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Articles by Alyona Medelyan
Harnessing the Power of AI in Customer Feedback Analysis
As the customer experience becomes more digital, consumer feedback represents the cornerstone for business improvements. Yet, as data increases, its interpretation becomes more complex. So how do you simplify work for you and your team? If you haven’t already, now may be the time to use AI for its revolutionary approach to analyzing customer feedback.
Customer-led strategy: a guide by Thematic
ADVERTORIAL We all know the incredible success stories of product-led companies. Slack, Calendly, and Zoom became household names almost overnight thanks to product-led growth. But even these companies had to hire large sales teams to maintain their growth momentum. Likewise, sales-led companies know that ultimately products must deliver customer value. This is where customer-led growth comes in. Customer-led growth supersedes product-led and sales-led strategy by putting customer needs first.
Council Post: Three Mistakes That Harm Customer Understanding And Retention In Times Of Uncertainty
Alyona Medelyan, Ph.D., is the CEO of Thematic, a customer feedback analysis SaaS solution. This year, the world is dealing with several “black swan events” at once. According to risk analyst and author Nassim Taleb, black swan events are those that experts cannot predict. Experts also struggle to predict their outcomes. This year, the Covid-19 situation has continued to evolve. War broke out in my birth country, Ukraine. Inflation and plunging stocks indicate a possible recession.
3 unique methods to analyze your customer feedback today
Have you ever left feedback as a customer yourself? Your star rating or a satisfaction score will tell how you feel, but it’s your comment that will explain why. Unfortunately, while most businesses collect feedback, they do not analyze text comments. Often feedback isn’t actionable. It’s too hard to read through all those answers to find those that can inform strategy. In this blog post, we’ll break down the task of analyzing open-ended customer feedback.
How to unlock the drivers behind your CX performance using Text Analytics
Many organisations, large or small, gather customer feedback to improve their CX efforts and ultimately their bottom line. But gathering feedback alone can’t make much of a difference. We need to analyze our feedback to discover insights that inspire us to drive action at our organisations. Enter; Text Analytics software.
How To Build Your Own Feedback Analysis Solution
By Alyona Medelyan, CEO of Thematic. At Thematic, we’ve spent years researching, designing and developing our customer feedback analysis platform. While I’d love for everyone to be using ours, I understand that you might have the resources, time and the data required to build one in-house. There are benefits to building vs. buying. You can customize it to your needs. You can use it as much as you want! But: You’ll still need to spend time maintaining and training others to use it.
Why customer feedback is critical for digital transformation in 2020
Digital transformation is one of those terms that has been taking the corporate world by storm for the past years. Like it or hate it, it’s here to stay. Here is proof from Google trends showing the growing popularity of the search term “digital transformation”: There are some great examples of digital transformation already happening in industries that aren’t typically digital-first. In this article, we’ll look at some of these examples.
One easy method for calculating the ROI of CX
“Demo or die!” – my PhD supervisor was very clear on what is the most critical path to success in academic research. As I later learned, in the business world, ROI or Return on Investment calculations play a similar role. You won’t be able to get executive buy-in, approval for budget, communicate success and ultimately progress in your career, unless you can estimate and then validate the ROI.
Calculating the ROI of CX: Complete Guide 2019 [Includes Spreadsheets]
This blog is a comprehensive guide that will tell you everything you need to know about calculating the ROI of Customer Experience (CX) to move from insights to action. It includes a step-by-step guide to help you calculate the ROI of CX. We’ve also created spreadsheets just for you that you can use to calculate two financial models: the impact of Net Promoter Score (NPS) on company revenue and on customer acquisition costs.
How to Perform an Effective Net Promoter Score Analysis
Is your Net Promoter Score Analysis effective? U.S. companies lose about $41 million every year due to bad customer service. In 2003, Fred Reichheld developed a new metric that would help measure and assess customer satisfaction – the Net Promoter Score or NPS. Reichheld and his team found that this method worked best in determining a consumer’s brand loyalty. But what is a good score? A high net promoter score means your company has earned more promoters than detractors.
Does it matter which customer experience metric you choose?
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)? Is one enough or should you implement several metrics? Does it actually matter? Here, we discuss the two arguments: Pro and against.
3 ways to close the customer feedback loop to prevent churn and increase revenue
Source: Shutterstock. What is the customer feedback loop? The customer feedback loop is the practice of responding to customers meaningfully when they leave feedback. For example, if a customer complains about being overcharged, the company may respond with an apology and issue a refund. But not all feedback is customer complaints. Customers also leave feedback when they have a particularly memorable experience. Or they may have suggestions for improvements or feature requests.
How to get meaningful, actionable insights from customer feedback - 3 examples
Source: Shutterstock. Do you gather vast amounts of customer feedback but don’t quite know how to get actionable, meaningful insights from it? You know the ones, insights that would help to influence your customer experience and overall business strategy. If you keep analyzing your feedback but not knowing how to action it, this post is for you.
Does it matter which customer experience metric you choose?
Source: Shutterstock. 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)? Is one enough or should you implement several metrics? Does it actually matter? Here, we discuss the two arguments: Pro and against.
Part 5: What is Thematic Analysis (plus our secret sauce on how to make it work even better)
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. All of the former approaches mentioned have disadvantages. In the best case, you’ll get OK results only after spending many months setting things up. And you may miss out on the unknown unknowns.
Part 4: Topic Modelling, an approach to Text Analytics
This is the 4th article in my series of Text Analytics posts explaining popular approaches to feedback analysis. Earlier, we talked about text categorization, a Machine Learning approach that requires training data. We concluded that it can’t detect emerging themes in people’s feedback and that it’s only as accurate as the supplied training data. This time, we’ll discuss topic modelling, also a Machine Learning approach, but an unsupervised one, which means that this approach learns from raw text.
Does It Matter Which Customer Experience Metric You Choose?
Are you responsible for measuring the progress your company is making in improving customer experience? If the answer is yes, I’m sure that at some point you needed to decide which metric (or metrics) to use for that job. Did you choose the ubiquitous Net Promoter Score (NPS)? Or perhaps CSAT, the traditional customer satisfaction metric? And don’t overlook the more recent entry, the Customer Effort Score (CES). Is one metric enough, or should you use several metrics? Does it matter?
Part 3 of 5: Text Categorization, an Approach to Text Analytics
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. If you haven’t read my earlier posts here are the links to: As you can guess, both of these approaches have shortcomings, but most importantly, they aren’t advanced, because a person has to painstakingly tell the algorithm what to look for.
Part 2: Manual Rules, an approach to text analytics
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. Today, I’ll turn to the second approach. Also quite popular. It’s based on Manual Rules, and is closely related to word spotting. Both approaches operate on the same principle of creating a match pattern, but these patterns can also get quite complex.
Part 1: How to build a text analytics solution in under 10 minutes
For a long time, I’ve been planning to write a post to clarify what’s possible in Text Analytics space today, in 2018. Throughout my career, I’ve spoken with many people who are living through the pain of analyzing text and trying to find a solution. Some try to reinvent the wheel by writing their own algorithms from scratch, others believe that Google and IBM APIs are the savior, others again are stuck with technologies from the late 90’s that vendors pitch as “advanced Text Analytics”.
3 learnings from the Deep Learning for Sentiment Analysis workshop
I wanted to share with you my learnings from when our team at Thematic organized and sponsored a free 3-hour workshop on Deep Learning for Sentiment Analysis, run by Dr. Felipe Bravo-Marquez. The workshop booked out within 24hrs of announcing it and more than 40 people attended. Felipe was joined by co-host Dr. Edison Marrese. Both Felipe and Edison are Post Docs in the general area of Deep Learning focusing on NLP.
3 tips for getting started with natural language understanding (NLU)
Source: Languages of the Roman Empire: Bound lead tablets of magic inscriptions (300–500 AD), Kircherian Museum on Wikimedia Commons.. 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.
Why US airlines rank best or worst, according to passengers
Source: Shutterstock. At Thematic, we often analyze customer feedback data with our AI powered text analytics, to get to the hidden insights that many companies cannot get. ThePointsGuy published the 2018 “The Best And Worst Airlines In America”, and 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.
4 steps to customer survey design - everything you need to know
This post contains (almost) everything you need to know about customer survey design. When designing a customer survey, as with anything, planning is key. As the saying goes, if you don’t plan, you plan to fail. It can be useful to think of the end-point and going backward when planning. What insights do we want to get? And how to design our survey questions so that we can achieve those outcomes? To best design a successful survey, I want to introduce you to the 4 steps for successful survey design.
What makes a Scotch top notch? - Thematic analyzes 1000 tasting notes of distilled spirits
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. Believe it or not, but this was how I often practiced my beginners English after school: translating labels on the backs of spirits bottles for my dad, and marveling at the skillful turns of phrases. This is what a spirits enthusiast Paul wrote about Knob Creek Kentucky Straight Bourbon, giving it a score of 90-95.
How to get actionable insights from your customer feedback analysis
I talk and write a lot about AI technology, but don’t worry, we still need humans! In fact, it doesn’t actually matter how good an AI technique may be, only a real person can effectively decide what’s actionable and what’s insightful for the business (applying the right context with the correct historical knowledge).
12 big mistakes when collecting and analyzing customer feedback
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? Here, I share some common mistakes I’ve seen companies do when collating and analyzing feedback, in my role as CEO for Thematic, and beyond. Make sure you’re not committing these faux pas. Once you’ve got the deep insights that are also accurate, they can transform your business strategy.
Guest Interview: Jeremy Watkin, Customer Service Leader at FCR
Can you briefly explain your background and current role in the customer service industry? I more or less happened into a customer service role at a SAAS startup out of college because I hadn’t quite figured out what I wanted to be when I grew up. I quickly found that interacting with people, building relationships, and solving problems suited me. As I grew in that role, I eventually had the opportunity to lead the team and found that those same customer service skills came in really handy.
Key take-aways from Sentiment Analysis Symposium 2016
Last month I was fortunate enough to speak at the Sentiment Analysis Symposium in New York. It is one of the most important events for those who invent text analytics solutions and for those who use them. I also attended the co-located sentiment analysis tutorial run by Jason Baldridge. Overall, this was an excellent event to get up to speed on the current state of affairs and the future outlook. So, here are my key take-aways. So, how would one detect emotions?
What Data Scientists Can Learn From Qualitative Research
By Alyona Medelyan, Thematic. Open-ended survey questions often provide the most useful insights, but if you are dealing with hundreds or thousands of people’s answers, summarising them will give you the biggest headache. If you are a data scientist, you may try using an NLP library or an API, but tuning them is hard and the results are often difficult to interpret.
7 NLP Must Haves for Customer Feedback Analysis
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Why Most Customer Feedback Analysis Tools Suck & How to Fix This
Customer feedback analysis tools are all the rage, but most of them suck. They collect scores into pretty dashboards, but don’t actually tell what the feedback is or how to achieve customer loyalty. If you ever left a review yourself, you will know that your score is not nearly as valuable as the review itself. Think about it: as a business, how useful is the average of 100 scores compared to 10 customer comments on why they gave you that score?
Three tips for getting started with NLU
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. Algorithms are getting much better at understanding language, and we are becoming more aware of this through stories like that of IBM Watson winning the Jeopardy quiz.
Applications of natural language understanding: Tools and technologies
Attendees should have a basic understanding of NLP and machine learning. If you are dealing with natural language—whether in product reviews, user feedback, or interaction with customers—you are likely to benefit from the latest advances in natural language understanding. With the deep learning revolution, AI is no longer the privilege of large corporates; most businesses that deal with language data can apply NLU techniques in their existing solutions or create new services.
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