RCM Insights sales demo - a prepared question, then the evidence that makes it defensible
Objective. A runnable, evidence-led demo script for RCM Insights built on the five-beat pattern: question -> screen -> evidence -> interpretation -> next step. The script sells defensibility, not fluency, states the honest limit out loud, and ends on the discovery questions the demo answers.
This demo wins by doing the opposite of a chatbot demo. Instead of improvising clever questions to show off fluency, you ask ONE prepared, decision-linked question and then spend the demo on the evidence behind the answer. The value point the buyer remembers is 'I could take this into a QBR', not 'it talks well'.
See it in the demo
The story arc
The arc is: a real decision a leader faces today -> a plain-language question about it -> a fast answer -> the evidence that makes the answer defensible -> a next step. The emotional turn you are engineering: the buyer is mildly impressed by the answer, then genuinely convinced when you open the evidence one click away and say 'this is the difference between a chatbot and something you can stand behind'.
Never demo random questions to show breadth. Fluency is table stakes and demoing it sells the product as a novelty. One prepared, decision-linked question carried all the way through to its evidence beats ten clever questions. The whole arc is built to make inspectable evidence the memorable thing.
The exact screens to show, in order, with one value point each
Run these in order, one value point each - do not tour every screen:
- Ask (https://rcm-insights.demo.timechamp.io/#/ask) - pose the one prepared, decision-linked question (for example 'which denial categories are growing fastest this quarter and what is the recoverable exposure'). Value point: a non-analyst got a meaningful answer today, without a ticket to the analyst queue.
- Data (https://rcm-insights.demo.timechamp.io/#/data) - open the evidence behind that answer. Value point: the source, coverage and freshness are one click away - presence of a source is not coverage, and here you can see the coverage.
- Metrics (https://rcm-insights.demo.timechamp.io/#/metrics) - show the definition of the key metric in the answer. Value point: the number means something specific and governable - a metric definition is what makes two figures comparable.
- Dashboards / Saved (https://rcm-insights.demo.timechamp.io/#/dashboards) - show the question saved into a reusable view. Value point: useful analyses are reused for real decisions, read in a stated scope, not re-derived each time.
Optional, if the room asks what to ask: Discover (https://rcm-insights.demo.timechamp.io/#/discover) - helps users find what is askable so they ask questions the data can support.
The limitation to state out loud
State this plainly, in the room, before anyone else raises it: the ask-and-inspect model is real and shown here on seeded demo data. Which live sources are connected for your account, how fresh they are, exactly what each metric means and how role scope is enforced are confirmed with product and set up in implementation - they are not what this demo proves.
And the discipline that comes with the product: an answer without suitable data coverage or evidence should not become a commitment. On thin data the honest answer is 'not enough data', and that honesty is the selling point to a finance-literate RCM buyer, not a weakness. Saying the limit out loud is what makes the rest of the demo credible - a rep who hides it sells a chatbot.
The discovery questions this demo answers
Use the demo to earn the answers to these, and ask them explicitly:
- 'When your leaders need an answer today, who do they ask, how long does it take, and can they see the assumptions behind it?'
- 'How often does a decision wait on the analyst queue? What does that delay cost you on a recoverable-AR or staffing window?'
- 'When a figure gets quoted in a client QBR or a board pack, how do you know it is right - can anyone verify the source, definition and freshness?'
- 'Have you been burned by a confident number that turned out to rest on partial or stale data?'
These map the pain to the product's actual value - decision speed AND decision defensibility at once - and set up the next step: a prepared, decision-linked question run against their own data in a scoped pilot.
Running it live - the five-beat discipline
On every beat, hold the pattern: question -> screen -> evidence -> interpretation -> next step.
- Question: state the decision, then ask it in plain language in Ask.
- Screen: let the answer land, briefly.
- Evidence: immediately open Data and Metrics - this is where you spend your time.
- Interpretation: say what the evidence lets them conclude, and what it does not - including 'if the data were thin, the honest answer would be not enough data'.
- Next step: propose running one of their real decision questions against their data in a scoped pilot, with definitions agreed up front.
If a prospect pushes you to improvise clever questions, redirect gently: 'I can, but the thing worth your time is whether you can trust the answer - let me show you the evidence.' That redirect is the demo.
Interpretation check
The demo sells defensibility, not fluency. An Ask answer is a starting point to verify ('not enough data' is valid); a saved dashboard reflects its own filters and scope; a metric definition governs whether a number is comparable. State the seeded-data limit out loud - hiding it sells a chatbot and destroys credibility.
In the field
KnackGlobal (RCM): the demo that lands is a director's real question - 'which payers drove the AR-days increase and is it worsening' - answered same-day with the evidence opened in front of them, because under active displacement the buyer's real fear is a fluent-but-unsupported figure repeated to their own client. Defensibility is the sale.
Practical task
Prepare and rehearse a seven-minute RCM Insights demo: write the one prepared decision-linked question, the four screens in order with the single value point you will say on each, the exact sentence in which you state the seeded-data limitation out loud, and the three discovery questions you will ask. Run it once end to end holding the five-beat pattern on every screen.
Self-check
Why one prepared question instead of improvising to show fluency?
Because the value is defensibility, not conversational polish. A prepared, decision-linked question lets you show the answer AND the inspectable evidence behind it - the real differentiator. Improvising sells the product as a chatbot.
What are the screens, in order, and the one value point of the Data screen?
Ask -> Data -> Metrics -> Dashboards/Saved (Discover optional). The Data screen's value point: the source, coverage and freshness behind the answer are one click away, and presence of a source is not coverage - here you can see the coverage.
What limitation do you state out loud, and why does stating it help?
That the demo runs on seeded data and the live sources, freshness, definitions and role scope are confirmed with product and set up in implementation. Stating it makes the rest credible; for a finance-literate buyer, honesty about data limits is the selling point.
A prospect pushes you to ask lots of clever questions. How do you respond?
Redirect: 'I can, but the thing worth your time is whether you can trust the answer - let me show you the evidence.' That redirect, back to inspectable evidence, is the demo.
What is the next step the demo sets up?
Running one of the prospect's own real decision-linked questions against their data in a scoped pilot, with metric definitions agreed up front - moving from demo fluency to defensible answers on their data.
Verified vs demo. All four screens are live in the demo and the ask-inspect-save-reuse flow is demo-shown on seeded data. Exact controls, answer logic, definitions and figures are SME-validation-pending; live sources, refresh cadence, metric definitions and role scope are customer- and entitlement-specific - confirm with product before any answer is quoted externally.