To start with .. some AI responses are just too funny not to share…

Whether you are a seasoned pro, or if you are looking for your first anaytics role, or just want to be able to convey your prowess in an interview .. having a solid analytics methodology up your sleeve is a must have.

The methodology needs to be simple, effective and most of all results driven.

This edition is about breaking down that analytics loop for you.

Not with fancy dashboards or another analytics tool, but with a straightforward method for turning murky questions into confident decisions.

I call it the Business-Driven Analytics Cycle.

It’s fast. It’s teachable. You can run through it in an afternoon, and it’s built to work under pressure. Especially when someone leans over and says So, what’s our next move?

You WILL BE READY

The idea’s simple: Start with the decision. Let the data get you there faster with fewer blind spots.

The cycle has five moves. They loop. They flex. And they mirror the way strong problem-solvers already think.

This just puts it in writing.

Lets go!

1️⃣ Start with the Right Question

Everything begins here. What are we really trying to figure out?

Not the vague version. Not the “I’m just poking around” version. A real, decision-ready question. One you could act on tomorrow.

🎯 Think specifics:

• Who are we talking about?

• What timeframe?

• What behavior matters?

• What would success look like?

For example, if you’re looking at churn, don’t ask “How can we reduce churn?” That’s too fuzzy. A sharper version might be:
“Among enterprise customers in Q3, what behaviors changed in the 30 days before cancellation, and by how much?”

That kind of clarity drives change. A process might shift. A budget might get tweaked. Messaging could tighten on Monday.

📝 Pro tip: Treat the question as a contract. It defines scope. It defines success. It keeps you out of rabbit holes that are interesting but irrelevant. If you only do one thing this week, write down a tight, actionable question and share it with someone who’ll give you a gut-check.

2️⃣ Pull Only the Data That Matters

Once the question’s clear, don’t overcollect. Don’t overclean.

Just pull the data that matches the scope.. audience, timeframe, key variables.

🧹 Quick sweep checklist:

• Filter to only what’s relevant

• Drop nonsense or obviously bad data

• Standardize inconsistent labels

And while you’re in there, start an Assumption Log. Just a simple list in the corner of your sheet. For each decision you make, jot down what it was and why.

📌 Don’t skip this. It’s the easiest way to build trust. When someone asks, “Why do we have zero-dollar transactions here?” you can point to your note: Kept them in because Finance confirmed they’re free trials.

💡 That one habit turns second-guessing into shared understanding. It speeds up reviews. It makes you the kind of analyst people trust with tough calls.

3️⃣ Find the Plain-English Story

Now that you’ve got clean, scoped data, ask it three dead-simple questions:

1. What’s the level?

2. What’s the trend?

3. What’s different between groups?

Stick to the basics. Means, medians, ranges, a few clear charts.

🗣️ Write your findings in full sentences.
Don’t say “Tickets up 20%.”
Say, “Churned accounts submitted 20% more technical tickets than renewals in their final month.”

Someone should be able to forward your note to a VP and have it land without extra context.

🔍 You’ll see tempting patterns. That’s normal. Just remember: correlation is a clue, not a cause. Treat patterns as leads, not conclusions.

4️⃣ Pressure-Test the Insight

Time to step back and ask:

1. Is this big enough to matter?

2. Do we have enough evidence to say it with a straight face?

This is where you check for practical significance, not just statistical noise. If the effect is tiny, say so. If the sample’s shaky, say that too. Clear beats clever.

🔁 If it’s muddy, loop back. Refine the question. Pull a sharper dataset. Take another cut.

📈 Iteration isn’t failure. It’s how actual learning happens.

5️⃣ Move the Business, Not Just the Mouse

📉 Data without action is just decoration.

Your job now is to translate insight into movement which means something real, something someone can do next week.

📬 Try this format (one slide or a short email):

• The business problem in one line

• The key finding in one line

• What to do next

• Expected impact

• Owner and deadline

If there’s no owner and no date, it’s not ready.

🧪 Uncertain about causality? Propose a pilot. A simple, low-risk test with a clear success metric. You’ll still move forward, and the business learns something new.

⏱️ Want to Try It Today? Here’s a 45-Minute Drill

No fluff. Just clarity shipped.

• 0–10 min: Write one sharp, decision-ready question with a defined success outcome

• 10–25 min: Pull a minimal dataset. Start your Assumption Log

• 25–35 min: Write three plain-English findings, each with a group and a “by how much”

• 35–40 min: Make a confidence call. Is it big enough to matter?

• 40–45 min: Draft a one-slide or one-paragraph recommendation with an owner and date. Email it to yourself or someone who can act

That’s it. No spinning. Just insight, action-ready. Practice for when it all gets real.

Where to Try It First?

Here are some idea that spring to mind.

Churn
Compare the final 30 days of behavior for churned vs. retained accounts. Trigger a re-engagement path for those showing risk patterns.

Revenue per Visit
Map high-value paths by segment and device. Push the best paths higher on the page for the right audiences.

Support Backlog
Identify ticket types that lead to reopens and long resolution times. Add a checklist before submission and track reopen rates.

Each one follows the same rhythm:
Start with a tight question. Pull just what fits. Tell a clear story. Check if it matters. Recommend something specific and doable.

📈 What Actually Changes When You Work This Way

• Less tinkering, more fixing

• People see how you think, not just what you built

• Faster reviews because your logic is visible and discussable

• Smaller, sharper, testable recommendations

• Most importantly, you create a habit of shipping real decisions

Try the cycle once this week. Just once. One question. One dataset. One decision.

Next week, do it again.

That’s how people start seeing you as the one who moves the needle, not just the cursor.

TADAA: The Analytics Decline Accelerates Alarmingly

Welcome to this week's special report on Career Annihilating Events. We're tracking the most significant job-killing news from the past seven days where AI is replacing the white-collar workforce. Here are the three most devastating stories that broke between October 19-25, 2025.

#1 MOST CAREER ANNIHILATING: Amazon Plans to Replace Over 600,000 Warehouse Jobs with Robots (October 21, 2025)

Internal documents reveal Amazon's robotics team aims to automate 75% of operations, eliminating the need to hire 160,000 US workers by 2027 and over 600,000 by 2033, saving 30 cents per shipped item or $12.6 billion. The e-commerce giant—currently America's second-largest employer—could transform from job creator to "net job destroyer" according to Nobel Prize-winning economist Daron Acemoglu, with automation already reducing warehouse staffing by 25% at its most advanced facilities.

Source

#2 MODERATELY CAREER ANNIHILATING: Meta Cuts 600 AI Jobs While Replacing Privacy Auditors with Automation (October 23, 2025)

Meta slashed 600 positions across its AI division including the prestigious FAIR research unit, while simultaneously eliminating over 100 risk review and privacy compliance roles—replacing human auditors with automated systems despite regulatory obligations to the FTC. The dual cuts reveal the brutal irony: even AI workers building the technology aren't safe, and the human oversight preventing AI disasters is being automated away to "achieve more precise and dependable compliance results."

Source

#3 SOMEWHAT CAREER ANNIHILATING: Salesforce Slashes 4,000 Support Roles, Replaces with AI Agents (October 19, 2025)

CEO Marc Benioff revealed Salesforce reduced its customer support team from 9,000 to 5,000 employees, with AI agents now handling 50% of all customer interactions and reducing support costs by 17% since early 2025. While cutting 4,000 support jobs, the company is simultaneously hiring 3,000-5,000 new salespeople, with Benioff stating AI "cannot replace human salespeople" but admitting it has completed over one million customer conversations—proving the company views support roles as expendable while protecting higher-value sales positions.

Source

✅ Ready to try the BDAC methodology? Pick your question now. Forty-five minutes later, you might just have a decision worth sharing.

Best,

Tom.

Disclaimer: Some of the articles and excerpts referenced in this issue may be copyrighted material. They are included here strictly for review, commentary and educational purposes. We believe this constitutes fair use (or “fair dealing” in some jurisdictions) under applicable copyright laws. If you wish to use any copyrighted material from this newsletter for purposes beyond your personal use, please obtain permission from the copyright owner.

The information in this newsletter is provided for general educational purposes only. It does not constitute professional, financial, or legal advice. You use this material entirely at your own risk. No guarantees, warranties, or representations are made about accuracy, completeness, or fitness for purpose. Always observe all laws, statutory obligations, and regulatory requirements in your jurisdiction. Neither the author nor EchelonIQ Pty Ltd accepts any liability for loss, damage, or consequences arising from reliance on this content.