Five questions an email marketing team should be able to answer from its data: from the true impact of a campaign to leaking discount codes and lapsed customers worth winning back.
Is the email marketing campaign working, or just taking credit?
A customer gets an automated email and buys something a week later. It’s tempting to call that a win. But plenty of those customers would have bought anyway, email or no email: they were already loyal, already due for a repeat purchase, already halfway to the checkout.
The only honest way to measure email campaign effectiveness is to compare: what did this group spend before they received the campaign, and what did they spend after, relative to a sensible baseline? Do that across a short window and a longer one, and across your different sales channels, and a genuine pattern starts to become visible. Without that comparison using email marketing analytics, a business can spend years crediting a campaign for results it never actually caused or, worse, cutting a campaign that was working.
Is your discount code leaking to people it was never meant for?
Promotional codes are built to reach a specific audience like new customers, a loyalty tier, people who took a specific action. But codes travel. They end up on coupon forums, get screenshotted and passed along group chats, show up on sites built purely to aggregate discounts. None of that shows up as fraud in a standard sales report; it just looks like redemptions.
Compare who actually redeemed a code against who was ever meant to receive it in the first place. A gap between those two groups, real usage by people outside the intended audience, is a straightforward, quantifiable signal that a code has escaped its original channel. Put a monetary figure on that gap, and a vague worry (“I think people are sharing our codes”) becomes a specific, fixable problem, usually solved with something as simple as tying codes to individual recipients.
Which lapsed customers are actually worth winning back?
Every business with a customer list eventually faces the same instinct: send a win-back offer to everyone who’s gone quiet. It feels productive. It’s often a waste of budget, because “gone quiet” describes two very different groups: high-value customers on the edge of leaving for good, and low-value, one-time buyers who were never coming back regardless.
A smarter approach scores customers on how recently, how often, and how much they’ve historically bought, then plots that against how many customers actually sit in each combination. That turns a single, undifferentiated list into a short, ranked set of segments genuinely worth pursuing and a much longer list that isn’t. The budget saved by not chasing the wrong segment is often worth more than the budget spent chasing the right one.
Customer segmentation: who was your customer, who are they now, and who are they becoming?
Most customer segmentation only answers the middle part of that question, ‘who is my customer now?’. Any retailer already knows this cast of characters instinctively:
- The teenager buying their first piece
- The parent picking something out on their kid’s behalf
- The young adult slowly building a collection
- The devoted regular who keeps coming back
- The one-time buyer who never resurfaces
What’s much rarer is knowing where any given customer sat on that map last year, where they sit today, and which way they’re moving.
A first-time buyer becomes a regular. A regular quietly goes quiet. Someone who once bought a single product line grows into a broader, higher-value relationship with the brand. None of that shows up if the only thing you measure is today’s snapshot.
Where exactly does your email marketing funnel break?
A top-line conversion rate tells you a percentage. It doesn’t tell you where in the process people actually give up, and that’s the part you need to fix anything. Sent, delivered, opened, clicked, converted: each step loses people, and each drop-off has a different likely cause and a different fix.
Losing people between “sent” and “delivered” is a technical problem.
Losing them between “delivered” and “opened” is a subject-line problem.
Losing them between “clicked” and “converted” is an offer or landing-page problem.
Treating all of that as one number hides which lever is actually worth pulling.
Marketing analytics in practice
We built exactly this kind of analysis for SkinKandy, a body piercing and jewellery retailer across Australia and New Zealand. Their lifecycle email programs (welcome, birthday, loyalty journeys) each needed a real answer on whether they were driving genuine revenue, whether their voucher codes were being used as intended, and which customers were actually worth further investment. You can read the full story in our case study.
Is your marketing data answering the right questions?
If your reporting can tell you what happened but not whether it mattered, that’s a smaller gap to close than it feels like. Talk to us about what deeper marketing analytics could look like for your business.
