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TMTM
I always wondered how companies like Amazon or Domino’s seem to know me better than some of my friends.
There was a phase when every Friday night was “pizza night.” I’d open the Domino’s app and almost like clockwork there’d be a “₹150 off on your favourite order” coupon waiting for me, usually right around 7:00 pm.
Then one month I got busy, stopped ordering for a while…
Suddenly, the messages changed:
“We miss you! Here’s a special comeback offer.”
“Flat 40% off on your next order – valid for 2 days.”
Same thing with Amazon.
I’d browse a few gadgets, close the app, and forget about it.
Next morning: an email in my inbox…
“Still thinking about these headphones? Here are some deals you might like.”
At first, it felt like good timing.
After a while, it felt too precise to be random.
The data person in me kept asking:
How do they know I’m a “regular” customer in some categories and a “lost” one in others?
How do they decide who gets which coupon, when, and for what?
That curiosity pulled me deep into the world of customer segmentation, RFM, and loyalty programs.
Forget the buzzwords for a moment.
At its core, customer segmentation is just this:
“Not all customers are the same. Let’s stop treating them as if they are.”
When you blast the same offer to:
Someone who buys from you every week
Someone who bought once two years ago
Someone who only buys during sales
…you’re basically wasting money and annoying people.
Segmentation says:
These customers buy frequently → treat them differently.
These customers spend a lot per order → treat them differently.
These customers used to buy but don’t anymore → treat them differently.
Instead of one big crowd, you now see clusters of behaviour.
And that’s where RFM comes in.
RFM is one of those things that sounds fancy but is beautifully simple.
It stands for:
R – Recency: How recently did the customer buy?
F – Frequency: How often do they buy?
M – Monetary: How much do they spend?
If you think about it, these three tell almost the whole story:
Someone who bought yesterday, buys often, and spends a lot → clearly valuable.
Someone who hasn’t bought in 6 months, used to buy frequently, and spent a lot → a churn risk worth winning back.
Someone who buys rarely and spends very little → probably not worth aggressive discounts.
Let me break each one down with how companies actually use them.
If I ordered pizza last night, Domino’s doesn’t need to “win me back.”
They might suggest add-ons or premium options, but they don’t panic.
If I haven’t ordered in 60–90 days, I suddenly look like:
Someone who switched to a competitor
Someone whose needs changed
Or someone who just forgot about them
That’s when you get those
“We miss you, here’s 40% off” messages.
High recency → soft nudge
Low recency → stronger “come back” campaigns
This is where companies differentiate between:
People who use them once in a while
People who use them like a habit
If you order every weekend, you’re:
More predictable
More reliable
More worth retaining at all costs
Frequent customers might get:
“Thanks for being a regular” style rewards
Early access to new menus / features
“Gold / Platinum / Elite” status badges
It’s less about discounting heavily and more about making them feel like VIPs.
Some customers order often but small.
Some order rarely but very big.
Some do both.
Companies look at:
Average basket value – how much they spend per order
Total spend – how much they’ve spent over their lifetime as a customer
High monetary value customers are often the ones who:
Get priority support
See early sale previews
Receive exclusive offers that aren’t shown to everyone
Now, when you combine these three:
High R, High F, High M → Your stars
High F, Low M → Frequent but low spenders (maybe push bundles)
High M, Low F → Big-ticket but occasional (maybe push reminders or memberships)
Low R, High past M → Dormant VIPs (strong win-back campaigns)
This is how you move from:
“Send this ₹200 coupon to everyone”
to
“Send this specific offer to this specific type of customer because
it aligns with their behaviour and our profit goals.”
Once you understand RFM, loyalty programs start to make a lot more sense.
Most loyalty programs are structured around two simple ideas:
Reward the behaviour you want more of
More orders
Higher basket value
Longer relationship
Protect profitability while doing it
Don’t give heavy discounts to people who would have bought anyway
Don’t waste offers on people who never convert
Invest heavily in the segments that truly matter
So you’ll see:
Tiers like Silver, Gold, Platinum
Points that accumulate as you spend
Exclusive benefits unlocked over time
Thresholds like “spend ₹10,000 in 3 months to maintain your Gold status”
All of these are essentially behavioural nudges designed on top of RFM-like logic.
They reward people who are already behaving well (for the brand),
and gently push others to move into those profitable patterns.
Here’s the part most users never see.
Behind your favourite pizza coupon or Amazon offer, there’s usually:
Multiple tables storing your activity
A data engineer / analyst who stitched them together
A set of metrics like total orders, average spend, and recency
A set of rules or models mapping those metrics to offers
It often looks a bit like this under the hood (conceptually):
Pull all customers and their purchases.
Compute:
Last purchase date
Number of orders
Total spend
Assign each customer into a bucket based on R, F, and M.
For each bucket, define:
What message to send
How much discount, if any
Whether to treat them as growth, retention, or recovery targets
We experience it as:
“Nice, they gave me a personalised coupon.”
But under the surface, it’s disciplined analytics + careful design.
If you work (or want to work) in data, marketing, growth, product, or CRM,
these are not just “nice to know” concepts.
They shape questions like:
Who should we spend our marketing budget on?
Which customers are worth retaining at a higher cost?
When does giving a discount actually make sense?
How do we measure the impact of a loyalty program?
And if you’re on the data side, your role is often to:
Pull the right data
Build RFM-style metrics
Make segmentation possible
Help business teams test and iterate on loyalty strategies
You’re not just writing queries.
You’re literally influencing how the brand treats its customers.
Next time you see:
“We miss you! Here’s 20% off.”
“You’ve unlocked Gold membership.”
“Special offer just for you.”
…you’ll know there’s a quiet engine behind it, powered by:
Customer segmentation
RFM-style metrics
And a carefully designed loyalty program
All working together to answer one question:
“How do we talk to this customer in a way that feels personal, makes them feel valued, and still makes business sense for us?”
And if you’re someone who loves both data and business, this is exactly the kind of system you can learn to design and power.
If you’d like to build this kind of loyalty engine yourself, check out this hands-on project:
Designing a Customer Loyalty Program for GlobalMart using SQL.
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