Feedback Analysis

Clothes hanging on a rack

What we learned from analyzing 350K customer reviews of major fashion brands Members Public

We analyzed 350K customer reviews of several high profile fashion brands to discover insights and learn how they're perceived by the public.

Alyona Medelyan PhD
Alyona Medelyan PhD
Customer Experience

3 best practices for coding open-ended questions Members Public

Open-ended survey questions often provide the most useful insights, but if you are dealing with hundreds or thousands of answers, summarising them will give you the biggest headache. The answer lies in coding open-ended questions. This means assigning one or more categories (also called codes) to each response. But how

Nathan Holmberg
Nathan Holmberg
AI & Tech

Visualizing customer feedback: 3 alternatives to word clouds Members Public

I've previously written about why word clouds suck. Is there a better way of visualizing customer feedback? Yes, there is, and the best thing about it is, you can even use Excel to create these visualizations – if you represent the data correctly. In this post, you will learn three simple

Alyona Medelyan PhD
Alyona Medelyan PhD
Data analytics

Why you need to mine positive scores for negative themes Members Public

Your survey results look awesome. The scores are high and customers seem generally happy. Most survey responses have top-box ratings, which means the customer selected one of the two highest rating options. Nothing to worry about, right? Not so fast. There might be a hidden danger lurking in those positive

Agi Marx
Agi Marx
Customer Experience
Tui billboard with a sarcastic slant

Sarcasm in customer feedback – how common is it? Members Public

Most people believe that text analytics solutions fail because sarcasm in customer feedback is very common. Somebody writes “Great service, yeah right!” and the dumb algorithm tags it as positive. So, whenever I speak on text analytics, someone in the audience will always ask: But how do you deal with

Alyona Medelyan PhD
Alyona Medelyan PhD
Feedback Analysis

Why word clouds harm insights Members Public

The picture above depicts Paul McCartney wearing a mullet [https://upload.wikimedia.org/wikipedia/commons/3/32/Paul_and_Linda_McCartney.jpg] . This article criticizes word clouds,the mullets of the Internet [http://brittbrouse.com/2011/10/14/do-you-agree-that-word-clouds-are-the-mullets-of-the-internet/] . :-) > “Every time I see a word cloud presented as insight,

Alyona Medelyan PhD
Alyona Medelyan PhD
Feedback Analysis

Actionable insights: can data analysis software deliver them? Members Public

When it comes to making sense of data, getting actionable insights is the holy grail. But what does this even mean? When is a finding an insight? When is an insight actionable? Can data analysis deliver them? Let’s get to the bottom of this by looking at some examples.

Alyona Medelyan PhD
Alyona Medelyan PhD
AI & Tech
Woman conversing with a chatbot on a giant phone

8 natural language processing (NLP) examples you use every day Members Public

What is Natural Language Processing, or NLP in short? If you’re unsure, you’re not alone. Many people don’t know much about this fascinating technology, and yet we all use it daily. In fact, if you are reading this, you have used NLP today without realizing it. What

Alyona Medelyan PhD
Alyona Medelyan PhD
AI & Tech

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