Are you receiving more feedback than you could ever read, let alone summarize? Maybe you’ve used text analytics methods to analyze free-form textual feedback? These methods range from simple techniques like word matching in Excel to neural networks trained on millions of data points.
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Are you responsible for measuring the progress in improving customer experience? If yes, I’m sure you needed to come up with a rationale on which metrics to choose for this: Is it an all ubiquitous Net Promoter Score (NPS), the traditional customer satisfaction CSAT, or a more recent invention Customer Effort Score (CES)?
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Over the past few weeks, we’ve reviewed several Text Analytics approaches to feedback analysis: word spotting in Excel, manual rules, text categorization, and topic modelling. Now, it’s time for Thematic Analysis.
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Last year, in 2017, my co-founder signed us up for the YCombinator’s Startup School (SUS). 10 weeks later, our lives had changed completely. We were admitted into the YC’s core program, moved to Silicon Valley and were growing faster than we could onboard customers. This year, YCombinator launched the second Startup School. And since my picture is listed on their homepage, I have received several questions about how to make the most out of this…
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Let’s bring some clarity to the messy subject of Advanced Text Analytics, the way it’s pitched by various vendors and data scientists. This is the 3rd of a series of 5 posts, where I explain what’s popular and what’s possible.
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My part 1 of 5 article last week covered Word Spotting, a simple approach to Text Analytics that anyone can implement in just 10 minutes in an Excel spreadsheet. We briefly reviewed how ineffective it can be.
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Like most Ukrainians, my dad is a spirits aficionado. One of his favorite things to do while drinking is to read the tasting notes printed on the labels of cognac or whisky bottles.
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Many companies collect vast amounts of customer data but simply don’t know how to derive actionable, meaningful insights from it. Is this your company? The key word being meaningful, as insights can be plenty but unless you can put them into the right context it will be meaningless for your business.
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What makes a cartoon caption funny? As one algorithm found: a simple readable sentence, a negation, and a pronoun—but not “he” or “she.” The algorithm went on to pick the funniest captions for thousands of the New Yorker’s cartoons, and in most cases, it matched the intuition of its editors.
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Last week, ThePointsGuy published the 2018 “The Best And Worst Airlines In America”. According to Forbes’ interview with Brian Kelly, the author of the report, 9 airlines were reviewed using 10 objective criteria. Here, we extend this report by adding the element of customer perception according to online reviews.
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