Hey Reader,

This week's newsletter is different. Not a regular "how to solve problem X with SQL" kinda thing.

Because I've realized something: you can write perfect queries and still never get the career impact you want.

You need "soft skills" like presenting your results to leadership in addition to "technical skills" like using CTEs and WINDOW functions.

Let me show you what I mean.

The Scenario

Wednesday, 2pm. Q2 review meeting.

CMO: "Our booking conversion is down. What's causing it?"

Everyone looks at you. You're the "analytics person."

Your response: "I'll pull the data and get back to you by Friday."

You can write queries. You understand JOINs. But translating business questions into on-the-spot insights? That's different.

Now imagine this:

Your CEO asks: "Should we target our expert-level customers with next quarter's marketing budget?"

You run the query. You've got hundreds of expert customers in the database. Your biggest customer segment by count.

You present: "Yes. Expert customers are our largest segment."

CEO: "Great. Let's launch next week."

The next day, you review the analysis one more time.

Something feels off. You filter for active customers instead of just total count.

Expert customers: Hundreds in database, only a few dozen active in the past 6 months.

You almost convinced leadership to spend their entire marketing budget targeting customers who already churned.

The problem? You answered the SQL question (count customers) without thinking about the business question (which customers should we actually reach?).

This is where most analysts get stuck. Not because they can't write queries, but because they don't have a framework for presenting insights.

Three Layers Every Executive Presentation Needs

Layer 1: The Business Question (Start Here)

Before showing any data, state the question you're answering.

Bad opening: "I ran a query joining customers, bookings, and payments to calculate retention..."

Good opening: "You asked which customer segments should we target for retention. Here's what I found..."

Frame everything around THEIR question, not YOUR process.

Layer 2: The Insight (This Is What Matters)

State your findings in plain business language. No SQL terms.

Bad insight: "The ROW_NUMBER() window function partitioned by customer segment showed that expert-level customers have a 47.3% repeat booking rate."

Good insight: "Expert-level customers book again at nearly 2x the rate of beginners - 47% vs 24%. That's our most valuable retention segment."

See the difference? Same data, completely different framing.

Layer 3: The Recommendation (Make It Actionable)

Tell them what to DO with these insights.

Bad ending: "So that's what the data shows. Any questions?"

Good ending: "Based on this, I recommend we focus our retention emails on expert customers first. A 10% improvement in their retention would generate $200K more revenue than targeting beginners."

Real Example: How This Looks in Practice

Here's this framework with actual Summit Adventures data.

The Query:

-- Analyze revenue by expedition type for Q2 marketing priorities
SELECT 
    e.expedition_type,
    COUNT(DISTINCT ei.instance_id) AS total_instances,
    COUNT(DISTINCT b.booking_id) AS total_bookings,
    SUM(p.amount) AS total_revenue,
    ROUND(AVG(p.amount), 2) AS avg_booking_value
FROM expeditions e
    INNER JOIN expedition_instances ei ON e.expedition_id = ei.expedition_id
    INNER JOIN bookings b ON ei.instance_id = b.instance_id
    INNER JOIN payments p ON b.booking_id = p.booking_id
WHERE p.payment_status = 'completed'
    AND b.status IN ('completed', 'confirmed')
GROUP BY e.expedition_type
ORDER BY total_revenue DESC;

The Results:

How most analysts present it:

"I joined expeditions to expedition_instances using the expedition_id foreign key, then to bookings via instance_id, then to payments using booking_id. After filtering for completed payment_status and grouping by expedition_type with SUM aggregation, cultural has the highest total revenue..."

Executive eyes glaze over. Meeting moves on.

How confident analysts present it:

"Question: Which expedition types should we prioritize for Q2 marketing?

Insight: Cultural expeditions lead in total revenue ($374K from 139 bookings). Hiking has the highest per-customer value ($2,095 avg). Photography has the highest volume (193 bookings) but lowest margins ($1,441 avg).

Recommendation: Focus Q2 marketing budget on cultural expeditions (proven revenue generator), upsell hiking to premium customers (highest transaction value), and optimize photography pricing (high volume, low margin opportunity)."

Same data. Different impact.

Putting This Into Practice

Imagine the same meeting six months from now:

CMO: "Q3 bookings are trending below target. What do we know?"

Instead of asking for time, you're ready:

"I looked at three things:

1. Volume vs Value - Booking volume down 18%, but revenue per booking up 12%. We're not losing money, we're losing transactions.
2. Segment Analysis - High-value customers haven't booked in 90+ days. That's where we're losing volume.
3. Product Gap - We're underperforming in our premium tier expeditions.

Recommendation: Fast-track 3 expert-level expeditions for Q4. Target inactive high-value customers with early-bird pricing. Projected impact based on historical conversion rates."

CMO: "That's exactly what we needed. Let's move forward."

Same technical skills. Different framing.

Three Practices to Build This Skill

Practice 1: The "Executive Summary" Discipline

For every analysis you do, write a 3-sentence summary BEFORE sharing results:

1. What question did I answer?
2. What did I find?
3. What should we do about it?

If you can't articulate this clearly, you're not ready to present.

Practice 2: Remove the SQL

When presenting findings, pretend SQL doesn't exist. Explain your insights as if you discovered them through conversation, not queries.

The HOW matters to you (learning, documentation, reproducibility). The WHAT matters to them (business impact).

Practice 3: The "So What?" Test

After stating each finding, ask yourself: "So what? Why does this matter?"

Keep asking until you reach business impact. That's the level executives care about.

Your Next Presentation

Next time you're preparing to share SQL analysis:

1. Write your executive summary first (question, insight, recommendation)
2. Remove all SQL jargon from your talking points
3. Add business context to every number
4. End with clear next steps for decision-makers
5. Keep your query available if anyone asks for methodology

Your SQL skills got you the insights. Your communication skills will get you the career.

Until next time,

Brian

Brian Graves, creator of Analytics in Action Say 👋 on X/Twitter, LinkedIn, or book a call with me. You can always reply to these emails. I check them all.

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