What measurement actually changes.
Kymata is a new practice, so we’re not going to show you a wall of logos we don’t have yet. What we can show you is how we think. Below are four scenarios drawn from the kinds of measurement problems businesses run into constantly, walked through the way we’d actually approach them.
These are illustrative scenarios, not client case studies. They describe common, real-world problems and how we’d work through them. We’d rather be upfront about that than dress up hypotheticals as history.
The problem
A multi-site group runs a patient outreach program: outbound calls, follow-up scheduling, reactivation campaigns for lapsed patients. The program director believes it’s working. The CFO isn’t sure, and the vendor contract comes up for renewal in a quarter.
The data exists. Appointment records, patient histories, reporting from every site. What’s missing is any connection between that and the question the CFO is actually asking: do patients who come through outreach stay longer and spend more, or do they churn at the same rate as everyone else?
How we’d approach it
Start with the tag. Can the group tell which patients came in through the program and which came back on their own? If it can’t, nothing downstream holds, because there’s no way to separate what the program did from what would have happened anyway.
Then read what the vendor’s reports measure. Touchpoints made and appointments booked are activity. A report full of activity can look excellent and still say nothing about whether the program earns what it costs.
What we’d build
A patient cohort tracking framework that tags reactivation patients at the point of re-entry, consistently across sites, then follows them through a defined value window: appointments completed, no-show rate, referrals generated, return visit rate. Where useful, we’d add a retrospective analysis from the historical data, with the confidence limits stated honestly given whatever tagging gap already exists.
The core question the framework answers: do reactivated patients return at a higher rate than the group average, and is the program earning the money it costs?
Whether the program changes patient behavior or just generates activity that looks like progress. You’d have a real number to bring to the renewal conversation, instead of two people defending opposite hunches across a budget meeting table.
The problem
A profitable multi-office firm brings in steady new-client volume. The managing partner puts it this way: “We’re spending a lot on marketing and I genuinely have no idea what’s working. I’ve asked the office manager, I’ve asked the agency, and nobody gives me a straight answer.”
The intake process captures how clients found the firm in a free-text box, when it captures it at all. Answers are inconsistent, frequently blank, and never tied back to financial reporting. Nobody can prove whether cutting a quarter of the budget would cost a single client.
How we’d approach it
The capture itself is the first problem. A free-text field with no standard options produces data nothing can be built on. Expect to find that a minority of recent clients have any source recorded, and that much of what is recorded says “Google” or “online” and stops there.
The second problem is that the four channels have different cost structures and different client journeys, and they’ve never been compared on the same terms. The agency may be driving contact-form volume that barely converts. Paid search may be feeding one practice area and nothing else. Events may be producing the most valuable clients in the portfolio, invisibly, because nobody ever tracked them.
What we’d build
A rebuilt intake attribution process: a structured, multi-step source question at intake (channel, then specific source, then referrer name where it applies), with standardized options and a required field. Then a value-tagging protocol so the firm can compare not just how many clients each channel produces, but how valuable they are.
On top of that, a simple monthly Channel ROI view: cost-per-contact, cost-per-client, and revenue-per-client by channel. Readable in a glance, and cheap to maintain.
A ranked, honest picture of where your marketing money goes and what comes back. That normally surfaces one channel worth cutting and one that’s been outperforming without the budget to match. From there, moving money is a decision you can defend, not a guess you renew every year.
The problem
Internal programs are the hardest thing on this page to measure, because almost none of them have a clean line to revenue. An operations leader can tell you what every program is doing. What nobody can tell you is what any of it changed, or which one to cut when the budget tightens.
It isn’t that the programs go untracked. Each one has its own reporting, often quite detailed. The reporting measures activity and process compliance rather than outcomes. When nobody defines success at the start, activity is all that’s left to measure at the end.
How we’d approach it
The same gap sits under every program: no baseline captured at launch, no agreed definition of success, nothing to compare results against. They’re experiments run without a control.
The metrics that do exist point the wrong way. A training program reports strong participation and completion, and nothing about whether retention or quality moved. A quality initiative reports scorecard updates rather than the defect reduction those scorecards were built to drive. Everything measured sits on the input side.
What we’d build
A Program Scoring Framework: a standardized way to give each program a baseline (reconstructed retroactively where needed), a defined success metric, and a regular review cadence, so every program is graded on the same terms at each review instead of on gut feel.
Paired with a portfolio view, a single page showing every program against its benchmark, so resource-allocation decisions become visual and defensible rather than political.
For the first time, a like-for-like comparison of programs that were never built to be compared. Some hold up. At least one won’t, and being able to prove it is what lets you move the money without a political fight. The lasting part is the habit it forces: the next program gets a success definition before it gets a budget.
The problem
This one looks different from the others on this page. Nothing has gone wrong yet. There’s no wasted spend to find and no broken tracking to repair. The company is deciding whether to make a change, and the cost of getting it wrong lands entirely in the future.
The founder puts it this way: “Everyone tells me subscription is where we should be. I believe them. I just don’t know what we’d charge, and I don’t know how many customers we’d lose finding out.” Those are answerable questions, and answering them costs a fraction of guessing wrong.
How we’d approach it
Start with what the business earns today, per customer and per segment, under the current model. Every scenario gets measured against that baseline, and it’s rarely as well understood as people assume. Transaction revenue concentrates: a small share of users generates most of it, which changes the answer considerably.
Then look outward. Companies have made this exact move before, and some of them wrote about what happened. What did they charge, what did they lose in the first year, and what did they change afterward? Comparable evidence won’t settle the decision on its own, but it puts a realistic range around the guesses.
Where the decision still turns on something nobody knows, which is almost always what customers would tolerate paying, that gap needs primary data. We’d design the instrument to get it: a survey built to measure price sensitivity and feature demand properly, rather than asking people whether they like the idea.
What we’d build
A model of each option, carried through to revenue, retention, and risk. Subscription at several candidate price points. Freemium with a paid tier. Staying transactional and growing volume instead. Each one carries assumptions, and each assumption is stated plainly enough to argue with.
The recommendation names a direction and the evidence behind it, along with the thing most analyses leave out: what would have to be true for the answer to change. A decision this size deserves to be revisited if the ground shifts, and that’s only possible if the reasoning was written down.
Whether to make the change, what to charge if you do, and roughly what it costs you in customers at each price. Not certainty, since no one can offer that on a decision about the future. But a recommendation you can put in front of a board, with the assumptions visible and the alternatives shown their fair hearing.