Data Analysis

Statistical packages are powerful, expensive and overkill for most customer feedback work. Excel can do almost everything they can do, and it's already on your desktop. Here's a step-by-step walk-through of analysing survey data in Excel — simple stats, error bars, histograms — with a video to follow along to.

Ask a customer how important price is and you'll get a 10 out of 10 every time. That doesn't tell you anything — customers can't, or won't, tell you what really matters. Here are the two approaches (stated and derived importance) and the four ranking methods, with the trade-offs of each laid out plainly.

Statistical analysis is a vast field, but the bit you actually need for customer feedback is tiny. Master a handful of techniques and you'll outrun ninety percent of the teams working alongside you. Here are the only statistical analyses you need, what they're for, and how to know when to reach for each one.

"I don't believe that NPS — you don't have a big enough sample size." It's becoming the go-to objection from anyone who doesn't like a feedback result. Most of the time it's wrong, and a five-point dataset can be statistically meaningful. Here's what sample size actually means, and how to get past slide one without an argument.

Comparing two survey averages and calling them different (or the same) is the most common mistake in customer feedback reporting — and it leads to killing things that are working and persisting with things that aren't. Here's how to run Student's t-Test in Excel to know whether the change is real, with a step-by-step worked example.

Charles Goodyear ran thousands of rubber experiments — but it was the analysis, not the mixing, that turned them into vulcanisation. Most customer feedback programs do the mixing and skip the analysis. Here are five analysis disciplines that decide whether your feedback drives change or quietly fills a database.