What to look for in your Klaviyo data and how it should change your strategy

You're staring at a Klaviyo dashboard showing 47% open rates on your welcome flow, 2.1% click rates on last week's campaign, and a segment labeled 'At Risk' that grew by 800 profiles this month. Now what? 

Most marketers using Klaviyo have access to more data than ever before, but building a clear Klaviyo data strategy is often easier said than done. Dashboards are full of metrics, reports update in real time, and attribution models provide clarity. Yet, many teams still feel unsure about what to do next.

The issue usually isn’t a lack of information. It’s the gap between metrics and meaningful decisions. When data isn’t translated into strategy, it often leads to over-sending, misaligned targeting, or reactive optimization that treats symptoms instead of causes. 

As director of email marketing at SmartSites, I’ve seen the same pattern emerge over and over. And I want to help you avoid this. Here’s my advice on cutting through the noise so you can focus on the signals that actually matter and how they should guide smarter choices as your program grows. 

Start with the foundations: Is your data trustworthy?

The key is knowing where to look first. Data strategy isn't about monitoring everything, but rather building a diagnostic hierarchy, where each layer depends on the one before it. The first being the integrity of your data. Before using data to shape strategy, it’s worth asking whether the data itself can be trusted. Even small tracking or attribution issues can quietly skew insights and lead to confident decisions built on shaky ground.

This usually starts with validating your core event tracking and profile data. In Klaviyo, check your integration health dashboard to ensure key actions like product views, add-to-cart events, purchases, etc. are firing consistently. If these events are missing or inconsistent, performance reports will never tell the full story. The same applies to profile properties like location, purchase history, or subscription source. Klaviyo pulls these from your connected integrations automatically, but when source data is outdated or incomplete, segmentation becomes unreliable.

Poor data quality tends to push teams into false conclusions. A flow may appear underperforming when it is actually under-attributed. Engagement might look healthy when duplicate profiles are inflating list size. Strategic takeaway here is simple but critical: clean inputs create confidence. When your foundation is solid, every insight downstream becomes easier to trust and act on.

Audience data: Who your customers really are

Audience data is where strategy should begin, yet it’s often reduced to list growth alone. Size matters, but value matters more.

Looking at revenue by segment quickly reveals who is actually driving the business. In many accounts, a relatively small portion of the list contributes a disproportionate share of revenue. Klaviyo's 2026 benchmark data shows that flows generate an average of $2.54 per recipient when set up correctly, compared to $0.32 for campaigns, but in many accounts, a small percentage of the list drives the majority of that revenue. That insight should immediately influence how much personalization, attention, and investment those customers receive. High-value segments often respond better to relevance and timing than to increased frequency.

At the same time, engagement by lifecycle stage tells a different story. New subscribers, repeat buyers, and long-term customers each have different expectations and tolerance for messaging. Klaviyo's RFM analysis can help identify where customers sit in their lifecycle and when they're at risk of churning. 

Then watch your inactive segment. When inactive subscriber counts grow unchecked, it’s usually a sign that cadence or content depth needs to change. Audience data should guide not just who you send to, but how you show up for them.

Engagement data: How customers interact with you

Engagement metrics are most useful when viewed as patterns rather than isolated results. Single-send performance can be misleading, especially when it’s taken out of context.

Trends over time offer far more insight. A gradual decline in engagement often points to list fatigue or declining relevance, not one poorly written email. To spot this, compare your rolling 30-day click rate against the previous 30-day period. A drop of more than 15% signals it's time to reassess frequency or content strategy. Comparing click rate to conversion rate also helps pinpoint where problems actually live. When clicks are high but conversions are low, the friction is usually onsite, so check your landing page load times, product page clarity, and checkout process. When opens are strong but clicks lag, the message itself needs work: a clearer call to action, better content-to-subject-line alignment, or more compelling offers. Use Klaviyo's click maps to see exactly where recipients are engaging and where they're not.

Unsubscribe and spam complaint rates are equally important signals. These metrics tend to reflect audience sentiment more honestly than opens ever will. When they rise, it’s rarely because the audience is “bad.” More often, it’s a cue to reassess frequency, targeting, or the value being delivered in each send.

Conversion and revenue data: What actually drives value

Conversion and revenue metrics are where strategy meets reality. They force prioritization.

Revenue per recipient is especially helpful because it encourages decisions rather than observations. Some campaigns deserve to be repeated or scaled, even if they don’t generate the highest engagement. Others may look successful on the surface but fail to drive meaningful revenue. Looking at performance by campaign type also helps rebalance efforts between promotional and non-promotional messaging, which is critical for long-term sustainability.

Time to conversion adds another layer of insight. If customers convert quickly after a send, urgency and clarity are likely aligned. If conversions happen days later, there may be an opportunity to reinforce the message, adjust timing, or improve follow-up without increasing overall volume.

Flow performance: Where automation is (or isn't) working

Flows often account for a significant share of revenue, yet they tend to receive less ongoing attention than campaigns. That’s a missed opportunity.

Instead of focusing on whether a flow exists, it’s more valuable to understand where it’s performing well and where it’s quietly leaking value. In Klaviyo's flow analytics, look at email-level conversion rates within each flow. A single underperforming message can drag down an entire sequence. If your abandoned cart flow converts 8% on email one but drops to 0.5% on email three, that third email needs work or removal. 

Pay attention to drop-off points and time delays between messages. If you're waiting 72 hours between emails in a welcome series, you may be losing momentum. If you're sending too quickly, you may be overwhelming new subscribers. Flow data should guide refinement, not wholesale rebuilds. Small changes in sequencing, messaging, or timing often have outsized impact when applied to high-intent automation.

Predictive and advanced data: Signals for what's next

Predictive insights are most powerful when used directionally. Metrics like expected lifetime value, predicted next order date, or churn risk aren’t meant to be perfect forecasts. They’re indicators of where attention may be most valuable.

When certain segments show rising churn risk, early intervention can make a meaningful difference. When predicted value increases, it may justify deeper engagement or earlier outreach. The key is to treat these insights as strategic signals, not rigid rules, and combine them with human judgment.

Turning insights into action 

Data becomes strategic when it’s applied consistently. A simple review loop can help keep insights actionable rather than overwhelming. 

1. Review performance: Look at your top 3 and bottom 3 campaigns by revenue per recipient. Check flow conversion rates for drop-offs. Note any segments showing declining engagement.
2. Decide on one change: Pick the single highest-impact optimization based on what you found. Resist the urge to fix everything at once.
3. Measure the impact: Give your change 2-4 weeks to generate meaningful data before evaluating.
4. Refine and repeat: Document what worked, adjust what didn't, and move to the next priority.

This approach encourages intentional progress instead of reactive optimization, and it keeps teams aligned on why changes are being made.

Common mistakes to avoid

Many teams struggle not because they lack data, but because they try to optimize everything at once. Here are the common traps to avoid: 

• Over-sending to compensate for weak performance. When revenue dips, the instinct is to send more. But if your content isn't resonating, more of it won't help—it will accelerate list fatigue and unsubscribes.
• Testing low-impact variables repeatedly. A/B testing button colors or minor subject line tweaks rarely moves the needle. Focus testing on offer structure, send timing, and audience segmentation instead.
• Treating dashboards as verdicts instead of signals. A single campaign's performance doesn't define your program. Look for patterns across multiple sends before drawing conclusions.
• Optimizing everything at once. When you change five variables simultaneously, you can't identify what actually worked. Make one change, measure it, then move on.

Avoiding these pitfalls often unlocks more improvement than adding new tactics ever could.

Let your data lead 

Your Klaviyo data already contains the answers to where your biggest opportunities live. The challenge is learning how to listen to the right signals at the right time. When metrics are interpreted with intention, strategy becomes clearer, messaging becomes more relevant, and growth becomes more sustainable.

Let me know your thoughts:

Additional helpful resources:

• How to access and use the activity map
• Understanding Klaviyo's predictive analytics
• Getting started with the product analysis dashboard
• Getting started with the funnel analysis report
• Getting started with the recency, frequency, and monetary (RFM) analysis report
• Understanding the customer lifetime value (CLV) dashboard
• Understand data transformation in Klaviyo

https://community.klaviyo.com/marketing-30/how-to-filter-metric-analytics-by-segment-15307