Recently I had an interesting discussion with Ron Stroeven, one the founders of Infotools, about open-enders, short for open-ended questions. In 1990 Infotools was established, but Ron has worked in market research far longer than that. He has a wealth of experience in survey design and data analysis, so it was fascinating to hear his view on open-enders, their history, and future.
Understanding customer comments, on a large scale, needs to be automated. So, it requires Natural Language Processing (NLP) or Text Analytics. Unfortunately, most open-source NLP tools were developed on text researchers have easy access to. These are typically news articles, research papers and movie reviews. I learned that the analysis of customer comments is quite different, and here is why.
Earlier this year, I have 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.
I was attending a conference at a resort hotel in Orlando, Florida, standing in the lobby trying in vain to connect to a client who needed to discuss the results of a range of multivariate output. To my chagrin, my cell would not pick up a signal. Imagine my surprise when the nearby concierge, viewing my angst, permitted me to use his phone to complete the call. He even dialed the number for me.
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 to go about it?
Artificial intelligence (AI) tools make it possible to easier anticipate customer needs in multiple ways. For example, marketers can analyze vast volumes of customer data, identifying the characteristics of high-value past customers which allows businesses to create highly personalized campaigns. Sales teams can quickly identify customer purchasing patterns and customer service reps can deliver relevant actions and sales offers.
To be right in the heart of the SaaS (software as a service) action, and learn from the best in the industry, we hopped over to San Francisco, USA, to attended SaaStr 2018 — the biggest SaaS conference in the world. It’s literally where the crème de la crème of software genius congregates.
There is no argument that AI is here and it’s here to stay. AI has been a hyped-up term for quite a while and is now a reality. This blog is a start of a series of blogs focusing on AI and how it can help improve your customer experience. To get a full insight, download our e-book “How to leverage AI to improve Customer Experience”.
To investigate the public perception of fashion brands, we’ve analyzed over 8,000 customer reviews of four brands and applied Thematic to find insights in this data. We chose three brands that directly compete in the US fast-fashion world, H&M, Forever21, Charlotte Russe, and one brand that targets the same audience but operates as solely an online store, Lulus.
Are you getting the most out of your customer feedback? How can you ensure your feedback will transfer to solid actionable insights that make a difference to your business?
Following on from a previous blog post, a great example of prescriptive analytics, shared by Bain, is the one of DiDi. DiDi is a major Chinese ride-sharing company providing transportation services for more than 450 million users across over 400 cities in China (i.e. many times bigger than Uber, whose China operations DiDi acquired and absorbed in 2016).
Often, AI is portrayed in the media as this ever-growing frenzy that will ultimately lead to robots stealing our jobs. And, how we should fear computers that are more intelligent than we are. Really? But as 2018 comes to a close, it’s clear some businesses aren’t paying attention to Hollywood’s ominous depiction of artificial intelligence. Instead, they are embracing AI whole-heartedly with the adoption of AI text analysis.
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.
How to achieve a high customer retention rate? It’s pretty much common knowledge: To achieve the most impact, work on the 20% that matters, that which will make the biggest difference. You can apply the same principle to customer retention. The least effort, meaning once you’ve set the following foundation strategies, it’s up to your organisation to make it happen.
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.
Continuing our blog series on artificial intelligence AI (see earlier blog posts), we share some examples of everyday AI applications and commonly used AI analytics. If you look hard enough, you’ll find plenty of everyday examples of how businesses have used AI to make your (and their) life easier, thus improving the customer experience.
Is your growth plan to acquire more new customers? What’s wrong with that? Nothing, if you have all the time and resources in the world, but there’s a smarter way to maintain growth whilst achieving a high customer retention rate. It’s simple, keeping your customers coming back for more will result in a greater ROI.