In online sales: Power all your various tools from one high-quality data source you can trust
Based on a decade of experience, we understand your needs:
- Accurate event data, ideally with guaranteed quality
- Data shared across all different MarTech tools
- Coverage of as many sales signals as possible
Our product is built on the belief that online sales should be able to focus on sales 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.
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.
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.
One tool-agnostic, future-proof, and cost-effective implementation for everything
Our implementation is the last one you’ll ever need, and you don’t have to lift a finger. We’ll replace all your current legacy implementations and data collectors with just one.
Free your websites and apps from legacy code slowing them down (maybe even breaking them) to make a lot of people very happy:
- Your users and customers
- Your UX / site reliability team
- Your security and compliance team
The data we collect for you can be used with all kinds of marketing technologies and data stacks.
The difference between DaaS and agencies / consultants
More than a decade of consulting and helping clients with their data issues has made us realize that the approach to collect data to create value is inherently broken.
Most companies work with their service providers on their data collection together. Unfortunately, shared responsibilities don’t work for increasingly complex implementations.
Instead, we recommend to make the data and its quality the responsibility of just one party so that the deliverable can be measured, and somebody can be held accountable.
Our DaaS comes with a data quality guarantee and can be used by you and your partners. We believe that agencies create a lot of value, but data creation requires undivided attention.
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.
You can find more information about past projects on my profiles on Cape.ly and LinkedIn, or on my personal website.
Increase conversion rates through personalization
When aiming to maximize conversion rates, increase average cart values or set individual prices for products and services, optimization and personalization efforts require data.
Optimize marketing spend by measuring 1st-party interactions
Identifying what drives conversions allows funds to be allocated efficiently. Consent-aware cohort-based or fully anonymous tracking can provide a full picture.
Reduce churn by reacting to customer behavior in real-time
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.
Make confident decisions through a single source of truth
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.
Inaccurate source data causes snowballing costs downstream
“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.
Switching to a new analytics solution won’t fix data issues
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.
Don’t treat analytics as one-off implementation projects
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.
Don’t underestimate technical data collection challenges
Due to more focus on privacy, browsers and mobile apps make it increasingly difficult to collect behavioral data, which requires sophisticated data collection strategies.
