Step by step guide to creating customer segments in Apteco FastStats

If you work in marketing or data and want to actually do something with your customer data—like send targeted campaigns or spot trends—then customer segmentation is your bread and butter. Let’s be real: most people open up a tool like Apteco FastStats and either get lost in the options or end up creating the same tired segments as last year. This guide is for anyone who wants to cut through the clutter and actually build segments that matter, without wasting a week reading manuals or being sold on AI unicorns.

Below, you’ll find a step-by-step, no-fluff walkthrough to creating customer segments in Apteco FastStats. I’ll call out what’s worth your time, what’s not, and where folks get tripped up.

Before You Start: What You’ll Need

  • Access to Apteco FastStats (with a database loaded)
  • A general idea of what you want to achieve (Don’t overthink it. “People who bought in the last 6 months” is a fine place to start.)
  • Basic understanding of your available data fields (e.g. customer demographics, transactions)

If your data is a mess or not up-to-date, fix that first. No tool, including Apteco, can work miracles with garbage data.

Step 1: Decide on Your Segmentation Goal

Don’t skip this. Before you even log in, ask: Why are you creating these segments? Be specific.

  • Sending a targeted email campaign?
  • Finding lapsed customers to re-engage?
  • Building lookalike audiences for ads?

Write it down. The clearer your goal, the easier the next steps get—and the less likely you’ll end up with “segments” like “all customers in the UK” that nobody ever uses.

Pro tip: Start small. You can always get fancier later.

Step 2: Get to Know Your Data

Open FastStats and take a minute to browse your data fields. Seriously—spend five minutes clicking around.

  • Look for: Demographics (age, gender, location), transactions (purchase dates, products), engagement (email opens, web visits)
  • Ignore for now: Ultra-specific fields you don’t understand. If you’re not sure what “Flag_A” means, skip it.

If you can’t find what you need, talk to whoever manages your database. Don’t waste time segmenting on incomplete data.

Step 3: Open the FastStats Selection Window

This is your main workspace for building segments. Here’s how to get there:

  1. Launch FastStats and open your database.
  2. Click the Selection button (sometimes called “Create Selection”).
  3. The Selection window should pop up. This is where the magic happens.

You’ll see your data fields on the left, a canvas or workspace in the middle, and options for combining or filtering on the right/top.

Step 4: Build Your First Basic Segment

Let’s say you want “Customers who bought in the last 6 months.”

  1. Find the transaction date field (e.g., “Last Purchase Date”).
  2. Drag it onto the workspace.
  3. Set a filter:
  4. Choose “is after” (or “is in the last”) and enter the date six months ago, or select the relative date option.
  5. Click “Run” or “Preview” to see how many customers you’ve got.

Don’t worry: If you get zero results, double-check your date filter. It’s usually a typo or the wrong field.

Combining Criteria

Want “Women in London who bought in the last 6 months”?

  • Drag “Gender” and “Location” onto the workspace.
  • Set Gender = Female, Location = London.
  • Combine with “AND” logic.
  • Add your purchase date condition as above.

Pro tip: The visual canvas lets you drag and drop, so experiment. If you mess up, just delete a box and try again.

Step 5: Refine with Exclusions and Advanced Filters

Here’s where most people go overboard. Start simple, then layer in complexity.

  • Exclude recent opt-outs: Drag your “Email Opt-Out” field, set to “No,” and combine with “AND.”
  • Exclude recent campaign recipients: Add a filter for “Last Campaign Date” is before X or is blank.

If your segment gets too small or oddly shaped, check your logic. “AND” narrows, “OR” broadens.

Gotchas to avoid:

  • Don’t stack up too many “AND”s unless you really need them—you’ll end up with a segment of five people and wonder what went wrong.
  • Watch out for default values or blanks; not all systems treat missing data the same way.

Step 6: Save and Name Your Segment

When you’ve built a segment you like:

  1. Click “Save” or “Save Selection.”
  2. Give it a clear, practical name—something like “2024 Q1 Lapsed Shoppers” beats “Test Segment 2.”

Avoid: Vague or cryptic names. You’re not impressing anyone with “Q2_revamp_agg2.”

Add a brief description if you can. Future-you (and your teammates) will thank you.

Step 7: Validate Your Segment

Don’t just trust the numbers—check them.

  • Preview a random sample of customers. Are they who you expect?
  • Spot-check filters: Change one filter and see if counts change as expected.
  • Ask a colleague: “Does this segment make sense for our campaign?”

If it looks off, revisit your criteria or check for data glitches.

Pro tip: Build segments in layers. Start broad, then add filters one at a time, checking the count as you go.

Step 8: Export or Use Your Segment

Segments don’t do much sitting in FastStats. Here’s what you can do next:

  • Export the list: Usually as CSV or Excel. Use the “Export” button and pick the fields you need (like email, name, etc.).
  • Send to marketing automation: If you’ve got Apteco PeopleStage or another tool, push the segment directly for campaign use.
  • Report or visualize: Create charts or dashboards to share insights with your team.

Don’t: Export more data than you need. Stick to what’s relevant for your purpose—privacy and sanity both matter.

Step 9: Rinse and Repeat—But Smarter

Once you’ve got the basics down, you can:

  • Test different segment ideas (e.g., high-value vs. new customers)
  • Layer in behavioral data (web activity, event attendance)
  • Set up scheduled or dynamic segments that update automatically

But don’t let “advanced features” distract you from what works: clear, actionable segments that serve a real goal.

What Works, What Doesn’t, and What to Ignore

What works: - Starting with a clear goal and a simple filter - Validating segments before you use them - Naming segments clearly for future reference

What doesn’t: - Overcomplicating with too many filters up front - Blindly trusting the data—always spot check - Creating segments just because you can (if nobody uses it, it’s clutter)

Ignore: - Fancy features you don’t understand (until you need them) - “Best practice” segments that don’t fit your actual goals

Keep It Simple—Iterate As You Go

Customer segmentation in FastStats isn’t magic, but it is powerful if you keep it focused. Start small. Name things clearly. Check your work. If your segment isn’t useful, tweak it and try again. Most importantly, don’t get caught up chasing perfect segments—just build, check, and improve as you go. That’s how you actually get value from your data.