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The five email flows every store should run

Welcome, abandonment, post-purchase, win-back and replenishment — in build order, with the revenue share each typically contributes.

Automated email flows are the highest-margin revenue in most e-commerce businesses. They send to a fraction of your list volume and routinely produce the majority of email revenue, because they reach people at the exact moment intent exists rather than on a Tuesday because the calendar said so.

A well-run lifecycle programme contributes 20 to 30 percent of total store revenue. Here are the five flows to build, in the order that pays back fastest, with the structure and timing we use on live accounts.

Build order matters

Build in this sequence: welcome, abandonment, post-purchase, win-back, replenishment. Each earlier flow feeds the later ones with subscribers and data, and each one is cheaper to build than the one before it is to fix badly.

Before any of it, three prerequisites. Authenticate your sending domain with SPF, DKIM and DMARC. Set up a dedicated subdomain for marketing sends and warm it over two to three weeks. Confirm your platform is receiving product, order and browse events correctly — every flow below depends on that data, and debugging a flow that never triggers is a waste of a fortnight.

One — welcome

Typically 15 to 25 percent of automated email revenue. The highest-intent moment a subscriber will ever have is the sixty seconds after they sign up.

Five emails over ten days:

  1. Immediate — deliver the promise. If you offered a discount, the code is the first thing in the email, above everything else. No brand story before the thing they asked for.
  2. Day 2 — the difference. Why you exist and what you do that competitors do not. One claim, specifically stated.
  3. Day 4 — proof. Reviews, results, press, user photographs. This is the email that converts sceptics, so make it concrete rather than decorative.
  4. Day 7 — the objection. Name the reason people do not buy — price, fit, shipping, returns — and answer it directly. Counterintuitively this outperforms another product email.
  5. Day 10 — the nudge. Restate the offer with a genuine expiry, then move the subscriber to the main list.

Split the flow by signup source. Someone who subscribed from a product page gets product-led messaging; someone who subscribed from a blog post gets an education-led version. This one split commonly adds 20 to 30 percent to flow revenue.

Two — abandonment

Typically 30 to 40 percent of automated email revenue. Three distinct flows, not one, ordered by intent.

Checkout abandonment — highest intent, shortest delay. Email one at 45 minutes, email two at 24 hours, email three at 48 hours. First email is functionally a receipt for an unfinished order: item image, price, one button back to the exact cart. No discount in email one — a meaningful share of these customers were simply interrupted, and discounting them trains the behaviour. Introduce an incentive only in email three, if margin permits.

Cart abandonment — added to cart, never reached checkout. Two emails at 4 hours and 24 hours. Include reassurance content: returns policy, shipping speed, a review of the specific product.

Browse abandonment — viewed a product twice or more, no cart. One email at 6 hours, framed as helpful rather than transactional. Suppress anyone who enters the cart or checkout flows so a single customer never receives all three.

Suppression logic is where most abandonment programmes go wrong. Map the exclusions before you build the sends.

Three — post-purchase

Typically 10 to 15 percent of automated revenue, and disproportionate influence on lifetime value. The period immediately after purchase is when a first-time buyer decides whether you are a shop they used once or a brand they belong to.

  1. Immediately after the order confirmation — what happens next, when it ships, how to reach a human.
  2. On delivery — how to use the product well. A short guide, a video, a common mistake to avoid. This single email measurably reduces returns in most categories.
  3. Day 7 — review request. Ask for the review only after they have had time to form an opinion, and make the ask one click.
  4. Day 14 — the complementary product, chosen by what the specific item they bought pairs with, not by best-seller list.
  5. Day 21 — the referral or loyalty invitation, while satisfaction is high.

Four — win-back

Typically 5 to 10 percent of automated revenue, at very low cost. Triggered when a customer passes 1.5 times their category’s median repurchase interval without ordering.

Three emails: a soft re-engagement showing what has changed since they last bought; a stronger incentive; and a final “are you still interested” that offers a preference change or unsubscribe. That last email looks like it costs you subscribers. It protects deliverability, which is worth more than the addresses.

Set the trigger interval from your own data, not a default. A coffee brand’s win-back should fire at eight weeks; a furniture retailer’s at eighteen months.

Five — replenishment

Typically 5 to 15 percent of automated revenue in consumable categories, and near zero in others. Build it only if your products run out.

Calculate the median days-to-reorder per SKU or category from your own order history — do not guess. Trigger the first email at 75 percent of that interval, a second at 100 percent, and a third at 125 percent with an incentive. Include a one-click reorder of the exact previous basket. Where a subscription option exists, this is the correct place to offer it, because the customer has just demonstrated the recurring need.

Measurement that keeps the programme honest

Track four numbers per flow:

  • Revenue per recipient, which is the only number that compares flows fairly across different volumes.
  • Conversion rate of the flow, and of each email within it.
  • Placement and complaint rate. Complaints above 0.1 percent are an emergency; deliverability damage takes months to repair.
  • Incrementality. Hold back 5 to 10 percent of eligible recipients from each flow and compare their purchase rate. Some abandonment revenue would have arrived anyway, and knowing how much is the difference between managing a programme and admiring one.

Common mistakes

Sending to everyone. Suppress recent purchasers, active flow recipients and unengaged contacts. Volume is not the goal.

Never rewriting. Flows quietly decay as products, pricing and positioning change. Review each one quarterly against its own revenue-per-recipient trend.

Discounting the first email. In checkout abandonment especially, this converts customers who would have paid full price and teaches the rest to abandon deliberately.

Ignoring plain text. A well-written plain-text email from a named person frequently outperforms a designed template, particularly in win-back and post-purchase.

Treating deliverability as an IT issue. List hygiene, sunset policies and engagement segmentation affect revenue more than any subject-line test will. Suppress contacts who have not opened in 180 days, and the rest of your programme gets better immediately.

A realistic build schedule

Welcome and checkout abandonment in week one — those two alone typically recover the cost of the whole project. Cart and browse abandonment in week two. Post-purchase in week three. Win-back and replenishment in week four, once you have pulled the repurchase interval data. Then spend the following quarter testing within the flows rather than adding new ones, because depth in five flows beats shallow coverage across fifteen.

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Analytics · FAQ

Questions this raises.

Something not covered above? Ask a senior specialist →

What is server-side tracking?

Server-side tracking sends analytics and conversion events from your own server to each destination instead of directly from the browser. It typically recovers 10 to 30 percent of conversion signal lost to ad blockers and browser tracking prevention, and improves match quality in Google and Meta.

Do we need server-side tagging?

It is worth the infrastructure cost when ad spend is high enough that better signal changes bidding, when over 30 percent of your audience uses Safari or Firefox, or when platform conversions and back-end orders disagree by more than 10 percent. Otherwise fix existing browser tagging first.

Will migrating break our historical reporting?

Not if you keep event and parameter names identical, run both collection paths in parallel for at least four weeks, annotate the cutover date, and publish the measured variance per metric. Redesigning your taxonomy during the migration is what breaks year-on-year comparison.

No, and it must not. Consent Mode v2 signals should be enforced at the point of forwarding, with modelled conversions filling the gap for users who declined. Sending events for declined users risks both regulatory penalties and loss of ad accounts.

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