Data Panda brings your Mailchimp 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.
Mailchimp was founded in Atlanta in 2001 as an inexpensive alternative to enterprise email software and has grown into a full marketing platform covering email campaigns, marketing automations, audiences, landing pages and a light CRM. Intuit acquired it in 2021, which folded it into the same toolbelt as QuickBooks and TurboTax. Millions of small and mid-market businesses use it as their main channel to paying customers.
The point of pulling Mailchimp into a warehouse is not that Mailchimp lacks reports. It is that opens and clicks are only the first half of the story. Next to your Shopify orders, your Stripe charges or your CRM deals, the same Mailchimp campaign has a second number attached to it: how much revenue did that email produce, per segment, per product, per cohort. That number lives outside Mailchimp by definition.
The email number next to the order number, per campaign and per cohort.
Keep the list in sync with the system that decides who your customers are.
Turn years of campaign history into scoring that makes the next send smarter.
Internal tools for the marketing team that Mailchimp's own UI does not ship.
A list of concrete reports, automations and AI features we have built on Mailchimp data. Pick the one that matches your situation.
Which email campaign paid for itself?
Campaign send cost next to attributed paid orders in the fourteen days after delivery, broken down by segment and product category. Ranks the campaigns whose revenue covered the send, the design time and the discount code, and flags the ones that just moved existing demand around.
Which segment is quietly dying?
Active-to-inactive drift per audience segment over the last twelve months, with open and click trend on the same chart. Surfaces the segment that still gets sends but no longer responds, before the deliverability score notices.
Are our automations doing more work than our campaigns?
Revenue per send from triggered automations against revenue per send from one-off campaigns. The automations that a team set up two years ago and forgot often turn out to be the biggest quiet contributor, which changes where attention is worth spending.
Campaign cost in the same picture as attributed revenue, per segment and per cohort. The marketing budget stops being defended on opens and starts being defended on paid orders in the weeks after the send.
Audience segments that are your best accounts, kept in sync from HubSpot, Salesforce or Pipedrive. Lifecycle emails are triggered by real pipeline stage, not by a list that someone last cleaned in 2023.
List health, bounce rate, complaint rate and segment drift over twelve months. Deliverability is monitored as a trend, not rediscovered the day a provider starts rate-limiting you.
Shopify customers land in the right Mailchimp audience automatically, with order history, lifetime value and last-order date attached. Post-purchase flows, winback campaigns and loyalty tracks run on the real buying signal instead of a quarterly export.
A contact's HubSpot lifecycle stage and lead score push into Mailchimp as merge fields and segment membership. Marketing stops running parallel tag systems in two tools and starts treating HubSpot as the source of truth for who gets what.
Account tier, owner and renewal date from Salesforce flow into Mailchimp as audience segmentation. Enterprise accounts receive the executive newsletter, at-risk renewals get a save-our-account sequence, and the split stays aligned to the CRM without a monthly CSV.
When a Pipedrive deal moves to a named stage or goes stale, the right Mailchimp campaign fires: onboarding for new customers, reactivation for deals that paused, or a targeted sequence for deals at risk of ghosting. Sales and marketing stop chasing the same list in two tools.
These are the 3 tables we currently pull from Mailchimp into your warehouse. Query them directly in SQL, join them to the rest of your stack, or build reports on top.
Missing a table you need? We can extend the sync. Tell us what is missing and we will build it for you.
You keep the reporting tool you already have. We connect it to the warehouse where your Mailchimp 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 Mailchimp account lands in its own schema, with a shared contact and audience dimension on top. That makes brand-level reporting and group-level reporting both possible without forcing a single audience model on every brand.
New merge fields land in the warehouse as new columns on the next run. Renamed or removed fields are kept historically so old campaign reports stay readable. Audience restructures are tracked as slowly-changing dimensions, which is usually the difference between a campaign post-mortem that works and one that silently reports against the wrong segment.
We review your Mailchimp setup and the systems around it. Together we pick the first thing worth building.