Your Customers Are Talking. Are You Listening?

Algorithm-based sentiment analysis is a game changer for customer experience initiatives.

By John Godwin

We see it with our clients and potential clients all the time: customer experience (CX) initiatives fall short or even fail to launch because their sponsors can’t build a case for change within the organization. What they need is a compelling storyline. Hunches and guesses, after all, are not enough to earn buy-in, secure funding and effect change. And when budgets are tight, skepticism flourishes. So where can you find compelling storylines to jumpstart your CX initiative? The answer is sentiment analysis.

Sentiment analysis is the collection and analysis of opinions about a product, service or brand. (Although it’s not exactly the same thing as emotion artificial intelligence — emotion AI — or data mining, people tend to use these phrases interchangeably and the subtle differences aren’t worth dwelling on here.) Traditional sentiment analysis can produce what feels like anecdotal evidence. It’s a time-consuming, monotonous task that’s prone to error. But machine learning’s ability to analyze large amounts of consumer opinion to reveal patterns radically transforms this process.

The more business interactions shift into the hands of the consumer — quite literally — and the more consumers use social media posts, product reviews and other expressions of opinion as their personal megaphone, the more important this kind of “super” sentiment analysis becomes. It is more accurate, faster and scalable.

Brandseye, a small firm based in Capetown that tracks real-time social media sentiment for the likes of Uber and Pizza Hut, used machine learning and sentiment analysis to do what polls and pundits couldn’t: predict Britain’s exit from the European Union and forecast Trump’s victory over Clinton. How did they do it? By using AI to scrape 37 million public social media conversations (read: consumer sentiment). To improve its accuracy, Brandseye crowdsourced human analysis of individual messages. (AI is limited when it comes to understanding context or detecting tone — sarcasm, hope, etc. — but there are, as this example illustrates, ways to compensate for this.)

In 2014, machine learning quickly enabled Expedia Canada to see that more than half of the people commenting on its “Escape Winter: Fear” commercial, which had performed well in focus groups, hated the violin soundtrack. Meant to be slightly annoying, it became insufferable for those who saw it play in “bonus” spots over and over during the World Junior Hockey Championships.

When the Twittersphere demanded revenge, Expedia complied.

The company created three new videos in response, including one where an angry citizen who had Tweeted that someone should smash the shrill violin got to do just that. “We’re listening,” the voiceover declared. “And hopefully that’s music to your ears.” Expedia’s ability to pivot quickly — and to do so with a sense of humor — was a win for the brand.

Did I mention that Brandseye scraped 37 million public social media conversations? That’s six zeroes, folks.

Social media conversations — about both you and your competition — are a gold mine of consumer sentiment.

To get started, you have to choose the best model and toolkit for your purposes.

No matter what your goals, informed business decisions are better decisions. And the availability of algorithm-based sentiment analysis means there are no more excuses for not knowing how your customers feel about you. Start listening: it’s music to their ears.

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