Playbook
The Klaviyo Profile: What You Know About Each Subscriber (And What to Do With It)
Most brands treat their Klaviyo list like a mailing list - a column of email addresses you send stuff to. But every profile in Klaviyo is a small file of behavior, history, and preference data. Learning to read it, and act on it, is what separates brands that send email from brands that run email programs.
The Kaydence TeamSeptember 25, 20268 min read
Open any Klaviyo profile and you'll see more than a name and address. You'll see the first time this person showed up, the last time they opened something, every order they've placed, the product pages they've visited, which flows they've entered, and in some cases answers to quiz or form questions they filled out directly. It's not magic - it's just data that accumulates every time someone interacts with your store or your email. The problem is most brands never look at it systematically, and so it just sits there.
The Signals That Are Already in Every Profile
You don't have to do anything special to collect most of this. Klaviyo builds it passively as your store runs. The properties worth paying attention to - and acting on - fall into a few buckets.
- Engagement history: Last open, last click, email open rate over time. This tells you whether someone is still a real member of your audience or a ghost who'll hurt your deliverability.
- Purchase history: Number of orders, total spend, first purchase date, last purchase date, what categories or products they bought. This is the single richest signal you have.
- Predicted properties: Klaviyo calculates things like predicted lifetime value, expected next order date, and churn risk. These are estimates, not certainties, but they're directionally useful for prioritization.
- Site behavior: Page views, product views, collections browsed. This tells you what someone is thinking about even when they haven't bought anything yet.
- List and segment membership: Which signup form they came through, which flows they've entered and exited, whether they've been flagged as a VIP or lapsed.
- Custom properties: Anything you push in yourself - quiz answers, subscription tier, answers to a post-purchase survey, a birthday field. This is where zero-party data lives.
None of this is useful as trivia. It's useful because it should change what you send this person and when you send it.
Purchase History Is the Most Underused Profile Data in DTC Email
Most DTC brands segment on "bought once" versus "bought multiple times" and call it a day. That's a start, but there's much more available. Someone who bought once, twelve months ago, in a category you've since expanded - that's a different conversation than someone who bought twice in the last ninety days. One is potentially lapsed and needs re-engagement before a win-back sequence starts. The other is in an active purchase cycle and probably wants to hear about what's new or complementary to what they've already tried.
The category data matters too. If someone only ever buys from one product line, sending them campaigns about a different line might actually feel off-brand to them - like getting a recommendation that has nothing to do with why they showed up in the first place. You can use purchase category to tighten campaign targeting so the right message finds the right part of your list.
Site Behavior Tells You What Campaigns to Send (and Skip)
Someone who viewed your best-selling bundle page twice this week is not the same as someone who clicked through a campaign months ago and never came back. Both are "active subscribers" by most standard definitions. Only one of them is in a buying mindset right now. When you're deciding who gets a campaign promoting that bundle, recent site behavior is a sharper signal than a generic engagement score. In Klaviyo, that's a segment condition as simple as: Viewed Product at least 2 times in the last 14 days - one filter that meaningfully narrows your audience to the people most likely to act.
This is also why browse abandonment flows exist - but it's broader than just the flow. Site activity data should inform campaign targeting too. If a subscriber has viewed a specific collection three or more times in the past two weeks, they belong in the segment that gets the campaign about that collection. That's not complicated logic. It just requires someone to actually set it up.
Engagement Data Is a Health Check, Not Just a Deliverability Stat
The deliverability angle on engagement data gets talked about plenty - suppress unengaged subscribers, protect your sender reputation, all of it true. But engagement data is also useful at the individual profile level as a signal of relationship health. Someone who opened regularly for six months and then stopped is telling you something. It might be that your content drifted from what they signed up for. It might be that their buying window closed. It might just be inbox crowding. You don't always know why, but you know when - and that timing is useful for deciding whether to run a re-engagement sequence or start walking them toward the sunset list. In Klaviyo, a segment filtered to Has opened email zero times in the last 90 days and was added more than 90 days ago is the clearest possible picture of who needs that decision made.
The mistake brands make is treating disengagement as a binary: either someone is active or they go straight to suppression. The middle ground - a deliberate re-engagement sequence that gives them a real reason to interact - is worth running for a defined window before you make that call. What that window looks like depends on your purchase cycle. A brand that sells a product people buy quarterly has a different definition of "lapsed" than one with a 30-day replenishment cycle.
Custom Properties Are Where Your Strategy Gets Personal
Everything covered so far is data Klaviyo collects automatically. Custom properties are what you add on top - and they're often the most actionable layer. A subscriber who answered a quiz and told you their skin type, their budget range, or their primary concern is giving you permission to be specific. A subscriber who filled out a post-purchase survey and said they bought as a gift is telling you they're a potential repeat gifter. A subscriber who selected "I buy for my business" at signup is probably not the right person for a campaign aimed at personal use.
The catch is that custom properties only work if you collect them and then build targeting logic around them. In Klaviyo, that looks like filtering a segment where skin_type equals dry, then suppressing that segment from campaigns featuring oily-skin SKUs. That's one property doing real work. A lot of brands run quizzes or surveys and store the answers without doing anything with the data downstream. The collection is only the first step.
How to Actually Act on Profile Data Without Building Fifty Segments
You don't need a segment for every possible combination of properties. Start with the decisions that actually change your sends. A useful filtering hierarchy looks like this:
Engagement gate: Is this person engaged enough to receive this campaign at all? If they haven't opened or clicked anything in the past 90-plus days, they may need a re-engagement path first, not the campaign.
Buyer tier: Are they a one-time buyer, a multi-buyer, or an active replenisher? Each group has a different relationship with you and needs a different message, tone, and offer logic.
Relevance match: What have they bought or browsed that makes this specific campaign relevant to them? This is where purchase category and recent site behavior do the filtering work.
Those three questions will filter your list more cleanly than most brands currently do, and they rely entirely on data that's already sitting in Klaviyo. Building and maintaining that logic is the part most brands underinvest in. It's not glamorous. It doesn't feel like marketing. But it's the infrastructure that makes every other part of the email program more effective over time - because you're sending things people actually have a reason to open.
Reading the Profile Before You Touch a Single Flow
When our team takes on a new Klaviyo account, reading the profile data is one of the first things we do. Not just to check what's there, but to understand what's being ignored. We look at whether segments are built on meaningful properties or just defaults, whether custom data is being collected and actually used, and whether the flow and campaign logic reflects what the profile file is actually saying about each subscriber.
If you want to see how well your current setup uses what's already there, a free Klaviyo teardown is where to start.
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