No-Code, Self-Service Analytics

When users perform a stream of behaviors, how impactful would it be to predict what they will do next? How much easier would your life be if you could understand, within a reasonable margin of accuracy, what a cohort of users would most likely convert into?  

Topics: Data Analytics, self-service, no-code

When a group of users are like-minded and do a certain similar behaviour it could be because they have something in common. What they have in common, their behavior,  interests or how they act; can all be variables in the explanation of how a group of users do what they do. 

Topics: Data Analytics, self-service, no-code

Why one small step for big data really is a giant leap for mankind

The thing about mastering analytics is that the “master status” only lasts until the next iteration --just ask any SEO expert after a Google Search algorithm update if they’re still an expert. New technologies 

Topics: Data Analytics, quality data, Smart Analytics, Mobile, Podcast

When a shopper has a flawed customer experience, they are far more likely to abandon their carts than explain where the user interface went wrong.

If there’s any profession that needs regular gut-checks, crisis advice, and perpetual professional development, it’s product managers. It’s a high-stress business that can be isolating--meaning

Topics: AI, Data Analytics, Product Management

COVID-19 has rocked the business world in many ways, one of them being in-person events; conventions, conferences, and other opportunities for idea sharing are canceled for the remainder of 2020. Luckily, the internet is home to some informative and dynamic forums that can provide professionals with valuable professional

Topics: Data Analytics, Smart Analytics, COVID-19, Trends, Data Science, Forums

Merriam-Webster defines an anomaly as “something different, abnormal, or not easily classified.” In data science, anomalies usually create a need to research more comprehensive contextual databases to understand the root cause--in other words, a lot of

Topics: AI, Data Analytics, Smart Analytics, Trends, Anomalies

(The Facts About User Behaviors You Can’t Miss — Blog 5 of 5)

In our last blog, we looked at data analysis on generational preferences and consumer behavior patterns for Generation X and Generation Y (more commonly known as Millennials). 

Generation Z, however, is a bucket all its own mainly because a significant portion of this demographic is still too young to be independent consumers of tech and other products and services. But with the growing power of predictive and Augmented Analytics, early user behavior

Topics: AI, Data Analytics, Mobile, Trends, User Behavior, Social Media, Gen Z

(The Facts About User Behaviors You Can’t Miss — Blog 4 of 5)

Topics: Data Analytics, Smart Analytics, Mobile, Trends, User Behavior, Social Media, Gen X, Millennials

An interview with Yuanming Shan, SVP of Analytics and Data Science at Smule

Topics: Data Quality, Smart Analytics