Kategorie: Product analytics

Posted in Analytics, Data careers, Data education, Data quality, Product analytics

Solving the speed vs. quality experimentation dilemma and growing the New York Times- an interview with Shane Murray

Contextualizing our world with data, part 4: Journalism. Solving the speed vs. quality dilemma and growing the New York Times, also during the Trump years. Shane Murray, Field Chief Technology Officer at Monte Carlo and former Senior Vice President of data & insights at The New York Times, talks with about experimentation and growing a digital subscriber business, the New York Times. Shane talks about how to solve the experimentation speed vs. quality dilemma – and often outright conflict – between business stakeholders and data teams. Shane also talks about how the New York Times transformed itself into a digital subscription product and tech company.

Posted in Business, Data Leadership, Data strategy, Data-driven marketing, Immigration, Insights, KPIs, Product analytics, Strategy

Fall in love with the problem, not the data – an interview with Mor Eini

Mor Eini’s career started in the Israeli Defense Force in the Office of the Prime Minister and took her to the VC ecosystem in Berlin. Mor Eini from APX, which is an early stage investor, explains how she evaluates a startup’s use of data. Mor also talks about the Israeli and Berlin ecosystems. She also shares her insights as a B2B investor on how data is a tool to create, foster, accelerate innovation, but data is not the innovation.

Posted in Digital product management, Insights, Product analytics, Strategy

Babbel Live’s data-informed success: How early stage digital products can be hypothesis driven despite little data

Babbel Live is a success story that product managers who are launching new digital products can learn from. Massive amounts of data are not necessary in order to use data to make good decisions. Starting simple and working in a data-informed way to prove or disprove hypotheses can yield positive results quickly.

Posted in AI use case, Data-driven marketing, Product analytics

Customer Segmentation: Rules-based vs. K-Means Clustering

Customer segmentation is a means by which you group customers into an identifiable category that you can use as a basis for analysis of a specific group of customers. Customer segmentation is useful for activities such as strategic planning, campaign planning & customer targeting, product analytics, planning customer communications, customer experience management, churn prediction, upselling, cross-selling, acquisition, sales operations and more.