Abandoned-checkout flow with a discount problem
An online outdoor-gear retailer runs a three-email abandoned-checkout flow in Klaviyo. The trigger is Checkout Started from its Shopify integration. Email 1 sends after 4 hours, email 2 after 24 hours, and email 3 after 72 hours with a 10% discount. Last month the flow received 4,200 triggered profiles. Email 1 had a 51% open rate and 7.8% click rate, but the flow’s placed-order rate was only 1.9%. Smart Sending is on, quiet hours are 10 p.m.–8 a.m., and the flow sends to every profile that starts checkout, including repeat customers and profiles with no marketing consent. The client wants to add a fourth email and increase the discount to 15% because revenue is below target. Before making changes, you need to decide whether the bottleneck is deliverability, timing, offer, or audience definition, and whether the existing discount is cannibalizing full-price orders.
1. Which three data checks would you run first to separate a deliverability problem from a timing or offer problem? Explain what each check tells you.
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Start with delivery and audience diagnostics, not creative. Check:
- Sent vs delivered, bounce rate, and spam-complaint rate for each flow email. A high bounce or complaint rate points to list quality or authentication, not the offer.
- Click-to-open and revenue per recipient by email position. If email 1 gets clicks but no orders, the landing page or offer is suspect; if email 3 drives most orders, timing and discount are doing the work.
- Conversions split by first-time vs repeat customer and by cart value. If repeat customers convert at a much lower rate, the flow is over-mailing people who would have bought anyway.
Also compare time-to-purchase after the trigger against the 4/24/72-hour schedule. This separates deliverability from timing and offer before adding a fourth email.
2. Redesign the flow logic. Specify at least two conditional splits and one exclusion, and explain why each improves the outcome.
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Use flow filters and conditional splits:
- Add a flow filter so profiles who place an order after starting the flow exit. This prevents discount emails after purchase.
- Split on customer status: repeat customers get a service-oriented reminder with no discount; first-time prospects get the incentive later.
- Split on cart value: high-value carts get free shipping or concierge help instead of a percentage discount; low-value carts may need a minimum-order threshold to protect margin.
Keep Smart Sending and quiet hours, and confirm consent because the current flow includes profiles without marketing consent. The reasoning is to stop paying for orders that would have happened, reserve discounts for price-sensitive prospects, and avoid a flat 15% offer that trains all buyers to wait.
3. The client wants to add a 15% discount in email 1. What evidence would convince you that this is a bad idea, and what test would you propose?
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Evidence that 15% in email 1 is bad: higher discount redemption with flat AOV, lower revenue per recipient after margin, more repeat customers using the code, and a rising share of orders placed only after email 1. Propose a test: keep email 1 without a discount, then randomly assign eligible prospects to 10% vs 15% in email 3, with a no-discount control if ethically possible. Measure incremental margin, not just conversion. If the 15% variant wins on revenue but loses on margin and repeat rate, do not roll it out. Also check whether the existing 10% is already cannibalizing full-price orders by comparing a holdout of similar profiles.