Klaviyo filtering: the secret to maximizing the Shopify ROI

Master the logic of Klaviyo filtering for Shopify. Learn how to use trigger, flow, and segment filters with Littledata to boost your email ROI.

While many people see email profiles filtering as a simple audience creating tool, its power actually comes from the flow logic becoming dynamic. Such a dynamic engine is the driving force behind the right message reaching the right person at the peak of their intent.

One of the primary problems most email marketers face today is that "blast" campaigns lead to poor deliverability, high unsubscription rates and overall trust erosion. Simply put, intrusive emails make subscribers feel negatively about your brand. By using filters in your Klaviyo email marketing campaigns, you create guardrails that keep your messaging laser-focused.

There are three levels where filtering can be applied within the Klaviyo ecosystem - Triggers, Flow filters and Segments.

Trigger filters

The trigger filters apply to whatever event starts your flow and act as a gatekeeper at the very beginning of the journey. They determine who is supposed to enter an automated sequence (and who is not!) based on the details of the initial trigger:

For example, you might want to create a Klaviyo flow for everyone who viewed any product of a specific brand (above) or for customers who purchased six or more times because you want to thank them with a $$$ coupon for their next purchase (below):

By adding a trigger filter at the entrance for purchase count threshold, you can easily separate these loyal high LTV shoppers from the rest.

The filter in the first screenshot is item-scoped whereas second one is based off of user's profile!

Next, we have flow filters, which function as internal checkpoints, evaluated DURING the flow itself.

Flow Filters

Unlike trigger filters that only check for entry conditions, Klaviyo allows you to decide how each email recipient proceeds at any single step of an automation. With these filters you can apply conditional logic to a block within the flow, even after a user has already started the sequence. For example, checking profile's external datapoint (hair style preference) that this brand splits messaging on, can be done within the flow itself:

Another example would be if you are using the Klaviyo Time delay block and decide you want to wait longer for certain users or fast-track others based on their evolving behavior, flow filters give you the power to branch their path dynamically.

Segment Filters

The third pillar for understanding filtering is creating segments, which is primarily used for audience building. Oftentimes, this is where beginners should start because Create Segments UI has the best visibility of various dimensions and values available.

The ease of access to segment conditions allows junior marketers or those with no experience to see exactly how data can be used to define a specific audience.

In the example above the operator chosen is "What someone has done (or NOT done)" and it's just one of many self-explanatory operators you can see in the screenshot below:

You can figure out what each one of these operators mean, here we wanted to bring to your attention a nuanced approach to consent - "if someone is/not within the EU" offers a different perspective from "can receive email marketing because they subscribed" which you can see in the next screenshot:

With this example we wanted to highlight the filter icon on the right hand side which opens a proprietary set of values for each operator/dimension combo. We encourage you to dive into your own dataset and see which additional filter is at hand for the use cases you find compelling.

Segment size

We used the following example of using predictive properties such as profile gender to bring to your attention another essential feature of segment creation: headcount.

Whenever you apply a condition to a segment its size dynamically updates (the number in the blue box at the top of the image). This is extremely important to understand when it comes to suppressed profiles: unless you add "Person can receive email marketing" condition - your segment size will INCLUDE suppressed emails in the total number!

Don't worry, emails would never go to those suppressed email addresses, but this is the way to understand how each segment behaves when it comes to unsubscription rate.

Abandoned cart server-side tracking powered flow

If we were to choose one core use case for Shopify email automations, the abandoned cart flow would be the one that boosts your email marketing ROI and the overall bottom line. This flow is vital because the user has already expressed a strong desire to buy but has not yet finished the transaction. A common filter set at the beginning of this flow powered by the Add to cart event confirms that the user has placed zero orders among other conditions - in the next screenshot you can see how a real life filter looks like:

Having a strong and reliable identifier for user profiles who added an item (or several items!) to their cart, but never proceeded to purchase is email retargeting 101 alongside Browse abandonment or the Checkout abandonment flows. For all three scenarios, having the server-side signal that identifies the highest possible user profile count in a privacy compliant manner is crucial.

What has happened since you started this flow?

One of the most critical tools for preventing spammy automation is the "since starting this flow" operator, which you can see in the screenshot above. This allows the system to check if an action (in this case - a purchase) has happened after the user entered the flow, which ensures you aren't asking someone to buy something they have already paid for. You can also refine these groups using shopify tags to include or exclude specific segments like wholesale buyers or influencers.

The reason why Littledata server-side tracking matters so much for bespoke filtering is cause it provides user identification that native, browser-based integrations often miss. If a user adds an item to their cart but is not logged into the shopify store account, standard integrations can not recognize user identity, because the email is not present at the moment of the add to cart event. In such scenarios Littledata uses several fallback methods to track user's Klaviyo identity across sessions while remaining 100% privacy-compliant. Matching identifiers with your store's existing email pool results in significantly higher user count for Cart, Browse items or checkout abandonment flows compared to standard Klaviyo to Shopiify integrations.

Furthermore, Littledata works seamlessly with shopify markets to provide regional identifiers, making it easy to introduce localized logic into your flow filters based on a shopper's specific country or currency.

Filters in Klaviyo: conclusion

Filtering is only as powerful as the data feeding it. While Klaviyo allows you to pretty simply deploy a filtering logic in the interface, Littledata provides the high-fidelity events that make these filters game changers. By moving away from generic messaging and leaning into granular flows by applying proper filters, you protect your brand reputation and ensure that every email sent actually adds value, both to your bottom line as well as to the customer experience.