Data Panda brings your Klaviyo data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.
Klaviyo was founded in Boston in 2012 around the idea that consumer brands had far more customer data than they were using. It has grown into a marketing platform covering email, SMS, reviews, segmentation and a customer data layer, with tight Shopify, Magento and BigCommerce integrations. It reports more than 193,000 paying customers, mostly DTC and mid-market e-commerce brands who measure themselves on repeat purchase rate.
The point of pulling Klaviyo into a warehouse is that Klaviyo reports the first order well, and the fifth order badly. Repeat purchase rate, cohort LTV, flow attribution that survives twelve months and profitability after returns all sit outside Klaviyo by design. Next to Shopify, Stripe and support data, the same Klaviyo flow gets a number that reflects whether its customers came back.
Flow performance, campaign value and cohort LTV on one timeline instead of three Klaviyo reports.
Let Klaviyo fire on the signals that the rest of the stack already saw.
Turn Klaviyo history into scoring that reshapes the next send.
Merchant-facing tools on Klaviyo data for people who do not live in the Klaviyo UI.
A list of concrete reports, automations and AI features we have built on Klaviyo data. Pick the one that matches your situation.
Which flows produce customers who come back?
Repeat-purchase rate per Klaviyo flow, twelve months after first send. Separates the flow whose first-order revenue looked great and whose cohort disappeared, from the flow whose customers are still ordering a year later at a stable cadence.
How much margin are we giving away in discount codes that did nothing?
Discount-code usage against a behavioural baseline of what the same segment bought without a code. Quantifies the margin that went to customers who would have converted anyway, not to incremental buyers.
Is SMS earning its keep against email?
Cost and attributed revenue per channel, per segment, with deliverability and opt-out rates on the same view. Shows the segments where SMS is paying back the per-message cost and the segments where it is eroding list health.
Marketing channel cost against attributed paid revenue and cohort LTV. The marketing budget stops being defended on revenue-per-send and starts being defended on revenue-per-retained-customer.
Segment migration and repeat-purchase rate per cohort, per product category. Merchandising sees which campaigns build repeat buyers and which move existing demand around, per product line.
List health, bounce rate and send-volume capacity over twelve months, with deliverability trend. Channel capacity and opt-out risk become a measured curve instead of an annual surprise.
Shopify customers, orders and refunds push into Klaviyo as profile properties and metric events with margin, LTV and last-order date. Onboarding, winback and loyalty flows trigger on real buying signal instead of the monthly CSV import the agency keeps forgetting.
Stripe subscription starts, cancels, failed payments and refunds push into Klaviyo as metric events tied to the profile. The failed-payment flow runs the hour Stripe fires dunning, not the next day, and churn-save sequences trigger on the downgrade itself.
Klaviyo segments defined on LTV, repeat-purchase rate or product affinity push to Meta as custom and lookalike audiences. Meta stops optimising against a pixel definition of a good customer and starts optimising against the one Klaviyo already knows.
Profiles with an open severity-one Intercom case are suppressed from non-essential Klaviyo campaigns until the case is resolved. Support does not have to read a sales email to an angry customer at the same hour they are being apologised to.
You keep the reporting tool you already have. We connect it to the warehouse where your Klaviyo data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Yes. Each Klaviyo account lands in its own schema with a shared profile dimension on top. Holding groups and multi-brand operations get per-brand isolation and a deduped customer view at group level, without merging profile lists by hand.
Profile data is scoped by access role. Identifiable fields stay within marketing and support audiences, while aggregate cohort reporting for finance and the board uses pseudonymised profile ids. Retention rules and consent state are respected at the warehouse level, not only inside Klaviyo.
We review your Klaviyo setup and the systems around it. Together we pick the first thing worth building.