Situation–Complication–Resolution: a structure for communicating analytical findings in a way that drives decisions. Popularised by McKinsey, it turns a pile of metrics into a narrative people act on.
▸ Try the interactive toolThe SCR framework — Situation, Complication, Resolution — is a structure for communicating analytical findings in a way that drives decisions. Popularised by McKinsey, it organises a data story into three layers: the Situation establishes context and what was expected; the Complication introduces what changed or went wrong; the Resolution proposes what to do about it.
The framework exists because data doesn't speak for itself — a pile of metrics, however accurate, drives no decision until it's shaped into a narrative. SCR provides that shape: it leads the audience from shared context (Situation) through the tension that demands attention (Complication) to a clear recommendation (Resolution). The most common failure it fixes is the data dump — presenting every chart and number and leaving the audience to figure out what it means. SCR forces you to do that interpretive work for them, ending not with data but with a decision.
Situation — the context, what was expected.
Complication — what changed / the problem / the tension.
Resolution — the recommendation, what to do.
It turns metrics into a narrative that ends in a decision.
Data doesn't drive decisions on its own — presented as a dump of charts, even rigorous analysis leaves the audience to guess the point, and mostly nothing happens.
| The shortcut | What it costs | What it gives you instead |
|---|---|---|
| Data dumps | Every chart presented; the audience must find the point. | SCR does the interpretation and leads to a decision. |
| No narrative | Disconnected metrics that don't add up to anything. | The Situation–Complication–Resolution arc gives them meaning. |
| Burying the recommendation | The 'so what' lost among the numbers. | Resolution makes the recommendation explicit. |
| Starting with detail | Diving into data before establishing context. | Situation first gives the audience the frame to understand it. |
Open with the shared context — what's the background, what was expected or normal? This gives the audience the frame they need before any data means anything.
Present what changed, went wrong, or created tension — the reason this analysis matters. The complication is what earns the audience's attention and motivates the resolution.
Make the recommendation explicit: given the situation and complication, here's what we should do. End with a decision or action, not with more data.
Bring in metrics where they advance the narrative — evidence for the complication, support for the resolution — not as a comprehensive dump. The story leads; the data backs it.
Don't present numbers and leave the audience to interpret. SCR's discipline is that you turn the data into meaning and a recommendation — that's the value you add over a raw dashboard.
A PM presented an analysis as a comprehensive deck — chart after chart of accurate, detailed metrics — and the meeting ended with the audience unsure what they were being asked to decide. The data was sound; the communication failed. Everyone had to reconstruct the point for themselves, so the analysis drove no decision.
Restructured as SCR, the same findings landed. The Situation set the expected baseline; the Complication revealed the specific problem the data exposed; the Resolution proposed a clear action, with just the few charts that supported the arc. The audience was led from context to tension to recommendation — and left with a decision to make rather than a pile of data to interpret. The analysis hadn't changed; its shape had.
The deliverable is a finding communicated as Situation–Complication–Resolution — a narrative ending in a recommendation, with data in support.
| Data dump | SCR story |
|---|---|
| Every chart, no arc | Situation → Complication → Resolution |
| Audience finds the point | You deliver the point |
| Ends in data | Ends in a recommendation |
| Data leads | Story leads, data supports |
SCR turns analysis into action by giving data a narrative shape that ends in a decision. Its core discipline is doing the interpretive work for your audience rather than dumping charts and hoping they find the point.
The data-dump failure is endemic among analytically strong PMs precisely because they're comfortable with the numbers — they present everything they found, assuming the conclusion is as obvious to the audience as it is to them. It isn't. An audience handed a comprehensive deck has to reverse-engineer the analyst's thinking, and mostly won't, so even rigorous analysis drives no decision. SCR fixes this by imposing a narrative that leads the audience: establish shared context, introduce the tension that demands action, and deliver the recommendation — with data brought in only to advance the story. The value a PM adds over a raw dashboard is exactly this interpretation: transforming what the data says into what the team should do. SCR is the structure that forces that transformation.
Every chart and no narrative leaves the point unfound. Shape it with SCR.
The 'so what' lost in the numbers. Make the Resolution explicit.
Diving into data before context. Establish the Situation first.
Your value is doing the interpretation. Deliver meaning, not raw numbers.
SCR shapes the narrative within the metric review forum (Tool 28).
SCR is how you present revenue, retention, and experiment findings (Tools 14, 08, 17).
SCR is a core influence-and-communication skill (Module 6).
Like data-informed thinking (Tool 01), SCR keeps data in service of decisions.
AI is a strong drafting and structuring partner for the data story — helping you shape findings into Situation–Complication–Resolution.
The judgment that stays yours: AI can structure a story, but it can't supply the judgment of what the data means or which decision to advocate — that interpretation is the value you add. And it will confidently narrate a conclusion the data doesn't support, so the analysis must be sound before you let it tell the story.
Take an analysis you might present. Instead of listing the charts, write its three layers: the Situation (context/expectation), the Complication (what the data revealed), the Resolution (your recommendation).
Notice which charts you actually need to support the arc — and how many you'd have dumped that don't.
If the SCR version ends with a clear recommendation the audience can act on — where the data dump ended with them guessing — you've seen why narrative, not numbers, drives decisions.