In legal/compliance: Make sure that all of the company’s data teams and data tools are compliant
Based on a decade of experience, we understand your needs:
- Data must be compliant with legal rules and regulations
- The legality of data must not be an afterthought
- Most tools should not be allowed to collect their own data
Our product is built on the belief that legal compliance should be built-in and not an afterthought due to the risks that it creates for the business. 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 manage compliance and sharing with other tools
We guarantee quality event data to support your use cases.
Keep your data compliant with privacy rules and regulations
Your data is one of your biggest assets, but could also be one of your biggest liabilities due to legal compliance requirements.
Reduce your risk with our legal compliance framework that covers North America and Europe. We also support a variety of Consent Management Platforms.
Coming originally from Europe, we have many years of experience dealing with strict privacy laws. That’s why, with just a few clicks, you can set up:
- Cohort-based analytics
- and more!
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.
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
Everything on the left is taken care of for you. The tools and use cases on the right show how you can use our DaaS to create value.
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
- US business based in fast-paced NYC
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.
When aiming to maximize conversion rates, increase average cart values or set individual prices for products and services, optimization and personalization efforts require data.
Identifying what drives conversions allows funds to be allocated efficiently. Consent-aware cohort-based or fully anonymous tracking can provide a full picture.
Most companies accept churn and are only trying to reduce future loss. However, there are usually clear indicators that customers may churn that can be used to prevent it.
Instead of having many tools create redundant, usually inconsistent, and often low-quality data, it’s better to create data only once and focus on its quality.
“Garbage in, garbage out” is a huge problem, but the costs increase exponentially, because the further downstream the more effort it takes to fix data, if it’s even possible.
Instead of admitting that the implementation is not right, data teams often blame the tool. However, a new tool without an improved implementation won’t produce better results.
Even though most never do, some new implementations may produce quality data at first. However, it requires constant effort to maintain that level of quality.
Due to more focus on privacy, browsers and mobile apps make it increasingly difficult to collect behavioral data, which requires sophisticated data collection strategies.