual percentage of first-time clients from a given month who booked again within 60, 90, or 180 days. That number exists. It can be calculated from any booking record. And once you have it, it tells you things that the feeling of "I have a lot of regulars" cannot: when a price increase is justified by the data, when the waitlist is thick enough to stop filling with new clients, which clients to release, and whether your deposit-first booking change actually moved the needle on long-term commitment. This guide covers what client retention rate actually measures, how it differs from rebooking rate and other metrics solo pros commonly confuse it with, how to calculate it from whatever booking records you have today, how deposit-first booking changes the number at every measurement window, and what specific actions the number should trigger.

What client retention rate actually measures

Client retention rate is a cohort-based metric. It answers one question: of the clients who had their first appointment in a defined period, what percentage came back for at least one more appointment within a defined window after that first visit?

The cohort framing is what makes the number meaningful. You are not asking "how many clients do I have?" or "how many appointments did I do last month?" You are asking: of the specific group of people who became clients in January, what fraction of them are still clients by March? By April? By July? The percentage that answers each of those questions is your 60-day, 90-day, and 180-day retention rate.

The temporal window is the other critical element. A client who books in January and returns in December is technically retained — but she is not retained in any way that meaningfully affects your business in the intervening eleven months. Her one-appointment-per-year pattern produces different economics than a client who returns every six to eight weeks. The measurement window you use needs to reflect the natural rebooking interval for your service category. Color work rebooks every six to twelve weeks. Cuts reboot every four to six weeks. Lash fills reboot every two to four weeks. Nail appointments rebook every two to three weeks. The relevant retention window for a nail tech (60-day) is different from the relevant window for a colorist (90-day) because the expected interval between appointments is different. A 60-day window that shows 70% retention for a nail tech is worse than the same 70% for a colorist, because the nail tech expected two to three appointments in that window while the colorist expected one.

Understanding the service-specific rebooking interval matters because it sets the baseline expectation. A client who books a full color in January and has not returned by April is likely churned — the interval is too long to explain by scheduling variability alone. A client who booked lash fills in January and has not returned by February is almost certainly churned within her service category. Measuring at the wrong window will produce a rate that feels fine but hides real churn.

Retention rate vs rebooking rate vs retention system: three different things

These three concepts are related but measure different things and drive different decisions. Conflating them is one of the most common sources of confusion for solo beauty pros trying to think systematically about their client base.

Rebooking rate at the chair is the percentage of clients who rebook during their current appointment before leaving. It is a same-session conversion metric. A high rebooking rate at the chair means the chair-side close is working — clients are committing to a next appointment in the moment. It does not tell you whether those clients actually show up for the appointment they booked, or whether they rebook again after that second visit, or whether they are building into long-term relationships. A pro with a 75% rebooking rate at the chair might still have a poor 90-day retention rate if those rebooked appointments are frequently cancelled, rescheduled to distant dates, or not followed by a third visit.

A client retention system is the set of processes you use to encourage clients to return — the follow-up message cadence, the annual rebook nudge, the referral ask, the waitlist management process, the rebooking window in the booking confirmation. The system is the operational infrastructure. The retention rate is the output that tells you whether the system is working. You can have a perfectly designed retention system and a poor retention rate because the clients you are retaining were never the right clients to begin with, or because the booking friction on return visits is higher than it should be, or because your price increased before the relationship was established enough to absorb it without prompting a competitive comparison.

Client retention rate is the lagging metric that validates or refutes what the rebooking rate and the retention system are producing. It is the number you should be measuring when you want to know whether your actual client relationships are deepening over time — not just whether the chair is filling.

The practical implication: solo pros who only track rebooking rate can have an inflated sense of their retention because the metric doesn't follow clients forward in time. Solo pros who build a retention system without measuring the output have no way to know whether the system is producing the outcome it was designed for. The retention rate is what connects the two.

How to calculate your retention rate

You do not need a CRM. You need a booking record that shows, for each client, the date of their first appointment and the date of each subsequent appointment. Most booking systems — even basic ones — can export this as a spreadsheet. Instagram DM booking logs can be reconstructed manually for a recent cohort. If you have been keeping any kind of record at all, you have enough to calculate at least a 90-day rate for clients from three months ago.

The calculation itself is straightforward. Define your cohort: all clients whose first-ever appointment with you was in a given calendar month. Then count how many of them had at least one additional appointment within your chosen window. Divide that count by the total cohort size. That percentage is your retention rate for that cohort at that window.

Example: You had 14 first-time clients in January 2026. By the end of March 2026 (90-day window), 9 of them had booked a second appointment. Your 90-day retention rate for the January 2026 cohort is 64%.

To get a reliable picture, calculate the rate for at least three consecutive monthly cohorts. A single month's cohort is too small to be statistically meaningful and may be affected by seasonal variation, a referral spike, or a promotional push that attracted atypical clients. The average of three consecutive months gives you a number you can actually act on.

The three windows tell you different things. The 60-day rate shows you whether first-time clients converted quickly into a second visit — this is the strongest signal for services with short natural rebooking intervals (nails, lashes, cuts). The 90-day rate is the most broadly useful for beauty services generally — it captures a full rebooking cycle for most color work and gives enough time for a client who had a good first appointment to decide whether to come back. The 180-day rate shows you whether relationships are deepening into genuine regulars versus clients who booked twice and then churned. For most solo pros, the 90-day rate is the primary number and the 180-day rate is the secondary validation.

Benchmarks by service category, based on solo booth-rental operators surveyed in 2025-2026: color work typically sees 55–68% 90-day retention without a structured rebooking process and 70–82% with one. Cut-only services see 60–72% and 75–85% respectively. Nail services see 65–78% and 80–90%. Lash services see 55–70% and 72–85%. These ranges are wide because they reflect a mix of booking methods, price points, and client demographics — the number you care about is your own rate relative to your own prior period, not the benchmark itself.

Why most solo pros measure by feel — and what that costs

The feel-based approach to retention is almost universal among solo pros. It sounds like "I have a lot of regulars," "most of my clients come back," or "I don't really have a retention problem." These statements are usually sincere. They are also usually imprecise in ways that cost money.

The cost shows up most clearly at two specific decision points: the price increase decision and the waitlist threshold decision.

The price increase decision requires you to know two things: whether your chair is full enough that losing some price-sensitive clients won't hurt revenue, and whether your retained clients have a relationship with you strong enough that a price increase will not trigger a competitive comparison. The feel-based answer to both questions is "I think so." The data-based answer is: your 90-day retention rate tells you whether clients who came once are actually coming back (relationship depth), and your booking lead time tells you whether the chair is full enough to absorb price-related churn. A solo pro who raises prices with a 45% 90-day retention rate is raising prices on a client base that is already churning at a high rate before the increase. Adding price sensitivity to an already-fragile relationship set is a compounding problem. A solo pro who raises prices with a 78% 90-day retention rate is raising prices on a base that has already demonstrated commitment, where the price increase is adding friction to an established relationship rather than a tentative one. The same increase, radically different risk profile.

The waitlist threshold decision is similar. "Should I stop taking new clients and move entirely to a waitlist for existing-client overflow and referral-only new clients?" is a question that depends on how strong your existing client relationships actually are. If your 180-day retention rate is 55%, your existing client base is churning fast enough that you need new client volume to maintain fill rate. If your 180-day retention rate is 78%, your existing clients are stabilizing and you have enough of a base to reduce new client volume without jeopardizing the chair.

The third decision point where feel fails is the client release decision. Most solo pros who have a client with a problematic pattern — frequent late cancellations, aggressive price pushback, scope creep on every visit — spend months managing the relationship rather than releasing the client because it "feels" like the client is part of the base. But if that client has a 90-day retention rate of 100% and low lifetime value (she always finds a coupon, always pushes on price, and always books the minimum service), she is taking a slot that could go to a retained client with higher expected lifetime value. Retention rate by individual client shows you this. Retention rate by booking source or booking method shows you whether the pattern is structural.

How deposit-first booking changes your retention rate

The relationship between deposit-first booking and client retention rate operates through three distinct mechanisms, and the effect shows up at every measurement window.

The first mechanism is pre-selection at booking. A deposit-first booking requires the prospective client to make a financial commitment before the appointment is confirmed. That commitment filters out a category of potential client that the DM-first flow includes: the tentative booker who likes the idea of making an appointment but hasn't committed to it in any meaningful sense. Tentative bookers have high no-show rates (the same category that produces the 8–10% no-show rate for DM-first clients vs the 3–4% for deposit-first clients), but they also have low retention rates if they do show. A client whose first interaction with a booking system was "I'll just put something down, I can always cancel" is less likely to rebook than a client whose first interaction was "I'm paying a deposit now because I'm committed to this appointment." Deposit-first booking removes the first category from your cohort before they become clients, which raises the starting retention rate of the cohort by changing who enters it.

The second mechanism is behavioral anchoring through financial commitment. The psychology of sunk-cost and investment commitment operates in both directions: a client who paid a deposit to book an appointment feels differently about that appointment than a client who booked verbally. She is more likely to prepare for it (arrive on time, bring reference photos, answer the pre-appointment intake), more likely to be satisfied at the end of it (because preparation produces better outcomes for hair and beauty services specifically), and more likely to rebook afterward because the experience was better than average. Better experience at visit one produces higher probability of visit two. The deposit is not just a financial mechanism — it is an attention signal that improves appointment quality across the board, which compounds into higher retention at every measurement window.

The third mechanism is forward booking at the deposit step. When a deposit-first booking system is configured to surface the next available rebooking window at checkout (or in the booking confirmation), deposit-first clients receive an invitation to plan their next appointment before they have even had their first one. This is not a spam message — it is a "while you're here, your next appointment would fall in this window" note that appears during the most motivated moment in the client journey: the moment she just paid to secure a slot. A subset of those clients will pre-book their second appointment before they have attended their first, which eliminates the post-appointment rebooking window entirely and guarantees a second visit regardless of how the first one lands.

The net effect of all three mechanisms: deposit-first clients reliably show 8–14 percentage points higher 90-day retention rates than DM-first clients in the same service category at the same price point. The spread widens at 180 days (10–18 percentage points) because the behavioral anchoring effects compound over time while the tentative-booker churn that DM-first booking carries continues to erode the DM-first cohort.

What a deposit-first client's booking history looks like at month 12

The concrete difference between a deposit-first and a DM-first client's booking history at month 12 is visible across four dimensions.

Booking interval. A deposit-first color client who starts in January 2026 typically shows a booking interval that stabilizes between seven and ten weeks by month six and remains stable through month twelve. The interval may compress slightly (she books slightly more frequently as the relationship deepens) or remain stable, but it rarely stretches significantly unless the client proactively communicates a schedule change. A DM-first color client who starts in January 2026 and returns at all shows a booking interval that is more variable through month six — the first return might be at week eight, the second at week fourteen, the third at week seven — and a higher probability of not returning at all after the second or third visit. The deposit-first client's twelve-month booking history shows regularity; the DM-first client's shows volatility.

Price acceptance. A deposit-first client who has booked seven or eight times by month twelve has internalized the current price through seven or eight payment cycles. A price increase of 10–15% at month ten or eleven lands as a minor adjustment on a regular expense she has already categorized. A DM-first client with the same number of visits but more variable booking history may not have the same automatic expense categorization — each visit has a slight novelty that requires her to re-evaluate the price. The deposit-first client's month-twelve record shows zero price objections; the DM-first client's record shows a higher probability of at least one re-price event or rate inquiry.

Scope consistency. Deposit-first clients who pre-approve scope at the booking stage have their services defined before they arrive. By month twelve, the intake record shows consistent service selection with predictable variations (seasonal additions, service upgrades) rather than arriving-and-deciding behavior. The DM-first client who decides her service scope on arrival or in the chair creates more per-appointment variability, which makes time management harder and makes the financial projection of her long-term value less reliable.

Referral pattern. Deposit-first clients who have experienced a structured booking flow — intake form, deposit confirmation, preparation instructions, good appointment, easy rebooking — have a model for what their friends can expect. When they refer, they say "here's her booking link" rather than "DM her and figure it out." The referral carries the full booking structure with it, which means the client their referral generates tends to be deposit-first from the start as well. By month twelve, the deposit-first client's referral history shows a higher probability of having generated at least one additional deposit- first client. The DM-first client's referral history, where it exists, tends to generate DM-first clients — the same pattern continues.

The three inflection points where retention rate drives a specific action

The value of having an actual retention rate number is that it tells you when to take a specific action, rather than relying on intuition about whether the conditions are right. Three actions have clear retention-rate thresholds.

60-day rate drives waitlist-fill strategy. If your 60-day retention rate is below 50%, your new clients are churning at a rate that requires a continuous intake of replacement clients to maintain fill. The waitlist strategy at this retention level should prioritize volume: keep the new-client intake open, maximize the waitlist for slot-fill purposes, and focus on understanding why the 60-day rate is low before attempting a price increase. If your 60-day rate is above 65%, you have enough short-cycle retention to begin considering reducing the new-client intake window and shifting toward a smaller, higher-quality waitlist. The 50–65% range is a watch-and-measure zone: run the number for three consecutive months before deciding.

90-day rate drives price increase timing. A 90-day retention rate above 70% is the data signal that a price increase is likely to be absorbed without significant net revenue damage. At 70%+, at least seven of every ten clients who come once are demonstrating enough commitment to return within a full rebooking cycle. That commitment level is durable enough to survive a 10–15% price increase for most clients in that cohort. Below 60%, a price increase risks accelerating churn in a cohort that is already fragile. The 60–70% range is where the price increase may work but should be introduced gradually (test on new clients first, roll to existing clients over two to three months) rather than as a clean price-sheet update applied universally on a given date.

180-day rate drives client release decisions. The 180-day rate tells you which clients from six months ago are still active. Clients who do not appear in your booking record at all in the 180-day window after their last appointment are effectively churned. The question the 180-day rate answers at the individual level: is this specific client whose appointment history includes two late cancellations and a no-show worth the slot opportunity cost she is generating? If she appears in the 180-day record as retained but with below-average service value, she is occupying a slot that would go to a retained client with higher expected value. If she does not appear in the 180-day record, she is already churned in practice — the decision about whether to pursue a re-engagement is separate from whether to hold the slot for her return.

How to track it without a CRM

The two-column method works for almost any solo beauty pro's volume. Open a spreadsheet with three columns: Client Name (or initials), First Appointment Date, and a column for each measurement window (Second Appointment by Date X, Third Appointment by Date Y). Fill this in from your booking system export or from your booking history log.

The monthly retention audit takes fifteen to thirty minutes. On the first Monday of each month, pull the cohort from three months ago (for the 90-day rate) and six months ago (for the 180-day rate). Count how many of each cohort's clients have a second (or further) appointment in the window. Divide. Record the rate. Compare it to the prior month. That is the entire process.

If you have a booking system that exports to CSV (most do — Booksy, Square Appointments, Acuity, ChairHold), filter by first-visit date to get your cohort, then filter for any subsequent visit within the window. The count of clients with at least one subsequent visit, divided by the cohort size, is your retention rate. The filter takes two minutes once the export is clean.

If you are working entirely from manual records (a paper appointment book, DM history), the process is slower but the same logic applies. You are looking for whether specific clients appear again after their first visit. Even a rough cohort of ten first-time clients from three months ago gives you a directionally meaningful rate.

The discipline is running the audit on a consistent schedule rather than reactively. A retention rate calculated twice a year in response to a slow week tells you what happened months ago. A retention rate calculated monthly gives you a rolling picture that catches declining retention early enough to diagnose and address the cause before it compounds.

What falling retention rate usually signals

A retention rate that drops two to three percentage points between consecutive cohort measurements is worth investigating. The most common causes fall into four categories.

Client mix shift. If you recently ran a promotional offer, got featured in a local directory, or had a spike of referrals from a single source, the cohort from that month may contain a higher proportion of price-sensitive or low-commitment clients than your typical cohort. The drop in retention is real but may not reflect a change in your booking quality — it reflects a change in who entered the cohort. The diagnostic: check whether the cohort with falling retention coincides with a specific acquisition spike.

Price increase without sufficient relationship depth. If you raised prices in the period between a cohort's first visit and their 90-day window, clients who had only one prior appointment at the old price experienced the increase as a near-stranger asking for more money. The relationship was not established enough to absorb the change. The diagnostic: check whether the cohorts immediately following a price increase show lower 90-day retention than the cohorts preceding it.

Booking friction increase. If you changed your booking system, moved to a different platform, added a new intake form, or changed your booking page URL, some portion of returning clients who went looking for the booking link found friction they didn't expect and didn't push through. The diagnostic: check whether the retention drop aligns with a system change.

Service quality variance. A period of unusually high volume, a process change in a particular service, or a run of difficult appointments can produce lower satisfaction across a cohort. The diagnostic: check whether complaint or reschedule rates in the cohort period were above average.

The retention rate doesn't tell you which cause is operating — it tells you that something changed. The diagnosis requires one more level of specificity, usually available from the booking records you already have.

Segmenting by booking source

Once you have a baseline retention rate, the most informative segmentation is by how the client found you and how they booked. The relevant comparison for most solo beauty pros is deposit-first vs DM-first booking, and referral vs cold discovery.

Calculate the retention rate separately for clients who came in from Instagram DMs with a deposit-first booking versus clients who booked directly through a booking link without a prior DM conversation. Then compare referral clients to cold-discovery clients. The segmented rates tell you which acquisition channel and which booking flow is producing your most retained clients, which informs where to invest marketing attention and whether the booking flow change you made is actually showing up in the retention data.

Most solo pros who make this comparison find that deposit-first referral clients have the highest 90-day retention, followed by deposit-first cold-discovery clients, followed by DM-first referral clients, with DM-first cold-discovery clients showing the lowest retention of the four segments. The gap between the highest and lowest is typically 15–22 percentage points at the 90-day window. That gap is the per-cohort cost of DM-first cold booking, expressed as retention rather than revenue.

Six common retention rate mistakes

Measuring by feel instead of by cohort. "Most of my clients come back" is not a retention rate. It does not tell you how many first-time clients never returned, whether the retention is improving or declining, or whether it is high enough to support a price increase. Replacing the feel with a number is the single highest-leverage diagnostic change a solo beauty pro can make.

Conflating rebooking rate with retention rate. A client who rebooks at the chair and then cancels the rebooked appointment was retained at the rebooking step but not retained in the retention sense. The retention rate measures actual subsequent appointments, not commitments made at the prior appointment.

Using the wrong window for the service category. A 90-day retention rate for a nail tech whose clients have a natural two-to-three- week rebooking interval understates churn because it allows far more time than the service interval warrants. The nail tech should be looking at the 60-day rate as her primary metric. A 60-day rate for a colorist working on keratin treatments and balayage on a twelve-week interval overstates churn because it doesn't give clients enough time to reach their natural rebooking window. Match the window to the expected interval.

Not segmenting by booking source or booking method. A blended retention rate that averages deposit-first and DM-first clients hides the contribution of each. If 40% of your clients book via deposit- first and show 80% retention, and 60% book via DM-first and show 55% retention, your blended rate is 65% — which looks respectable but obscures the structural advantage in the deposit-first segment. Segmenting lets you act on what is actually working rather than managing an average.

Raising prices before checking the retention rate. Price increases should follow retention data, not precede it. A retention rate check before a price increase takes fifteen minutes. The cost of a price increase applied to a fragile client base is months of churn recovery.

Calculating retention rate once and not updating it. A retention rate calculated in January and not revisited until October is a snapshot of a past period, not a current signal. Monthly calculation on a consistent schedule is what turns the metric into an early-warning system rather than a post-mortem.

Three operational checklists

One-time setup (60–90 minutes)

1. Pull your booking export or reconstruct your most recent three months of first-visit clients from whatever records you have.
2. Create a three-column spreadsheet: Client, First Visit Date, Return by Date (for each window).
3. Calculate your 90-day retention rate for the most recent complete cohort (clients whose first visit was three-plus months ago).
4. Calculate the same rate for two prior monthly cohorts to get a three-month average.
5. Record the rate and the date in a running log (a note, a spreadsheet cell, anywhere consistent).
6. Set a monthly calendar reminder for the first Monday of each month to run the audit.
7. If you have enough booking history, segment the rate by booking source (deposit-first vs DM-first) and note the gap.

Monthly retention audit (15–30 minutes, first Monday)

1. Pull the cohort from three months ago. Count: how many first-time clients in that month? How many have returned at least once since? Divide. Record.
2. Pull the cohort from six months ago. Calculate the 180-day rate. Record.
3. Compare both rates to the prior month. If either dropped more than three percentage points, note which cohort and flag for diagnosis.
4. Review any individual clients in the 180-day cohort who have not returned. Are any of them clients you have been managing through a difficult pattern? The 180-day non-return is confirmation to release the slot, not continue the hold.
5. Set one forward action based on what the data shows. A rising rate means price increase timing is approaching. A falling rate means diagnosis before further action.

Quarterly retention review (60–90 minutes, every 13 weeks)

1. Calculate the 90-day and 180-day rates for all three months in the quarter. Average them. Compare to prior quarter average.
2. Segment by booking source if you have the data. Note the deposit-first vs DM-first gap. Is it widening or narrowing?
3. Check whether any price increases were made in the quarter and whether the cohorts immediately after the increase show different retention than cohorts from before.
4. Review the three clients with the lowest individual visit frequency in the past six months. Make a deliberate decision about each one: active re-engagement attempt, reduced priority for new slot allocation, or release.
5. Set the retention rate threshold decision for next quarter: if the 90-day rate is at or above your price-increase threshold (typically 70%), set the next price review date. If it is below, identify one specific cause to address and one operational change to test in the next cohort.

Three-year compound: what the retention rate difference looks like in revenue

Two colorists with identical pricing, identical markets, and identical technical skill diverge on client retention rate from month one.

Colorist A uses a DM-first booking flow. She takes walk-in inquiries, books verbally, and follows up by DM when the appointment slot approaches. Her first-time clients no-show at 8%, return in the 90-day window at 54%, and retain to month twelve at 38%. She starts at $120 for a full color and raises to $130 at month fourteen because a peer mentioned it was time to raise. She acquires twelve to fifteen new clients per month to keep the chair full as existing clients churn. By year three she has a stable roster of about sixty active clients — clients who have booked in the past six months — but the roster has churned through roughly 280 clients to arrive at that sixty. Her average monthly revenue across thirty-six months is $4,700. Cumulative three-year revenue: approximately $170,000.

Colorist B switches to deposit-first booking in month one. Her first-time clients no-show at 3.5%, return in the 90-day window at 72%, and retain to month twelve at 61%. She calculates her 90-day retention rate monthly. At month nine, the three-month average crosses 70% for the first time. She raises from $120 to $135 — a price increase she applies to new clients immediately and to existing clients with thirty days notice. The price increase produces two churn events in a roster of about forty-five active clients; the chair fills from the waitlist within one week. At month sixteen, the three-month average has been above 72% for four consecutive months. She raises to $150. At month twenty-four, she raises to $162. At month thirty, her roster of forty-two active clients books two to three weeks in advance. She acquires four to five new clients per month — enough to replace natural churn — and applies her waitlist to fill any cancellations. Her average monthly revenue across thirty-six months is $6,400. Cumulative three-year revenue: approximately $231,000.

The $61,000 gap over three years — from the same chair, the same skill, the same market — comes from three compounding factors: the deposit-first retention rate that produced higher repeat booking frequency, the price increase timing that was keyed to retention data rather than peer advice, and the reduced acquisition cost that came from not needing to replace a perpetually churning DM-first client base. None of those three factors required a new marketing channel, a rebrand, or a new service offering. They required a measurement habit and the booking infrastructure to produce the data.

Colorist B's primary ongoing task, from month nine onward, is a fifteen-minute audit on the first Monday of each month. The audit produces the number. The number drives the decision. The decision compounds.

Where to start

The one-time setup takes sixty to ninety minutes. The output is a number you do not currently have. That number is the foundation for the price increase decision, the waitlist threshold decision, and the client release decision — three decisions that most solo pros make by feel and that benefit more than almost anything else from a data point to anchor them.

If you have been booking for at least three months with any kind of record, you can calculate a 90-day retention rate today. Pull the cohort from three months ago. Count the first-time clients. Count how many of them came back. Divide.

If the number is below 60%, the most likely cause is either a DM-first booking flow that allows tentative clients into your cohort or a gap in the post-first-visit follow-up process. Switching to deposit-first booking and adding a rebooking prompt to the booking confirmation are the two highest-leverage changes at this retention level. If the number is above 70%, you are likely past the threshold for a price increase and the data supports making it. If the number is between 60% and 70%, the retention is moving in the right direction — the question is whether there is a specific segment (DM-first, cold-discovery, promotional cohort) that is pulling the average down and whether that segment can be addressed without changing the booking flow for the higher-retention segments.

The metric is already there in your booking records. The fifteen minutes to calculate it produces a clearer picture of your client relationships than any number of feel-based assessments will.