Making recommendations

Why it matters

Junior analysts report findings. Mid-level analysts make recommendations: they say what the organisation should do, why, what it will cost, and how confident they are. This is uncomfortable at first, because recommendations can be wrong. But stakeholders hired you to reduce uncertainty in their decisions, and a list of "interesting findings" pushes the hard part back to them. A good recommendation is also honest about its limits, which is what makes people trust the next one.

The structure: answer first

Use a top-down, "answer first" structure (often called the pyramid principle, from Barbara Minto):

1. Recommendation: one sentence. "Shift 30% of the paid social budget to search next quarter."
2. Why: two or three supporting points, each with its key number.
3. Evidence: charts and details behind each point, available but not leading.
4. Caveats and risks: what could make this wrong.
5. Options considered: including doing nothing.
6. The ask and next step: the decision you need, from whom, by when.

Busy readers who stop after line 1 or 2 still get the message.

Worked example

Recommendation: Move 30% of the paid social budget (about $45k per quarter) to paid search, starting next quarter, and review after eight weeks.

Why:

1. Search brings paying customers at $38 each vs $71 from social (last two quarters, last-click attribution).
2. Search customers keep paying longer: 62% vs 48% still subscribed after 90 days.
3. Search volume is not saturated: our impression share is 41%, so extra budget can buy more clicks at similar cost.

Caveats: Last-click attribution undercounts social's role earlier in the journey, so true social value is higher than shown. Search costs per click may rise as we spend more. We do not have a controlled test yet.

Options: (a) keep the split (no risk, no gain); (b) move 30% now (recommended); (c) run a four-week geographic holdout test first (slower, more certain).

Ask: Approve option (b) with an eight-week review, or option (c) if you prefer certainty over speed. Decision needed by the 28th for next quarter's budget.

Notice that the caveats are specific, and the options let the decision-maker choose their own risk level.

Quantify impact and confidence

• Translate metrics into the organisation's language: money, time, customers, risk. "+1.5 points conversion" becomes "about 230 extra orders a month".
• Give ranges: "saves $20k to $35k per quarter".
• State confidence plainly: high (controlled experiment, large sample), medium (consistent observational evidence), low (early signal, small sample). Explain why.
• Separate what the data shows (association) from what you infer (cause), unless you have an experiment.

Anticipate questions

Before presenting, list the five hardest questions a sceptic would ask and prepare short answers:

• "How do you know it is not seasonal?"
• "What happens if competitors respond?"
• "What does the finance team's number say?"
• "What would make you change your mind?"
• "What does it cost to be wrong?"

Talk to key stakeholders before the meeting. Nobody likes being surprised by a recommendation in front of their boss, and early feedback catches gaps.

When the data does not support what people hope

Sometimes your analysis contradicts a leader's favourite idea. Stay factual and calm: show the evidence, acknowledge what you might be missing, and suggest a way to resolve it (a test, more data). Your job is to make the decision better informed, not to win the argument, and not to bend the numbers.

Common mistakes

• Presenting the analysis in the order you did it.
• Hedging so much that there is no recommendation.
• Caveats that are vague ("data may have issues") instead of specific.
• No "do nothing" option, so the comparison is unclear.
• Ending without a clear ask and deadline.

Quick recap

• Lead with the recommendation, then two or three reasons with numbers.
• Quantify impact in business terms, with ranges and a confidence level.
• Be specific about caveats; offer options including doing nothing.
• End with a clear ask, owner and deadline; pre-wire key stakeholders.

Try it

The Do tab has you write and deliver a one-page recommendation from an analysis you have done.