As a data engineer: 10x your productivity by being able to focus on what actually creates value
Based on a decade of experience, we understand your needs:
- Accurate event data, ideally with guaranteed quality
- Data models optimized for data pipelines
- No unexpected schema changes
Our product is built on the belief that data engineers should not be spending 90% of their time trying to fix data that should be fixed at the source. We provide analytics / clickstream / event data like SaaS. With our Data as a Service (DaaS), everything is done for you:
- We collect your event data (implementation included)
- We monitor and validate your data, handle any issues
- We optimize your data for usability
We guarantee quality event data to support your use cases.
Fixing data issues downstream is exponentially more expensive
A high-quality implementation to collect high-quality data costs more than a low-quality one, but dealing with low-quality data incurs even more costs downstream.
You have probably heard of “garbage in, garbage out” (or GIGO), which further contributes to the problem: The further downstream you move, the less likely it gets that you can actually fix your data.
That’s why our platform includes:
- Data models optimized for data science use cases
- Schema enforcement and data contracts
- Constant adjustments to evolving environments
- Event data based on best practices from a decade of experience
A new and modern approach to collecting and using event data: Single Source of Truth by design
From a data architecture perspective, analytics tools, data stacks, and marketing technologies are all just data pipelines that consist of the same components:
- Creating / collecting data
- Processing data
- Storing data
- Using data / creating value
Instead of having all these solutions create redundant, usually inconsistent, and often low-quality data, it’s better to create data only once and focus on its quality.
With our DaaS, you can stream the same data into different MarTech tools, analytics solutions, and data pipelines to ensure overall consistency and remove redundancies.
Enabling all your use cases that require quality event data
- Machine learning and artificial intelligence
- Marketing spend allocation / ROI optimization
- User journey and user behavior analytics
- Product analytics / user experience analytics
- Dashboards and reports, e.g. for management
- One 360 degree view on the customer or user
- Ecommerce analytics, e.g. checkout funnels
- Conversion rate optimization (CRO)
- Churn reduction
- Customer activation
- Dynamic pricing
- a/b/n testing and personalization
Got a use case that isn’t listed here? Please let us know.
Data optimized for data science, machine learning, and AI
The data we produce for you is suitable for rather basic data use cases, but supports advanced use cases as well.
Because our DaaS is one single data stream, we put a lot of effort to assure its general compatibility and versatility.
Our data is optimized for programmatic consumption first, so any data point that can exist on its own is kept separate, for example.
You can use the transformation features of our DaaS platform to adjust the data to your needs, e.g. localizing, modeling for specific use cases, or preparing for specific analysis.
Stop worrying about data collection and quality: We’ve got it covered
We focus 100% on collecting the best data possible and covering as much of the user journey as possible. Unlike agencies, we don’t analyze the data, and unlike analytics vendors, we don’t store any data and use it.
Our sole focus is the data and its quality, so we constantly adjust your implementation to technological changes, and integrate the data from various sources:
- Websites, SPAs, PWAs, etc.
- iOS, Android, smart device apps
- Ads, online & offline user journey
- CRMs, emails, SMS, chats, calls, etc.
Spend less time on tedious data tasks and focus on creating value through your use cases.
Your data and its quality are constantly monitored
We have been collecting high-quality event data for over a decade. Our DaaS platform incorporates everything we have learned along the way to ensure your data is accurate. These are just some of the features:
- Data schema validation
- Data schema monitoring and alerting
- Data transformations and customizations
- Ability to apply custom business logic to the data
- User identity resolution
- PII pseudonymization and anonymization
Our platform covers the entire data creation process.
What Cape.ly does
- Collect event data and provide it to downstream consumers
- Work with your IT on our highly automated implementation
- Guarantee the data quality based on 10+ years of experience
What Cape.ly doesn’t do
- Store data long-term or provide analytics features
- Read from downstream consumers or do reverse ETL
- Not an agency, a data lake, a CDP, or an analytics tool
Data as a Service
Focus on creating business value with data you can trust and build on.
Stop worrying about data issues. Use your tools to their fullest potential.
Let us take care of everything, based on our 10+ years of experience.
Your goals are automatically our goals due to our business model
You need high-quality data continuously, not just now, because data use cases take time to implement and usually get more profitable over time.
Our business model is built around long-term partnerships. Everything we do is thought through and meant to work for a long time.
We have learned from a decade of experience that quick fixes, workarounds, and makeshift (or band-aid) solutions don’t last.
We have huge initial implementation efforts, so if we don’t earn your business for a second year, we lose money. This sets us apart from other service providers.
Obsession for quality and more than a decade of experience
Hey there, my name is Ian and I am the founder of Cape.ly.
For over a decade now, I have architected analytics implementations and debugged them at the network and source code level to deliver the best event data possible.
My work with medium-sized to large companies in North America and Europe has provided me with a wealth of experience and a unique combination of traits:
- Stereotypical German obsession for quality
- Stereotypical Canadian kindness
- Company in the business and data hub New York City
I believe very few have as deep an understanding of all the client-side and server-side technological details that affect data quality and reliability as I and other team members do.