Terminology
IoT analytics refers to the use of data analysis platforms to find value in large volumes of data generated by IoT devices.
Terminology
Predictive analytics is a form of advanced analytics that uses current and historical data to make predictions about future outcomes and behavior.
Terminology
Interactive analytics is a way to make real-time data more intelligible for non-technical users through the use of tools that visualize and crunch the data.
Terminology
A key component of business intelligence, a semantic layer translates complex data language into simple, familiar terms like “product” or “revenue.”
Terminology
Operational analytics is the practice of utilizing data in real-time to make instant decisions in business operations.
Terminology
IT monitoring helps analysts measure the performance of their IT equipment to identify and respond to any problems.
Terminology
A tech stack is a combination of all the frameworks, language tools, and software used to create and run a particular application. It’s also known as a solutions stack or data ecosystem.
Terminology
Businesses can evaluate a customer's lifetime value (CLV), customer's loyalty and future expected revenue with survival analysis.
As AI, IoT, and data privacy regulations continue to evolve, there is tremendous potential for consumer-focused industries to transform the way they interact with customers. In a privacy-first DX 2.0 economy, a brand's success depends on its ability to quickly generate comprehensive 360° customer profiles, analyze data from multiple channels, and deliver dynamic and hyper-personalized experiences in real-time.
The future of privacy compliance is still in limbo, but one to keep tabs on. President Biden’s recent executive order may have laid the framework for a new era of transatlantic privacy compliance, but it will likely be several months before the framework receives EU regulator approval, let alone the enviable legal challenges to follow. In the meantime, the stakes have never been higher for transatlantic brands.
Customer data platforms (CDPs) help businesses aggregate and analyze customer data from multiple channels. As brands interact with consumers through various touchpoints, the CDP cleans and unifies the data to build more complete customer profiles. But getting a true 360° view of user behavior remains a challenge.
Time-series data is everywhere—whether or not your brand is equipped to handle it. Data-driven organizations need time-series analysis platforms to make the most of their data, but some brands may not realize there are different techniques for achieving time-series analysis. The question isn’t whether time-series analytics platforms are worth it—they are—but knowing which analysis technique is best suited for your brand goals and needs.
Explore what a true self-service customer experience analytics platform can do for your business.
Learn how Bleacher Report uses Scuba to rapidly gain insights into user behavior, increase conversion and engagement, build satisfaction, and redesign their web and mobile applications.
Learn how Scuba is helping Edmunds enable self-service analytics for business users, analysts and executives - faster than complex SQL-based queries.
One of the largest telecomms in the world set an ambitious goal – to dramatically improve their customer experience. They wanted a real time customer experience analytics solution to tackle changing consumer demands and growing competitive pressures.
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