As an HR person: Understand the importance of data quality to attract talent and retain employees
Based on a decade of experience, we’d like to explain:
- Data issues frustrate your best talent and drive them out
- Data issues are used as excuses by low performers
- Data quality has enormous effects on the workforce
Our product is built on the belief that decision makers should be able to make data-driven decisions and not have to worry about the underlying 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.
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.
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.
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.
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.
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
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.