I attended IBM’s inaugural Think event in Las Vegas last week. This event, IBM’s largest (estimated 30,000+ attendees!), focused on making your business smarter and included keynotes and sessions on such topics as artificial intelligence, data science, blockchain, quantum computing and cryptography. I was invited by IBM as a guest to share some insights from […]
As users/customers engage with a company (their products, services, surveys), they generate a lot of data about their behaviors and interactions with the brand. Predictive analytics and artificial intelligence capabilities provide a way to extract insights from that data to help you improve the customer experience and optimize customer loyalty. Artificial Intelligence and Predictive Analytics […]
A recent survey of over 16,000 data professionals showed that the most common challenges to data science included dirty data (36%), lack of data science talent (30%) and lack of management support (27%). Also, data professionals reported experiencing around three challenges in the previous year. A principal component analysis of the 20 challenges studied showed that challenges […]
Over 16,000 data professionals were asked to indicate their favorite data science blogs, podcasts and newsletters. The top two favorite blogs were KDNuggets and R Bloggers. The top two podcasts were Becoming a Data Scientist and The Data Skeptic. The top two newsletters were O’Reilly Data and Data Elixir. Data professionals use a variety of […]
A recent survey of over 16,000 data professionals showed that the most used platforms/resources included Kaggle, Online courses and Stack Overflow Q&A. Additionally, the most useful platforms/resources included Personal Projects, Online courses and Stack Overflow Q&A. On average, data pros used around three (3) different platforms/resources to learn data science skills. There are many ways […]
Data science requires the effective application of skills in a variety of machine learning areas and techniques. A recent survey by Kaggle, however, revealed that a limited number of data professionals possess competency in advanced machine learning skills. About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression […]
Data science methods and related tools (i.g., predictive analytics, machine learning) can help companies improve their customer success programs by answering 5 important questions about customer churn, including what is the current churn/retention rate (e.g., descriptive analytics), who is at risk for churning (predictive analytics), what actions can prevent churn (i.e., prescriptive analytics) and more. […]
The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals […]
Learn how to build your business around the customer using customer-centric measurement and analytics. Available in Kindle and paperback. Learn more about TCE: Total Customer Experience.
Beyond the Ultimate Question: Book includes comprehensive review of Net Promoter® Score (NPS) and presents better measures of customer loyalty and best practices of customer feedback programs.
Measuring Customer Satisfaction and Loyalty (3rd Ed.): Book includes discussion of how to measure the customer experience, including customer satisfaction and customer loyalty.