What is an insight, and why most reports do not contain one

The quarterly deck that nobody reads

Every quarter, the five houses of Seeblick Hotels send their figures to head office in Bad Ischl, and every quarter a 38 page PDF comes back. Occupancy by house. Average daily rate by month. Revenue by channel. Guest satisfaction by question. Twelve slides on the loyalty programme. It is accurate, it is on time, and it is beautifully formatted. Seeblick is a fictional small hotel group in the Salzkammergut and the numbers in this chapter are illustrative, but I have seen this deck, in slightly different fonts, at a dozen companies.

The managing director told me she reads the first three pages and then skims the rest looking for anything red. Her hotel managers do not open it at all. One of them, quite reasonably, said: "It tells me what happened. I was there. I know what happened."

That sentence is the whole chapter. The report contained several hundred facts and not one insight, and the difference between the two is what the reader can do next. Most analytics output fails at exactly this point: it stops at the finding. This chapter is about the last step, the one that turns a number into a decision, and about why so few teams take it.

Where this sits in the series

This is the third and last chapter of part one of the series, how analytics helps. In the previous chapter, on consumer behaviour as the basis of marketing strategy, I argued that the behaviour of real customers, not the channel report, is the raw material of strategy. Here I close part one by asking what all that analysis is for, and the answer is not a chart. From the next chapter, on modelling demand and price elasticity with regression, we move into part two, dependent variable techniques, where the modelling proper begins.

Reporting, analysis and insight are three different jobs

Reporting answers the question "what happened". Occupancy was 78 percent. Direct booking share was 41 percent. Reporting is necessary, it should be automated, and it should be boring.

Analysis answers "why did it happen". It cuts the number by house, by device, by guest type, by month, and looks for the place where the change is concentrated. But analysis on its own still leaves the reader with a shrug. You now know why direct share fell. Fine. What would you like me to do about it?

Insight generation answers "so what do we do, and what is it worth". This is the step almost everyone skips, because it requires the analyst to leave the safe ground of describing the data and take a position on the business.

My working definition: a finding is a true statement about the data. An insight is a finding that has been attached to a decision, priced, and had its risk stated out loud. If any of those three parts is missing, it is still a finding, however clever it is.

An insight has to pass four tests, in this order, because the last one matters most:

1. New. The reader did not already know it. "Weekends are busier than midweek" fails this test at every hotel on earth.
2. Non obvious. It could not have been guessed from the surface of the numbers. It usually comes from a cut, a comparison or a model, not from the headline.
3. Explains why. It names a mechanism, not a correlation. "Guests who use the spa spend more" is a correlation. "Guests who book the spa at check in stay longer because the spa slot anchors their departure day" is a mechanism, and it can be wrong, which is exactly what makes it useful.
4. Actionable. It names a decision that someone in the room has the authority to take, with a size and a risk attached.

Three out of four is trivia. The fourth test is the one that separates an analytics function from a very expensive newsletter, and I wrote about that mindset in Start with the decision, not the dashboard.

Pricing the decision

If an insight must name a decision, it must also say what the decision is worth, and that needs one small piece of maths. The expected value of a decision is the probability weighted sum of what happens if you take it:

$$EV(d) = \sum_{i=1}^{n} p_i \, v_i$$

Here $d$ is the decision, $i$ runs over the $n$ outcomes you think are plausible, $p_i$ is the probability you assign to outcome $i$, and $v_i$ is the value of that outcome in money, usually per year. The probabilities must sum to one. They are judgements, not measurements, and the honest way to present them is as judgements with a range.

The expected value on its own is not the number that matters. The number that matters is how much better off you are than if you did nothing, after paying for the change:

$$\Delta EV = EV(\text{act}) - EV(\text{status quo}) - C$$

$EV(\text{act})$ is the expected value if you take the decision, $EV(\text{status quo})$ is the expected value if you carry on as you are, and $C$ is the cost of making the change, including the people whose time it takes. If $\Delta EV$ is positive, the decision is worth taking on expectation, though a small positive number with a large downside still deserves the risk line the brief carries later in this chapter. I covered the fuller version of this, including what it is worth to buy more information before deciding, in The three questions your data should answer.

One more formula. Candidate insights are scored on the four tests plus the size of the prize, with actionability weighted double:

$$S = N + O + W + 2A + P$$

where $N$ is newness (0 to 2), $O$ is non obviousness (0 to 2), $W$ is whether it explains why (0 to 2), $A$ is actionability (0 to 3, counted twice) and $P$ is the size of the prize (0 to 3), for a maximum of 15. Anything below 8 goes in the appendix. Anything with $A$ of zero goes nowhere, regardless of the total.

The same numbers, written three times

Back to Seeblick. Page 14 of the deck contains a perfectly good finding.

Version one, the finding. "Direct booking share fell from 44 percent to 41 percent in Q3 compared with the same quarter last year. Platform bookings rose correspondingly."

True. Formatted nicely. Ignored by everyone, because nobody knows what to do with it. Three points could be seasonal mix, a platform promotion, or noise.

Version two, the analysis. Cut by house, device and guest type, the story gets specific. The fall is entirely concentrated in two houses, Seeblick am Wolfgangsee and Seeblick Traunsee, which moved to the group's new booking engine in June. In those two houses the mobile completion rate of the direct booking flow dropped from 3.1 percent to 1.9 percent after the new date and room selection step went live. Desktop completion did not move. The other three houses, still on the old engine, are flat year on year. The guests being lost are mostly returning guests, whose direct share fell from 70 percent to 61 percent: they start on the hotel site on their phone, give up at the new step, and finish on a platform app that already knows their card details.

Now we know why. That is analysis, and still nobody has to do anything.

Version three, the insight. "Fix the mobile date and room step on the new booking engine at Wolfgangsee and Traunsee, and give returning guests a stored profile plus a direct rate reminder three weeks before their usual rebooking window. Group room revenue is around 9.6 million euros a year, so three points of share is 288,000 euros routed through platforms at 15 percent commission, or about 43,000 euros a year in commission, plus the guest relationship. Expected recovery is about 25,000 euros a year against a cost of about 9,000 euros, so the decision is worth roughly 16,000 euros in year one and the full amount in every year after. The main risk is that the shift is partly a mix effect; the share of first time guests is flat year on year, so I do not think it is, but we will know within one quarter."

That last paragraph is what an insight looks like. A decision, an owner (the group's web team), a prize with its working shown, a probability weighted expectation, a cost, a risk, and a date when we will know if we were wrong. Here is the working, because an insight that hides its arithmetic asks to be trusted rather than checked.

Outcome	Probability	Commission saved per year	Weighted value
Full recovery of the three points	0.45	43,200 euros	19,440 euros
Partial recovery of one point	0.40	14,400 euros	5,760 euros
No measurable effect	0.15	0 euros	0 euros
Expected value of acting	1.00		25,200 euros
Cost of the change			9,000 euros
Value of the decision, year one			16,200 euros

Read it line by line. The first row says that if the fix does what the funnel data suggests, the group stops paying 15 percent commission on 288,000 euros of bookings, which is 43,200 euros, and I give that slightly less than an even chance because booking engines are never fixed as quickly as the vendor promises. The second row is the realistic partial outcome: the step is fixed but the returning guests have already formed the platform habit, so only one point comes back. The third row is honest failure, and I have never written a brief without one. Sum the weighted column, subtract the cost, and you have the number that goes at the top of the brief. The worst case is minus 9,000 euros, which the managing director can live with, and that sentence belongs in the brief too.

The 9.6 million euros, the 15 percent and the probabilities are illustrative. The structure is not.

Scoring five candidate insights

The same deck yielded five statements that people in the room wanted to call insights, scored here against the rubric from the formula above.

Candidate	New	Non obvious	Explains why	Actionable (x2)	Prize	Score
Direct booking share fell three points	0	0	0	0	1	1
Mobile abandonment at the new date step explains the lost direct share at two houses	2	2	2	3	2	14
Returning guests drift to platforms because the platform remembers them and we do not	1	2	2	2	2	11
Weekend occupancy is higher than midweek	0	0	0	0	0	0
Guests who use the spa spend 40 percent more per stay	1	1	0	1	1	5

The first row is the finding from the deck. It scores one point, for the prize, because there is money in it somewhere, and fails every other test: everyone knew it, it has no mechanism and it names no decision.

The second row is the insight we ended up writing. It scores 14 of 15. It loses a point on prize only because 43,000 euros a year is real money but not transformational at 9.6 million euros of room revenue.

The third row, the companion insight about returning guests, is slightly less new because the general manager had suspected it, and slightly less actionable because stored profiles are a bigger project than fixing one step. It still clears the bar and goes into the same brief as a second phase.

The fourth row is there because it appeared in the deck, in colour, on page 9, every quarter for three years.

The fifth row looks like an insight and is not. Spa users spend more. Of course they do: they bought a spa treatment. Without a mechanism the statement cannot tell you whether promoting the spa would raise spend or merely relabel the guests who were going to spend anyway. I wrote about this trap in Is your dashboard lying to you?. Score five, appendix, and a note that a proper test might promote it.

From data to decision

The path from a database to a changed decision has more gates than most teams draw, and the gates are where the value is created.

Every finding meets two gates before it becomes an insight. Most belong in the appendix, and that is fine. When you cannot price a candidate, you go back and find the missing number; you do not write a vaguer brief.

The second chart is the reason executives ignore analytics output, drawn as plainly as I can.

Seeblick over four quarters: pages of reporting produced (bars) against decisions the management team could trace back to a report (line). Illustrative, but I have rarely seen the line above five.

Forty pages a quarter and, on a good quarter, three decisions. The ratio is what happens when nobody is asked to take the fourth step. The fix is not fewer pages. It is a different first question.

The one page insight brief

Everything an executive needs from an insight fits on one page, and if it does not fit, the insight is not finished. Here is the template I use.

INSIGHT BRIEF                                   Date / Author / Version

DECISION      One sentence. Who does what, by when.
EVIDENCE      Three to five bullet points. Each one a number with its source.
PRIZE         Expected value per year, with the working. Best and worst case.
RISK          What would make us wrong, how we would notice, and how soon.
COST          Money and people time, including the people who will resist it.
OWNER         One name. Not a department.
CHECKPOINT    The date we look at the result, and the number we look at.

The decision comes first because the reader should know within five seconds what they are being asked to do, and the risk sits in the middle rather than in a footnote, because an executive who has been given the risk in plain words will trust the prize, and one who has not will halve it anyway.

The habit that makes all of this work is starting with the decision. Not "what does the data say" but "what decisions are on the table in the next quarter, and which number would change each of them". If you start from the data instead, you will generate fifty findings and then hunt for a decision to attach to each one, which is how you end up with the spa row.

The hamper shop version

A second, smaller example from my own demo shop, The Gift Bow.

The finding, straight from the Solidus admin: corporate orders are 22 percent of orders but 38 percent of revenue in the fourth quarter. Interesting, known, and it names no decision.

The analysis: 61 percent of last year's corporate buyers ordered again this year, and those who did placed their order between 28 November and 8 December. By 10 December, 39 percent of last year's corporate accounts had not ordered. Last year's corporate revenue was 68,000 pounds, so about 26,500 pounds of it was, at that point, quietly lapsing.

The insight: on 27 November, send every corporate account from last year a short personal email from the owner with last year's order prefilled and a one click reorder link. If the reminder recovers a quarter of the accounts that would otherwise lapse, that is around 6,600 pounds of revenue for an afternoon's work, and the downside is a few unsubscribes. Owner: whoever runs the shop. Checkpoint: 12 December, compare the reorder rate of emailed accounts against the previous year's 61 percent.

A decision, a prize with its arithmetic, a risk and a date, in one paragraph. The numbers are illustrative; the timing pattern is one I would bet on in any gifting business.

Running an insight review

The insight review is a thirty minute meeting once a month, and it replaces the reporting walkthrough.

Before the meeting, the management team lists the decisions that are actually due in the next ninety days. Pricing for next season. Whether to renew the platform contract. Five to ten items. For each one, someone writes down the number that would change it. This step alone usually kills half the standing reports.

The analysts then bring candidate insights, each scored on the rubric, each on one page in the brief format. Anything under 8 is not presented. It goes in a shared appendix that anyone may read and nobody must.

In the meeting, each brief gets five minutes: decision, prize, risk, questions. The outcome for each is one of three words: yes, no, or more, where "more" means a specific piece of analysis with a name and a date. Every yes gets an owner and a checkpoint before the meeting ends.

The data work behind a brief is rarely exotic. For Seeblick it was one query against the bookings table:

select h.name as house,
       b.device,
       case when g.stays_before > 0 then 'returning' else 'first time' end as guest_type,
       date_trunc('quarter', b.booked_at) as quarter,
       avg(case when b.channel = 'direct' then 1.0 else 0.0 end) as direct_share,
       count(*) as bookings
from bookings b
join houses h on h.id = b.house_id
join guests g on g.id = b.guest_id
where b.booked_at >= date '2025-07-01'
group by 1, 2, 3, 4
order by 1, 4, 2, 3;

The skill is not in the SQL but in knowing which cut to ask for, which comes from having a decision in mind first.

Before I trust a candidate enough to write the brief, I run four checks. Is the sample big enough that the shift is not noise: a share moving three points on a few thousand bookings comfortably is, a segment of two hundred bookings might not be. Is it a mix effect, meaning the population changed rather than the behaviour, which is why the first time versus returning split matters. Does the mechanism show up somewhere else in the data, here in the funnel completion rates. And would I find the reverse story equally convincing if the numbers had moved the other way, the check that catches most of my own wishful thinking. If one fails, the candidate goes back to "more".

Pitfalls

Insight theatre. The word gets applied to everything. "Key insight: revenue grew 4 percent." Once every bullet point is an insight, the executive stops reading them all, including the one that mattered. Guard the word.

Correlation wearing a mechanism's coat. The spa row again. A plausible story that has not been tested is more dangerous than no story, because it stops people looking. If the "why" has not been checked against a second cut of the data, say so in the risk line.

A prize without a probability. The headline number in most decks is the best case, presented as the expectation. "This could be worth 43,000 euros a year." It could. It is expected to be worth about 25,000 euros, and that gap decides whether the executive trusts your next brief. Always show the weighted column.

The action nobody owns. "We should improve the mobile booking experience." Who? By when? An insight that ends in the passive voice is a finding with ambitions. Put one name on it, and if nobody in the room can be that name, say whose decision it really is.

The decision that was already made. Sometimes the analysis is commissioned to justify a decision the board took last month. When that is happening, the honest answer is to say so and save everyone the fortnight. I have a longer piece on how this kills dashboard projects in particular: Why your dashboard project keeps dying on the shelf.

Waiting for certainty. The opposite failure. Because the probabilities are judgements, some teams refuse to write them down and wait for a number that never arrives. A written 0.45 that turns out wrong teaches you something. An unwritten "probably" teaches you nothing, and the season is over by the time you have argued about it.

How I do this for clients

We start with the free workshop: half a day with the people who take decisions, not the people who make the slides. We list the decisions due in the next ninety days and, for each one, the number that would change it. Most clients leave that room with a shorter list of reports than they came in with.

Then I take two weeks of real work on me, before you commit to anything. I need read access to the systems the decisions depend on: the booking engine or shop database, the analytics account, the finance export. In those two weeks I produce the first three insight briefs in the format above, scored, priced, with the risk written down, and I run the first insight review with your team so the habit has been practised once before I step back.

What you get after that, if you continue, is not a dashboard. It is a monthly set of one page briefs, each tied to a decision on your list, each with an expected value and a checkpoint date, plus one dashboard if and only if it answers a question you ask every week. You own all of it: the queries, the briefs, the scoring sheet. Where a brief needs a proper model behind it, that is the data science work covered in the rest of this series, and where the decision is a marketing one, I will happily be paid partly on the result rather than the hours through Growth Hacking Plus.

Costs are on the pricing page in plain terms: a fixed monthly amount below one analyst's salary, no long contract, and the workshop and first two weeks cost you nothing but your time.

Questions to ask your own team

Take these to the next reporting meeting. If more than two get a blank look, the reports are not producing insights.

• Which decision, due in the next ninety days, does this report change, and which number in it would change that decision?
• For the last three things we called an insight, what did we do differently afterwards, and what did it turn out to be worth?
• What is the expected value of the recommendation on this page, and what probabilities did you assume to get there?
• What would have to be true for this recommendation to be wrong, and when would we notice?
• Who owns this action, by name, and what is the date we look at the result?
• If we stopped producing this report tomorrow, who would notice, and what would they do instead?

This series is inspired by Mike Grigsby's Marketing Analytics (Kogan Page). The explanations, examples and numbers here are my own.