Over the years, I've seen a massive struggle with adding digital data to existing or new data science initiatives. The challenge here is that digital data holds an incredible amount of value in today's world and without it, data science will suffer. Whether the data is an input for attribution (any flavor), scoring models, machine learning, or otherwise, the challenges always tend to remain the same:
- Lack of a data model and schema
- Accuracy issues or missing key elements of data
- Inability to structure the digital data output in a usable format
- Inability to deliver timely data for models that require more immediate inputs
- Lack of individual level data
- Inability to build cross-session attribute tables at an individual level
Tag-based solutions, or those that rely on a data layer as a primary input, require advanced configuration in order to ensure that the micro-interactions within a particular page or experience are not only captured, but also tied to the session and to the individual. Even the most complex implementations, in my experience, struggle with the cross-session and device stitching of the individual. For data science, this presents as a core challenge given that many of these micro-interactions are required as signal inputs to the models being developed.
Furthermore, data captured without a relational structure are rendered relatively useless for data science without a heavy amount of costly transformation and joining of data downstream. I cannot stress enough about the importance of having a data model and schema readily available from whatever vendor you are using to capture digital data. It's a gap in the industry, generally speaking, and something that you won't realize is an issue until you try to connect that data from your marketing cloud or tech stack to an external system or vendor.
So if you're a Data Scientist or analyst who would love to leverage your valuable digital data in all data science initiatives, imagine for a moment the value to your organization, and how easy your job would become, if you could do it like this:
- Zero tagging – instead instant, tag-free capture of all micro-interactions
- 80% reduction in the time you spend completing the usually arduous data prep task
- Easy access to a complete data model and schema processed in milliseconds for your use downstream, which you can expand upon as you see fit
- Instant stitching across sessions and device
- Built-in compliance with all browser regulations, such as ITP, to ensure your attribution models and visitor identities persist beyond 7 days
- Ability to connect your data in a format of your choosing, using pre-built connectors for data science and machine learning out-of-the-box
- Automatic capture of individual digital profiles for all visitors to your digital properties
- Ability to structure outputs, have them available in milliseconds, in any format of your choosing based upon the particular models being built
- Rapid understanding of your digital behavioural data set to reduce time to value
Enter Celebrus for Data Science. A data capture solution that combines 20+ years of passion, deep domain expertise in data capture, unique patented technology processes which is quietly embedded in organizations who realized that there is gold in their customers' digital data. Global brands you know include large retail banks, airlines, insurance companies and major retailers who are using Celebrus to feed their data platforms and decisioning systems like Teradata, Pega and SAS. Why? Because they crave truly 'real-time' data which enables them to deliver highly personalized, relevant and 'in-the-moment' customer experiences. All this is achieved while prioritizing customer privacy and simultaneously meeting data compliance regulations, thereby safeguarding their organization's reputation. That is the true Celebrus advantage.
So, why settle for less? Please reach out to start a conversation and learn how you too could evolve your approach using our patented data capture technology.