Data storytelling | A simulated case study 1,200 voices. 84,000 conversations. 25 faces. One clear move.

Scroll through a market entry project and watch three research methods fuse into a single direction. This isn’t a gallery of charts; it’s how data should be visualised to reach its full potential.

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This is a simulated case study. The company, the 1,200 respondents, the 84,000 conversations and the 25 faces are illustrative, built by us to show how the H3™ method turns human signal into a decision. No client data appears on this page, and each method is shown in a simplified form, far lighter than a real study.

The brief

A Berlin fintech. A market it had not read yet.

A Berlin-based personal finance app wanted to enter Spain. The question wasn’t how to launch, but whether to enter as they were, adapt their proposition or wait. Before designing anything, we measured the market they were about to walk into and ran a small soft launch in Spain, so we could see how people there lived with the app day to day.

Most firms would have fielded a quick survey and started designing screens. We did something far more certain. Before a single recommendation, we looked at the same question through three different lenses, because a market entry can often be broken by what you didn’t think to ask.

What follows is how the H3™ method turned scattered human signal into one direction the client could bet on.

*This brief is invented and deliberately simplified. A real project carries far more history, constraints and stakes than a few paragraphs can hold.

Project brief Illustrative

BerlinOrigin

MadridSpain | Target

Geographic distance: 1,870 km. Cultural distance: the whole point.

Client Fintech scale-up, Berlin
Objective Go-to-market, Spain
Product Personal finance app
Question Enter, adapt, or wait

At stake A seven-figure launch, and the single decision behind it.

Examined through three lenses Quantitative

The H3™ method

One question, seen through three lenses.

Many firms pick one method and call it research. We look at the same question from every angle that matters, then fuse what each one sees into a decision.

01 | QuantitativeThe ScaleHow big is the opportunity, and how high is the wall? 1,200 people draw the terrain.Go to this part
02 | Social listeningThe UnfilteredWhat people post, comment and film when nobody is asking. The honest texture of a market.Go to this part
03 | QualitativeThe DepthThe why beneath the data. Twenty-five faces, in their own words, on video and voice.Go to this part
04 | H3™ methodThe FusionWhere the three lenses stop being separate decks and fuse into one answer.Go to this part
05 | Cultural adaptationThe ResultWhere the answer turns into a launch: product, message and campaign, reshaped for how Spain feels about money.Go to this part

Lens 01 | Quantitative

We asked 1,200 people. Then we questioned their answers.

A representative survey across Spain, read with the soft launch’s usage data, gave us the shape of the opportunity and one figure that should have stopped a feature roadmap cold.

Hover or tap any person to hear them

73in100

In Spain, 73% of the people we surveyed wouldn’t trust a single app with their whole financial life, against 41% in Germany, the client’s home market. That isn’t a niche: it’s the majority of the market we were trying to win.

The same survey | Step by step

Four numbers, in the order they landed.

That 73% was the last number to reach us. Scroll through the four in the order they arrived, and watch the case for a quick launch fall apart.

Quantitative Interactive survey explorer

Method

Online panel across Spain, n = 1,200, weighted to the 18 to 55 national profile.

17%

27%

29%

27%

18 to 2425 to 3435 to 4445 to 55

1,200 people across Spain, weighted to the national profile.

Simulated study | n = 1,200 | Spain, ages 18 to 55

Simplified for the story. A real survey goes far deeper than four numbers: dozens of questions, segments and cross-tabs.

Step 01 | The sample

1,200

people across Spain, ages 18 to 55, weighted to the national profile. That’s enough to trust the patterns, but not to explain them.

Step 02 | The appetite

41%

of the 1,200 in Spain put help with saving at the top of what they want from a finance app. On a pitch deck, this is the slide that tends to get a launch approved.

Step 03 | The gap

19%

of the same people actually pay for any finance app today. Saying you want help costs nothing, and the gap between these two numbers is where launches can fail.

Step 04 | The wall

73%

in Spain don’t trust a single app with their whole financial life, against 41% in Germany. A number like that should put a feature roadmap on hold until someone understands it.

Simulated survey of 1,200 people in Spain, ages 18 to 55, weighted to the national profile
Survey question	Finding	Value
Sample	Adults across Spain, ages 18 to 55	1,200 people
Which features matter most to you in a finance app?	Rank help with saving as most important	41%
Do you currently pay for any finance app?	Currently pay for any finance app	19%
Do you trust one app with your entire financial life?	Do not trust a single app	73%

Lens 03 | Qualitative

Then we sat with 25 of them. Face to face.

Twenty-five in-depth sessions, with people walking us through the finance apps they already live with. By the third, the wall had a texture: shame, and the dignity people build to hide it.

Participant during an in-depth interview

In-depth interview UX Research session

Illustrative Daniel, 33 | Madrid

0:00 / 48:36 Money, growing up

Full transcript Auto scrolling with playback

Money, growing up

The apps he opens

What he hides

Help, not a verdict

A bank, not an ally

What it must feel like

Simulated in-depth interview | reconstructed for illustration | one of 25 sessions

Javier, 41 | Valencia Voice note | UX Research session

Illustrative Distrust

0:00 / 4:00

“

I hide my account from my own partner. Why would I open it to an app?

Javier, 41 | Valencia

Money is private

The door he keeps shut

Apps that want it all

What it would take

Maybe, slowly

Simulated voice note | reconstructed for illustration | one of 25 sessions

The language of money

The words they used, once we coded them.

How often each idea came up across 25 transcripts, coded by meaning: how the apps made people feel, and what they asked for instead.

How the apps made them feel

What they asked for instead

Counted by meaning, not just the word. Hover or tap any word for the voices behind it.

*In the client’s home-market transcripts, the same blunt tone was coded as klar and ehrlich, clear and honest. Only in Spain did those words curdle into shame.

The emotional journey

Where the wall actually goes up.

Shame shows up in two specific moments. We mapped the emotional line across a normal month of use, and the same dip appeared in session after session.

1 / 6

*Mapped in the home market, the same emotional line barely moves: participants in Berlin took those moments as useful honesty.

The client’s app | First contact

Same screen. Opposite reaction.

We showed the same alert from the client’s app to a participant in Madrid and one in Berlin. The pixels were identical, and the whole market entry sits in the gap between their reactions.

The one screen they both saw

9:41client app | spending alert

−€240

You overspent this month

Lands as the exact same alert

The full Spanish reaction, coded live

Drew them in

a calm first screen no numbers shouting at me it asked, it did not tell

Left them cold

another dashboard charts I will not read

Made them tense

connect all accounts, day one a red overspend banner

What they wished for

let me start small talk to me like a person private by default

04 | The fusion

Three lenses, one answer.

A survey of 1,200 people and the soft launch found where trust breaks, 84,000 signals measured the feeling and 25 conversations explained why. Alone, each is a clue; together, they’re evidence.

Lens 01 | Quantitative n = 1,200 | soft launch

The scale. In the soft launch, cancellations concentrate in one moment: the week the spending alert goes out.

Lens 03 | Qualitative 25 sessions

The reason. The alert reads as a verdict on the person, and trust leaves with it.

H3™

Three lenses caught the same moment from three sides. What follows is the fusion.

H3™ triangulation

Where the numbers, the noise and the room agree.

Every method has a blind spot. The survey shows where the damage happens, the listening how loud it feels, the interviews why. Tap each lens to read what it captured, then fuse them.

How to read this

Three lenses, one question

Each lens caught a different face of the same moment. Tap each one to read its finding, then tap H3™ to fuse them.

Lens 01 | Quantitative | n = 1,200

The where

Cancellations cluster in a single week, the one that follows the first spending alert. The soft-launch data locates the break with precision.

62% of soft-launch users who cancelled left within days of an overspend alert.

The how loud

The conversation runs hot: half of it is negative, six in ten posts carry an emotional charge, and the sharpest spikes gather around alerts and the feeling of being watched.

Lens 03 | Qualitative | 25 sessions

The why

In the room, the alert reads as a verdict on the person. Several participants hide their balance from their own partners and brace for any app that might scold them. What they’re protecting is their dignity.

“It’s just a bank telling me I failed. Again.”

H3™ | the three lenses agree

One instruction

Trust breaks at the spending alert, and the remedy is emotional: the product has to feel like help without judgement, starting with the alert that announces a bad month.

The where, the how loud and the why land on the same moment, and that agreement is the evidence.

Fusion, not combination

Drop any one lens and the answer breaks.

Here’s the uncomfortable test we run on our own work: read the same project with one lens, then two, then all three. Only the third turns it into an instruction you can defend in a boardroom.

+ +

Reading 1 of 3 | Quantitative only

Redesign the alert screen

In the soft launch, cancellations spike the week the overspend alert lands. Read alone, the data points at the interface: soften the red, reword the banner, ship it.

A patch on the wrong layer

Reading 2 of 3 | Quant + social

Something emotional is going on

The listening adds temperature: the spike is wrapped in anger, sarcasm and shame, so the trouble sits deeper than a screen. The team now knows something real, but still has nothing to build.

Closer, but no instruction

Reading 3 of 3 | The full H3™

Take the judgement out, everywhere

The rooms name what the numbers and the noise could only point at: the alert reads as a verdict, and dignity decides. Now the fix is concrete: rebuild the moments that judge, starting with the alert, and let trust arrive slowly, in private.

The instruction

1 / 3

H3™ | The answer

The Berlin playbook, rewritten for Madrid.

The product that wins at home was built on bluntness, full visibility and control. The version that wins in Madrid leads with calm, asks for less on day one and keeps a person’s numbers private.

Arrived with | the Berlin playbook

Respect, said bluntly

Open with the numbers, all of them.

Connect every account on day one.

Say it straight, in red if it has to be.

Leaves with | the Madrid rewrite

Help, in a low voice

Open with calm, one step at a time.

Earn access slowly, one account first.

Say it kindly, and say it in private.

Strip away the privacy talk and you reach what people are really guarding: their dignity. They keep their money out of sight because the numbers feel like proof they’ve failed, and they brace for any app that might say it to their face. So the fix is emotional: every screen has to earn its way in by feeling like quiet help with the judgement taken out, starting with the alert that announces a bad month. That alert doesn’t travel well. In Berlin, bluntness reads as respect, a product that takes you seriously. Carry the same words to Madrid, where many people already feel their banks are keeping score, and they land like a verdict.

The fused answer | H3™

05 | The result

An answer is worth what you build with it.

Research earns its keep in the weeks after the report lands. Here, four findings become four decisions. The numbers are illustrative, but the logic is the one we use on real projects.

What changed

The alert became quiet help

The overspend banner was redrawn from scratch: calm colour, plain words, and the way out placed right next to the number.

BeforeA red banner. Minus 240 euros, you overspent this month.

AfterA calm line. 60 euros left and four days to go, you’re on track.

What changed

Onboarding starts small

The first session connects a single account. The rest get invited later, once the product has earned it.

BeforeConnect every account at signup.

AfterOne account to start, the rest when trust has grown.

What changed

Private by default

Discreet mode ships switched on. Balances hide with one tap, and privacy leads the launch campaign.

BeforeBalances visible on the home screen.

AfterNumbers stay hidden until you choose to show them.

What changed

The voice was rewritten

Every message in the money journey was rewritten in a person’s tone, the overspend message first.

BeforeYou have exceeded your monthly budget.

AfterA tight month happens. Here’s where it went.

The adaptation

Same company, a screen that finally fit.

Traceability is the luxury of good research: every change in this entry strategy points back to something a person actually said. Flip the screen to compare the Berlin and Madrid versions.

9:41client app | spending alert

−€240

You overspent this month

Budget exceeded. Review your spending.

€60 left

4 days to go, you’re on track

A tight month happens. Here’s where it went.

Tap the screen or the toggle

Shipped from Berlin | the default

Blunt, the Berlin way

The minus sign leads the screen.

You overspent, delivered flat.

The screen ends at the bad news.

Shipped for Madrid | the adaptation

Quiet help, the Madrid way

What remains leads, 60 euros still yours.

On track, said like a person would.

The way out sits next to the number.

The payoff

What getting the culture right was worth.

We put both versions in front of the same Spanish panel. The figures are illustrative, and each one moved for a reason a person told us.

Both versions tested on the same Spanish panel | modelled launch figures

First week

Activated in the first week

The Berlin default, tested in Spain

34%

The adaptation, same panel

61%

Why it moved | from the study

The default opens by asking to connect everything, the exact wall the interviews kept hitting. The adaptation starts with a single account, so far more people simply make it inside.

Stayed past the first overspend alert

The Berlin default, tested in Spain

58%

The adaptation, same panel

88%

Why it moved | from the study

In testing, the old alert was the single biggest exit, the moment people described as a verdict. Redrawn as quiet help with the way out next to the number, it keeps nine people in ten past the hardest moment of the month.

Would recommend it, unprompted

The Berlin default, tested in Spain

29%

The adaptation, same panel

57%

Why it moved | from the study

People talk about what makes them feel steady. With privacy leading and a voice that speaks like a person, the product became safe to bring up over dinner.

Still active at day 90

The Berlin default, tested in Spain

23%

The adaptation, same panel

47%

Why it moved | from the study

The rooms were clear that trust in money apps grows slowly. A product that earns access step by step is still around when month three arrives.

A market entry is a bet.

We made it a smaller one.

Four numbers, one pattern: the product stopped bruising people at the moments that matter. This launch was a simulation, built so the method could show its work.

The bets on your desk are real.

1 / 5

Straight answers

Questions we get about data storytelling

Something else on your mind? Ask us directly. You’ll get an answer from the people who would run the work.

What is data storytelling in market research?

It’s the work of turning research evidence into a narrative a decision-maker can follow and act on: what we found, why it matters and what to do about it. Charts and dashboards are part of it, and each one has to earn its place by moving the reader closer to the decision at the end.

Is this a real client case?

No. The company, the figures and the people on this page are illustrative, built to show how the H3™ method works from brief to launch. The method, the deliverables and the way we reason are the ones we use with clients, whose data we never publish.

Why combine a survey, social listening and interviews?

Each method has a blind spot. A survey sizes a problem without explaining it, social listening shows how people talk when nobody is asking, and interviews reveal the reasons behind both. When the three point at the same moment, a recommendation becomes evidence you can defend in front of a board.

What do we receive at the end of a study like this?

The H3™ Intelligence Report and the formats your teams need to act on it: navigable PDFs, dashboards, workshops, documentaries and a private hub that keeps every file of the study in one place. You can see all nine formats, and what each one is for, on the Reporting Ecosystem page.

How long does a market entry study like this take?

A study like this usually runs between six and twelve weeks, from the research design to the session where we make sense of the findings with your team. Every project is scoped on its own terms, and the proposal you receive shows the timeline before anything starts.

Bring us the market you’re staring at.

Tell us which market entry you’re weighing, and we’ll tell you what we’d test, how long it would take and whether H3™ fits your bet.

You get in touchBy email, through the form or on a video call, whichever suits you.
We ask for the briefEvery detail we need, so the proposal is shaped around what you actually require.
We design the proposalThe approach that answers your objectives, with the timeline and the budget.
Approval, and we beginYou sign off, the paperwork is settled, and the work starts.

Start a conversation

Senior researchers only. No juniors.
End to end. Design, fieldwork, reporting.
No AI automation. Any use, disclosed.

Data storytelling | A simulated case study 1,200 voices. 84,000 conversations. 25 faces. One clear move.

A Berlin fintech. A market it had not read yet.

One question, seen through three lenses.

Four numbers, in the order they landed.

The conversation, unfiltered

In Berlin people argue about features. In Spain, about comisiones and trust.

Sentiment, volume and reach

The words they used, once we coded them.

Where the wall actually goes up.

Same screen. Opposite reaction.

Where the numbers, the noise and the room agree.

Drop any one lens and the answer breaks.

The Berlin playbook, rewritten for Madrid.

Same company, a screen that finally fit.

What getting the culture right was worth.

Questions we get about data storytelling

Bring us the market you’re staring at.