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🚨 ANNOUNCEMENT 🚨

In case you missed my email yesterday… Next week, I am previewing a secret screening of a pilot for a new show where I dive deep to find the truth on AI for CFOs. Part webinar, part TV pilot. Serious guests, a proper presenter, and yes, me in full 8-bit glory (I’ve even got legs).

This is a pilot, and only open to newsletter readers. So if you want to join the secret screening, it’s Wednesday, March 4th. 11am ET / 4pm GMT / 5pm CET.

If you want in, leave your details by clicking the below button, and I’ll send you the invite.

Back to the this week’s Playbook…

Today, Furner is CEO of Walmart Inc., the largest retailer on the planet (well, until Amazon overtook them last week, anyway). Back then, he was running its U.S. operation. And he'd just said something that would send shockwaves through the global food industry.

In case the point wasn't clear, he hammered it home: "We see that the people on these drugs are buying less food."

Walmart was already seeing measurable changes in food buying behavior, barely two years into the mainstream adoption of GLP-1 weight-loss drugs.

This wasn't data from a survey or a forecast. It was observational proof of a drug replacing food at the till. It showed the GLP-1 hype was real, and as uptake grew, it was going to compress the total volume of food sold. Especially the high-calorie, processed kind. Morgan Stanley would later confirm GLP-1 users were cutting grocery spending by 8-9%.

Furner knew this before the data was published. Because Walmart was the data.

The market reacted immediately… but not on Walmart. Its exposure was diversified, and the affected cohort was still tiny.

But on the companies whose entire revenue model was selling sugar and calories. Coca-Cola fell 4.8%, hitting a 52-week low. PepsiCo lost 5.2%. Nestlé fell 3.4%.

What impressed me though wasn't the market reaction. It was that Furner had this detail at his fingertips.

This is a business doing ~$650bn of revenue a year. Walmart carries around 140,000 SKUs. The GLP-1 trend was a single SKU and a tiny fraction of that revenue. It would be invisible in the financials. Negligible in margins, absent from guidance, lost in the rounding of a number that size. And Walmart had been watching that behavioral shift, in a single customer cohort, driven by a single product. They'd built the methodology to isolate it: matching customers who filled GLP-1 prescriptions at Walmart pharmacies against a comparable group who hadn't, then watching the baskets diverge.

And Furner had been watching long enough to know what this was the start of a trend, not an anomaly. (Remember, at this point, mainstream GLP-1 adoption was still brand new.)

A customer segment with a structurally different purchasing profile, quietly growing inside the aggregate numbers. A lazier analyst would look at the top line and see nothing.

But it was there. In the units. In the mix. At an individual basket level.

Someone in the bowels of the data science team at Walmart had the discipline to go hunting for a nugget of insight that would move markets by hundreds of billions.

In week one, we broke down why you should never start your story with data (despite what the gurus tell you).

In week two, we stepped into the world of television and WWE to understand how to structure narrative arcs for different stakeholders.

And last week, we dissected the anatomy of a story itself, how to build causality and pace into it, and how to use that structure in the boardroom.

So… after many of you called heresy on my suggestion that CFOs should abandon data when telling stories, don’t worry. We’re coming home. Back to a safe space. Back to the numbers.

Think of it like listening to God Only Knows by the Beach Boys for the first time. The verse wanders into strange territory. Brian Wilson stacks these unusual chord progressions underneath lyrics that feel unresolved: “I may not always love you…” You don’t quite know where it’s going, it feels unsettling.

And then the chorus lands: “God only knows what I’d be without you,” on top of the tonic A chord. And everything feels right with the world.

This week, we will be diving into how to make sure you pull the correct story out of the data rubble.

One of the hardest parts of financial storytelling is knowing what level of detail to speak at.

Most teams get this gloriously wrong. They fall into three traps:

Trap 1: The Executive Summary Trap

This is where analysis stays top-down and never goes any further than the surface. The “story” just parrots the variances.

You know the type:

“What,” not “why.”

We’ve all read board packs like this. And if we’re being honest, we’ve all probably written one at some point.

The real story gets lost because the aggregate numbers are a blender: offsetting impacts net against each other, issues cross P&L lines, and half the drivers cancel out deep in the bowels of the business.

But as we saw in the Walmart example, just because you can’t see it from the top floor doesn’t mean it's not important.

Valuable insight doesn’t turn up wearing a name badge. You have to go find it.

Trap 2: The Wallpaper Trap

Then there’s the opposite sin: bottom-up analysis that turns into wallpaper.

Pages and pages of bridges. Dashboards. Heatmaps. Variance charts. By region, by P&L line, by category, by product, by channel, by whatever.

The actionable answer is in there somewhere. But good luck finding it.

And if someone does find it, they didn’t need you, they needed a search bar and a free afternoon. The real story gets lost in the noise of mass disaggregation.

In reality, most high stakes storytelling commits Trap 1 and Trap 2 at the same time: a thin summary up front, then 40 pages of appendix detail behind it, with one “compensating” for the other.

What that really means is you’ve pushed the hard work onto the reader. And you’ve given every reader permission to invent their own story.

Lazy. Lazy. Lazy.

Trap 3: No-mans land

Now, you are probably expecting me to say the right answer is to land on a level of detail somewhere in the middle.

Using the top-down approach, but pushing a layer or two deeper.

So it’s no longer “gross margin missed by 200bps.”

It’s “Gross margin missed by 200bps, driven by a 500bps miss in the domestic market.”

Or:

“…driven by this product category.”

It sounds better. And it is a little better. But it still falls well short of the why.

The right approach uses the depth of Trap 2… AND the context of Trap 1.

You go all the way down to shop-floor reality, deep enough to find the small number of things that actually matter. The stuff that really moved the outcome. The truth, the whole truth, and nothing but the truth.

Then you bring those truths back up to the top and rebuild the narrative around them.

I call this drilling for oil.

You don’t stop at “gross margin pressure” or “domestic market,” or “product line X.”

You keep going until you hit something real. Until you hit oil….

You might ask, do you really want to be bringing your “oil” into the boardroom? Do they really want that messy, operational detail?

Yes. They absolutely do.

So often, the valuable insights from the shop floor get lost in the messy middle of the business, lost in politics, poor systems, or competing narratives.

When building a financial story, I’ve always tried to follow the Rule of 3.

Find the three pieces of oil that matter most. Build the story around those three things.

Yes, sometimes it’s four. Occasionally five. But honestly, the rule of three exists for a reason.

Three just works. Goldilocks and the Three Bears. Snap, Crackle, Pop. Three-act plays. 3 pricing tiers. Rule of thirds. Not two. Not four. Three.

Psychologists have studied this extensively. People find information presented in threes more satisfying, more persuasive, and easier to remember. It’s the smallest number that creates a pattern, and patterns are how humans make sense of complexity.

I use this religiously as a CFO.

My board update was always ten pages. Seven pages followed a fixed, predictable format: the core financials, the operating metrics, and the standard reporting.

But there were always three pages in the middle reserved for the oil. And you bet it's where we’d spend 90% of the time each meeting.

One page for each of the three things that actually mattered that month. The three causal drivers that explained the performance of the business.

It started to set the rhythm for everything.

The board discussion. The exec team discussion. The internal operating cadence.

The business learned that if something became one of those three things, it was about to receive a serious amount of attention.

If you’ve got kids, you’ll know they all go through their “why” stage, usually around two or three.

It turns out that kid is doing better root cause analysis than half the finance function.

The “5 Whys” is undefeated. Anyone who’s seen lean manufacturing or Six Sigma up close will recognize it immediately. They’re relentless. They don’t stop at the first explanation. Or the second.

They keep going until they hit something fundamental.

Here is an example straight out of that Playbook:

This is so important because until you know the root cause, you don’t actually know what you are looking at.

Finance teams often assume variances show up cleanly and independently. That the gross margin variance lives in gross margin. The revenue variance lives in revenue.

They don’t.

A single operational issue can cascade across the entire P&L. It can hit margin, suppress growth, and drive secondary costs as the business reacts to it. Until you isolate the underlying mechanism, you don’t know how big the thing really is.

I remember one month, we were digging into three specific variances that accounted for roughly 80% of the total EBIT miss.

We’d already done what most teams would consider a thorough job. We’d isolated the affected products, channels, and regions. On the surface, they looked like separate issues. Different parts of the business with different explanations.

But we kept asking why.

And eventually, the threads converged.

All three variances traced back to a single operational issue.

It was a known issue, but the true extent of its impact had been hiding beneath the surface. Its effects were scattered across the P&L. A bit in gross margin here, a bit in revenue there, some secondary effects in OpEx, additional inventory tied up. Spread across different locations. Individually, none of those impacts looked existential. Together, they were enormous.

That single issue accounted for over 200% of the cashflow variance that quarter.

It completely reframed my three pieces of oil. What had looked like multiple unrelated pressures was actually one catastrophic (but well hidden) operational failure, partially offset by good fortune and mitigation elsewhere in the business. A totally different story for the business.

Once we saw it clearly, the response changed immediately. We reframed the problem, traced it to a specific part of the supply chain team, and put our heaviest resources into fixing it.

How you frame the performance story as a CFO matters tremendously.

Let’s dive further into an example. Imagine you are the CFO of a global manufacturer, staring at a terrible P&L for the last month.

The board is jumping up and down and waiting for you to explain what the hell happened and what you are doing about it. Here’s an example of how you might tell the story.

“EBITDA for the month came in $22m below plan, driven by:

The primary supplier of Resin PX-417, a critical input into our injection molding line at the Vietnam plant, suffered a factory fire and declared force majeure.

As a result, we were forced into the spot market at prices 38% above contract levels, driving a $19m raw material cost impact during the month. Secondary effects added a further $9m of impact across the P&L, including higher scrap rates as we adapted to new supply, increased logistics costs from expedited shipments, and additional labor overtime.

We have increased inventory levels to protect supply continuity. However, current spot prices remain approximately 50% above expected cost, resulting in an expected ongoing impact of approximately $25m versus plan over the next three months. This estimate assumes spot prices persist at current levels and supply recovery follows the supplier’s communicated restart timeline.

Separately, execution of the North America price increase program is progressing more slowly than planned, particularly within the Automotive division, where supply conditions remain soft. This reduced realized pricing and resulted in a $9m EBITDA impact during the month. We expect a similar monthly impact over the next two months before full implementation.

These headwinds were partially offset by stronger shipment volumes, contributing a $15m EBITDA benefit. Analysis indicates this is primarily timing-related, with customers accelerating purchases ahead of price increases and supply uncertainty. This effectively pulls forward demand from future periods rather than representing structural improvement.

In total, we missed plan by $22m for the month and currently face a further $100m+ of EBITDA risk over the next three to six months, depending on spot price normalization and supplier recovery timing.

Mitigation actions are underway, with $75m of EBITDA recovery identified across four initiatives:

This leaves an estimated $50m gap versus plan. We will generate a plan to close this gap within two weeks and report back.”

This is a practical example of distilling a complex P&L into a simple but deeply causal story:

The story itself fits on a single page. But the work behind it is immense.

Not just the drilling required to find the oil and connect it back to the P&L, in many ways, that’s the straightforward part. The harder work is quantifying the ongoing impact, projecting how it evolves, and aligning the business around a mitigation plan with clear ownership and accountability.

You wouldn't make a commitment to a recovery plan like this public without a shared story and ownership behind it. It’s the moments like this where CFOs earn their money.

One of the challenges in turning numbers into a story is that not all dollars are equal. They may look identical in the P&L, but:

So the precise definition of that variance today, will say something about the future cash generating capacity of the business. And therefore its enterprise value.

Just because something was the largest effect in the last reported period doesn’t mean it’s the most important or interesting thing. Walmart understood this when they took their insight on GLP-1 drugs into the public domain. It wasn’t about what was happening today; it was about what it suggested about the future.

You need to get forensic about this. How you classify variances shapes the story you tell and the actions that follow. And how you define truly what the ‘oil’ is in the story

Here are some different ways you can classify variances (and your response):"

You can’t find the right data for your story sitting behind your desk. No matter how good your dashboard is. No matter how senior your audience.

You have to go into the business. You have to drill down until you find the oil - the true root cause - and quantify its impact across the P&L.

Every high-stakes audience (boards, lenders, investors, etc.) loves hearing operational details. Not because they want more information, but because it gives them confidence. It shows you understand what’s really happening beneath the surface. It brings the numbers to life.

But this only works if it’s done with discipline. Analytical rigor to find the truth. Storytelling clarity to explain it.

Combine the two, and you get exactly what we set out to achieve at the start of this series:

You move your audience to take action.

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Disclaimer: I am not your accountant, tax advisor, lawyer, CFO, director, or friend. Well, maybe I’m your friend, but I am not any of those other things. Everything I publish represents my opinions only, not advice. Running the finances for a company is serious business, and you should take the proper advice you need.