analytics

Truly Understanding Minimum Detectable Effect

Now you know how to Predetermine Your Test Sample Size and How to Reach Statistical Significance & Power within Your Experimentation Programs. Our final blog concludes the series and will expand your understanding of how to use Minimum Detectable Lift, or Minimum Detectable Effect for optimum test design and results.

Economists predict e-Commerce sales will range from $427 – $443 billion in 2017, a growth rate of three times higher (8-12 percent) than the entire retail industry.  This upward trend will continue. In fact, Business Intelligence forecasts consumers will spend $632 billion in 2020.

5 Must-Read Optimization Posts for the Week of July 29

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Marketers should be more sophisticated in understanding the nuances of the customer journey, according to a recent post on Monetate’s blog. The company’s research shows that less than half of all purchases happen within the first hour of a shopper’s browsing session—and 16 percent happen about one day after a shopper first visited a site. The takeaway? Think about helping […]

5 Must-Read Optimization Posts for the Week of July 8

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“Ignore desktop at your own peril.” In a recent post on Monetate’s blog, Leo Strupczewski shares highlights from the company’s recent ecommerce report. Among their findings: desktop revenue is increasing as more non-converting traffic goes to tablets and smartphones (cross-reference with our recent post, “Mobile commerce: Should revenue really be your metric?”) and conversions on […]

Mobile Commerce: Should Revenue Really Be Your Metric?

young woman on phone

This is an interesting time for mobile commerce. It wasn’t too long ago that mobile was used mainly for texting, checking email, and getting directions. The thought of using your phone for research or a transaction was a bit absurd… kind of like the idea of a driverless car. Nowadays, mobile accounts for about half […]

5 Must-Read Optimization Posts for the Week of May 23

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When it comes to the decision-making process for mobile customers, perhaps the most important factor is time—that is, how much of it they have. In a recent article for Think with Google, Duke psychology and behavioral economics professor Dan Ariely explains that time pressure—when “our need for time exceeds the amount of time currently available to […]

5 Must-Read Optimization Posts for the Week of April 4

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Interested in building a dynamite data science team? In First Round Review, Instacart’s data science VP explains when you should invest in talent (“Data science only matters if data drives action”), where the team should fall in your org structure, and how to build a data-driven culture. The post is aimed at startups, but any […]