Customer.io isn't just an email tool. It's your engagement layer.
We connect product data, run multi-channel lifecycle programs, and measure what worked.
Start where you are
Moving to Customer.io
“We need to get off our current platform without interruptions.”
Migration, Data model mapping, Parallel run, Transition
We rebuild your programs in Customer.io and transition without interruptions.
• Audit of the existing engagement platform
• Data model mapping
• Journey and template rebuild
• Parallel run across both platforms
• IP and domain warmup
• Post-launch monitoring
Migrate from: Iterable, Braze, Marketo, Klaviyo, HubSpot, Salesforce Marketing Cloud, Intercom, and Mailchimp
“We bought Customer.io months ago and it’s still half set up.”
Implementation, Workspace architecture, Deliverability
Stalled implementations are usually a data model problem, not a scheduling problem. We build the structure so it holds up once your team starts shipping.
• Data model design: people, objects, and relationships
• Workspace and environment structure
• Sending domain and subdomain setup
• Deliverability foundation
• Permissions and governance
“Our product usage data never makes it into Customer.io.”
Data layer, Event schema, Pipelines, CDP
This is the foundation to a strong behavior-based lifecycle program.
• Event schema design
• Warehouse and pipeline architecture
• CDP integration: Hightouch, Segment, RudderStack
Live but underutilizing it
“Our journeys are Day 1 / Day 3 drips. Every build needs a developer.”
Program design & build, Creative ops, Multivariate testing
We map the golden path, design the triggers, and build the programs. Then we build the layer that makes it repeatable, so the team ships without a build queue.
Behavior-based programs, across five stages
• Activation and onboarding
• Free-to-paid conversion
• Expansion and upsells
• Churn prevention
• Winbacks and recovery
Creative operations
• Reusable Liquid components
• Dynamic content blocks
• Modular drag and drop design system
• AI-assisted multivariate testing
522% increase in trial user activation. Scout OS.
“We’re only sending email.”
Multi-channel, In-app, Push, SMS/MMS, Webhooks
Multi-channel is one of the main reasons a PLG company picks Customer.io. Most teams use a fraction of it.
• In-app messages
• Push
• SMS and MMS
• Webhooks
• Transactional
Short on resources to own and scale
“We have the platform and no one to run it.”
Ongoing management, Optimization, Scale
We own the engagement layer on a continuous cycle, and keep the instance healthy while we do it.
Running it
• Roadmap
• Builds and QA
• Optimization and reporting
• Implementation sprints that add journeys as your product evolves
Keeping it healthy
• Journey and campaign inventory
• Dead flow retirement
• Segment and profile hygiene
• Message volume and cost review
• Inbox placement and reputation
“We can’t prove any of this is working.”
Measurement, HoldoutS, Incrementality, Attribution
Reporting that ties lifecycle programs to ARR instead of opens and clicks.
• Holdout design
• Incrementality testing
• Warehouse-side attribution
• Product analytics: Mixpanel, Amplitude, PostHog
The foundational layer that fuels
behavior-based engagement
Event schema design
The events your programs trigger on, defined properly.
Warehouse and pipelines
Architecture and a CDP that actually delivers.
Hygiene and validation
So the triggers hold up.
Trigger on each user's behavior.
Events named and structured so any program can use them.
Most data models are added a feature at a time, by whoever shipped the feature. That leaves three names for the same action, properties that exist on some events and not others, and signals that mean different things depending on which team instrumented them. Lifecycle work then starts with a week of figuring out what an event really means before anything can be built on it.
We define the event schema around the moments that matter in the lifecycle: activation, conversion, habit, expansion, risk. Every event arrives with consistent naming and the properties a program needs to act on it. New campaigns reuse what’s already built instead of submitting a request for new tracking.
Ship in days, not quarters.
Product data should reach Customer.io in minutes, without an engineering ticket.
Most lifecycle teams can describe the program they want but can’t build it, because user behavior that should trigger each touch point is sitting in a warehouse without a path into the engagement platform. So the idea gets shelved, or it ships as a time-based drip that's the same for every single user. That’s the gap between behavior-based lifecycle that drives incremental revenue and what’s in place today.
We architect data models and Hightouch syncs that move product behavior into Customer.io continuously. Trial milestones, usage thresholds, account-level signals, all available as attributes and events in days. We shape data models around lifecycle strategy, so each user moves through a journey built on what they did in product.
Automate more. Check less.
Clean data is what lets a lifecycle program scale.
Programs trigger when they should, the right users get the right message, and nobody is checking sends before they go out. That’s the difference between running six programs and running two hundred and six. Most teams cap lifecycle pograms not because they’re out of ideas, but because they don’t fully trust what’s feeding the triggers, logic, and personalization.
We validate every event, field, and identity lifecycle programs depend on before launch, then keep validating as the product and the journeys change. Deduplicated profiles, consistent event naming, and alerts when a signal stops syncing. A broken trigger surfaces in hours, not a quarter later, because we’re the partner running programs that depend on it.
Three ways to partner. Each starts with an assessment.
The Retainer
Outsourced Lifecycle Team
Structure
Monthly recurring retainer.
Scope
We become your dedicated lifecycle marketing infrastructure unit.
Best for
Teams that need a permanent partner to own the attributed revenue.
The Project
Implementation Sprints
Structure
Fixed scope, short timeline.
Scope
A high-intensity overhaul to build your V1 stack, fix broken pipelines, or launch your essential lifecycle flows.
Best for
Early-stage startups needing a fast infrastructure build or migration.
On-Demand
Technical Support
Structure
Ad-hoc support.
Scope
Unblock specific engineering issues. Complex logic, deliverability audits, schema design reviews.
Best for
Teams with internal marketers who get stuck on complex technical hurdles.
Jobs 04, 05, 06, 07
Jobs 01, 02, 03
Any single job
What shapes the number
- Current platform and target stack
- Contact and profile volume
- Number of journeys and automations
- Number of templates to migrate or build
- Data model complexity and warehouse state
- Channels in scope
- Engineering needs
Two situations where we may recommend you to another team
You're not on Customer.io and aren't planning to move.
We'll point you to a partner who works in your platform.
You need a one-off email design, a single campaign built, or a design system.
That's a freelancer or a specialist designer. We'll refer you to someone we trust.
Frequently asked questions
Additional details you may want to know.
Can you take over an instance we've already built?
Yes, and it's most of what we do. We audit what's there, keep what works, retire what doesn't, and document the data model before we get started.
How long does a migration take?
It depends on profile volume, journey and template count, data quality, and third-party integrations. We give you a timeline during the assessment, before you commit to anything.
We already work with a lifecycle freelancer or agency.
Good ones handle creative and campaign execution well. The question is whether they can build the orchestration layer. Most can write emails but can't engineer the event schemas and CDP integrations that make behavior-first messaging work. So the part that actually blocks you is still sitting in the engineering queue.
We have an internal marketing team.
Internal teams have context and continuity we'll never match. What they don't have is engineering capacity, because they're competing with the product roadmap, and product wins. That's structural, not a people problem. We bring our own engineering layer so your team can own strategy and brand.
Can't we just hire a data engineer?
You could, but most data engineers build analytics and dashboards for internal stakeholders, not marketing pipelines tuned for ESPs and CDPs. They speak SQL, not Customer.io. That hybrid skillset is hard to find.
Can't we just use the AI features already in Customer.io?
Those are good at optimizing delivery, send times, and basic personalization. They work with the data you've managed to get into the platform. The constraint isn't the AI, it's what's feeding it. We build the layer underneath.
Do you build inside Customer.io, or strategy only?
We build. Automations, segments, templates, Liquid, integrations, and the data pipelines. The strategy exists to direct the build, not to replace it.
Will our deliverability take a hit?
Our process is built to protect it. IP and domain warmup, staged sending, and inbox placement testing before and after.
Can we handle part of the migration ourselves?
Yes. Plenty of teams want to own their own data model. We scope around the pieces you'd rather keep in-house.
Can we continue our current platform during migration?
Yes. We can cut over one program at a time, or wait and do a full switch. That's a decision we make together during planning.
What do you need from us to start?
Read-only access to your current stack, a list of the programs that matter most, and someone who can answer questions about the data model. That's enough for the assessment.
Start with the assessment.
Read-only access, an independent review of the setup, and findings back within a week. If we're not the right partner, we'll tell you and point you somewhere better.