As a data analyst: Create value faster with reliable data modeled and optimized for analytics
Based on a decade of experience, we understand your needs:
- Accurate event data, ideally with guaranteed quality
- Data models optimized for analytics
- No unexpected schema changes
Our product is built on the belief that data analysts should be able to focus on creating value and should not have to deal with confusing and/or broken data. 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.
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.
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.
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.
You need to measure everything to answer the why, not just the what
You can’t manage what you don’t measure. And if you don’t measure enough, a lot of questions remain unanswered making it difficult to provide actionable insights.
You may have been given this advice before: Focus on a few important data points and make sure that data is accurate and reliable. We strongly disagree with this limiting approach.
Transactional data answers the what, event data answers the why. So to answer the why, you need a complete picture of what happened, as anything can affect a user’s behavior.
Our Data as a Service aims to cover everything. And if something is missing and technically possible, we add it.
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.
For a flat fee: Ready-to-use, high-quality event data
Analytics vendors offer tools, agencies offer consulting services. We provide the end product, the data, as a service.
With data quality and reliability being our sole focus, we enable you and your stakeholders to focus on your use cases.
Similar to SaaS, our Data as a Service approach is worry-free and about 40% more cost-effective than traditional models.
We integrate seamlessly with your existing software solutions and service providers by taking care of the implementation.
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.
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.