Klaviyo agency

Before any building, we look at the event stream. Are orders arriving with line items, product identifiers, categories and values attached. Is the started checkout event firing once and carrying the cart contents. Are viewed product events present, and do they include enough to segment on. Are refunds and cancellations coming through, so a returning customer is not chased for a review of something they sent back. Gaps here are invisible until a flow behaves strangely six months later, so they get fixed before anything is written.

A list is a snapshot of who was in it when someone pressed save. A segment is a rule that keeps checking. Almost everything a store wants to target is a rule: bought this category and not that one, subscribed but never ordered, ordered twice, has not opened anything in ninety days, lives where the product is in season. We rebuild the audiences as segments, retire the stale lists rather than leaving them to confuse the next person, and write down what each one means.

The most common Klaviyo problem is not a missing flow, it is several flows firing at the same person at once. Someone abandons a cart, buys an hour later, and still receives two reminders plus a browse message. We set filters on every trigger and exit conditions on every flow, so a purchase stops the sequences it should stop, and the smart sending window prevents a person receiving more than the account has decided is reasonable. Then we test it with real profiles rather than assuming.

Klaviyo builds properties on each profile from the order history it has, including an expected date for the next order once enough purchases exist. That is more useful than a flat thirty day wait, because a customer buying a large size and one buying a small size do not run out on the same schedule. We use those properties where the data supports them, fall back to a product-level cycle where it does not, and keep the two clearly separated so nobody assumes a prediction is a fact.

Sign-up forms decide the quality of everything downstream. We check what each form asks for, what consent it records, where it appears and how aggressively it interrupts. Collecting a first name and a category preference at signup makes the welcome sequence far more useful than an email address alone. Suppressed and bounced profiles get handled properly rather than accumulating, and any list bought or imported from an old system gets treated with suspicion before it is mailed.

Flows can run split tests continuously, which makes them the right place to test and campaigns the wrong one. We change one thing at a time: the subject line, the timing of the second message, whether an offer appears at all, the order of the content. Tests run until the difference is large enough to act on, not until the end of the month. Results and decisions get logged in the account so a future test does not repeat one we already answered.