This week’s roundup discusses predictive analytics terms in plain English, why waiting for 95% statistical significance might be hurting your campaigns, measuring the ROI of data quality, how to eliminate customer data silos, and the benefits of using constrained optimization with predictive analytics.
Predictive Analytics Terms Business People Need To Know (No Hype Allowed)
by Meta S. Brown, Author of Data Mining for Dummies, featured on Forbes
Terms arise to fit a particular purpose, and when they become popular, their meanings change. No matter what type of analytics you’re talking about, there’s no reason it can’t be explained in plain English. Predictive analytics is a major hype zone. This article breaks down some popular analytics terms into language that is easy to understand.
There is Nothing Magical About 95% Statistical Significance
by William Gadea, Founder and Creative Director of IdeaRocket, featured on CXL
You are probably ending your A/B tests either too early or too late. The standard best practice in the conversion optimization industry is to wait until two conditions have been met before ending an A/B test. First, that a representative sample is obtained. Second, that the winner of the test can be declared with 95% certainty or greater. But is waiting for 95% certainty really the best approach?
Better Data Quality Equals Higher Marketing ROI
by Larry Myler, Founder and CEO of By Monday, Inc, featured on Forbes
The desire to be a data-driven organization is high, but the majority of businesses today acknowledge that they are far from where they ought to be. The challenge, it turns out, is often the inability to justify the costs associated with analytics implementations and ensuring accurate data collection. With zero-based budgets becoming the norm, executives are expected to demonstrate a predictable return on investments in enhanced data measurement and quality.
5 Ways to Eliminate Customer Data Silos
by Indrajeet Deshpande, Freelance Contributor, featured on MarTech Advisor
Often when large organizations work with independent teams, data silos are inevitable. Silo-ed data makes it difficult for marketers to present the right experience that customers expect, as they are not able to tap into the whole picture of the customer journey. So, how do you overcome data silos?
Empower Your Business with Constrained Optimization and Predictive Analytics
by Andrea Steffes-Tuttle, Director of Marketing at Lityx
In this article, we dive into predictive analytics and explore how combining predictive models, predictive optimization, and constrained optimization techniques allow marketers and analysts to make better decisions and improve efficiency.
Did you see an interesting article in the last week? Share it with us! Send it to astuttle [at] lityx.com.
