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Lilla Szücs

#9 Interview

How Does CX Analytics Deal With Sizing the Importance of Topics?

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: May 18, 2021 * 9 min read

Contexts information should be a part of analytics. If gender influence what’s important, it should be in the model, it is then a variable saying “one”, it’s a woman, “zero” it’s a man and this variable is influencing the outcome.

So with this context variables in the model, the model can learn, can attribute this outcome to the context, or whether it is really driven by the topic. If you could give the model the information, it can find it out by themselves. That’s the intention to use the data you have. You may argue, Oh I don’t have the data for certain contexts information. You will never have a perfect data set. We are not in the business of proving the ultimate truth. We are in the business of making better decisions.

 

That’s basically what we can do. We can take some data that we already have, and we can work on having better data tomorrow, but everything comes with a price, the price to learn about the ultimate truth is very high. It has the best ROI to get insights that are just closer to the truth, than everything else so far.

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#8 Interview

Minimum Number of Training Examples Needed to Feed Supervised Text Analytics?

Minimum number of training examples needed to feed supervised text analytics?

 

When it comes to a supervised text analytics, is there a minimum number of training examples that needed to be fed to the AI engine?

 

There has been huge progress made in the last years. If you have heard and have had an opinion formed a few years ago, It’s not true anymore. Actually there are supervised learning systems, which are already categorizing from scratch quite well. You really need to teach them only the specifics of your business and the context of your very question. Typically when it comes to B2B, but also and certainly B to C domains. If it comes to product names or some abbreviation or slang or sometimes it’s also the context and even sarcasm, everything can be trained. Sometimes also the answer depends on the question, that’s why it always needs some training be perfect and human equivalent. When we look at our projects at CX.AI, we typically teach 300, 500 or max 700 examples. Then the machine can categorize infinite examples automatically.

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#7 Interview

Customer Audio and Video Feedbacks

Customer audio and video feedbacks

 

What about the other fancy methods that we have around like audio or video feedback or something along the lines of conversational surveying?

 

That’s also on the rise and some companies do it and actually the basic language of everything IS “text”. If you record an audio, transcribe it and feed it in the same process I just described. If it’s video, the main information is also audio, it can be real-time transcribed and fit in the same process. You can in parallel run through other algorithms, which you can also tap on in real time, which is detecting the emotion on the voice. You can see the emotion in the face of a video. All this are available real-time cloud services, where you can, in addition to what they say you can give an algorithm what customers feel using voice or facial information. This can be captured and analysed with your CX analytics. The processes is the same, but in any ways, I think it’s worthwhile to pursue that because, over audio and also video people tend to talk more than in open-ended text fields.

 

It is similarly effective to active listening, which you can also do over audio interview. You can also have a machine asking questions – it’s not rocket science anymore. It’s done as we speak. So if someone wants to build something like that, that’s done in a short set up period.

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#6 Interview

How to Get Customers to Give More Feedback in Open-ends?

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: May 18, 2021 * 2 min read

Customers tend to not writing much, let’s be honest in open-ended text fields. Isn’t that a big problem? And if it is, can we actually fix it?

It is a problem, depending on the context, it’s a big problem actually. So for the analytics per se it can be mitigated by just collecting more data.

 

It doesn’t tell you exactly what for person X or person Y what’s their key pain point are, but it can analyze for all customers or certain segments, what’s their key criteria to act on are. For customers or visitors, if they don’t write much, you need to collect more data.

 

One way of collecting more data is to collect more data at one person. There are so-called active listening approaches. What the active listening does in the NPS survey is, it takes the customer text feedback sends it to the deep learning tech service platform in real time, categorizes it in real time. The based on rules the bot reacted. If it’s category is friendliness, it says: “Hey, I’m a bot. I understood, you’re not satisfied with friendliness. Did I get it right? It would be great if you can explain in more detail”.

This probing, which is adaptive, has proven to be very effective, because the response really feels okay. Customers feel “This is not an preprogramed bot here, but they feel understood me somehow, or they feel the bot didn’t get me, which has even the same effect. Customer feel: Oh, he didn’t get me. I need really to explain better. So this, has proven to get double of feedback and also improve the predictive power by another 50%.

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#5 Interview

On Key Driver Analysis for CX

On key driver analysis for CX

 

Coming back to that text feedback and the causal machine learning applied. How should companies reliably deal with?

 

You could sit down and just categorize feedback by hand, which is fine as long as you have enough time and the resources to do it and you find people who never get tired to do this. The letter this basically a problem. You, as a human, you get tired. Also, when categorizing you learn and you change your mind over time. If you use another person for categorization, everything gets inconsistent.

 

But it should be consistent and reliable. This is where machine learning AI comes into play. There are basically two types of Texts Analytics AI. The systems you may know are unsupervised learning because 95% of systems in use are that. They basically find clusters themselves without training and human intervention.

 

They find meaningful clusters and these clusters are automatically named and without your contribution, they find it , so it’s very easy to do. BUT: We did a big study around it and found that this approach just has half of explanatory power, compared to when you categorize manually.

 

You don’t want to lose information. You want to drive important decisions from that. Just by the fact that you don’t have enough coders, you cannot sacrifice the quality. That’s at least my recommendation.

 

We searched further and further, and there are so-called supervised learning approaches, which is kind of a deep learning methodology where you train an AI to code like a human. You may know that for instance , Google can spot cats from pictures. Why? Because humans told them what’s a cat and what’s not, nobody knows how the Google deep learning does it actually, because it’s super complex it has millions of parameters, but it’s simply a black box predictor and that’s basically the same approach, you can do it with text. Finally we found the technology, which is often times even better than manual coding, because it never gets tired.

Also produce sentiment scoring, which is better than what humans can do. Typically humans are good at identifying the meaning. But to really say the tone of this sentence is very positive, very negative. It’s hard for humans to switch between the rational and the emotional part. The recommendation I would do, is to really use the best supervised learning approaches to categorize your text feedback, this means also the machine needs to be trained. Someone needs to sit down and teach it.

 

No worries, we do it for you. 😉

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#4 Interview

How to Find Key Initiatives to Drive CX?

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: May 18, 2021 * 9 min read

How is it reliably possible to detect the most important improvement strategies? What you need to do is to first off quantify what people are saying. It means basically you count what is said and put it in buckets.

Once they say, “Oh, this guy said friendly service and buggy software”. That way you take on two things of possible 50 or 100 things, he could have said. That’s the first thing you need to do, to quantify what people are saying.

 

The next step is then across the whole sample, identify which of those top topics are predictive and are uniquely mutually and have the information to explain outcome, which is the customer experience and later churn and upsell.

 

When doing that, you also need to apply the right methodologies. So if you take simply regression, out of your statistical software, this is too simplistic and this approaches assumes independence of drivers and linearity and often things the causal impact is indirect.

 

Let me give you an example. If you have a category, which is great service, and you have a category, which is friendliness that may drive great service and great service then may drive customer experience. At the end, you can use great service to predict, the customer experience, you don’t need friendliness, but still friendliness drives great service.

 

It has an impact and important impact eventually. So you can only reliably measure the importance of topics if you consider it also the relationships between the topics. But still, this is not rocket science. This is established methodology, causal machine learning. With this, you can reliably understand what’s important and what’s not.

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#3 Interview

What is Driving Customer Experience and How to Improve It?

What is driving Customer Experience and how to improve it?

 

How do you find out what’s driving customer experience and what should you do to improve it?

 

What do companies do to find out what drives CX? It is basically you ask an open question. Why did you rate this way? That’s basically what was advised by the inventors of NPS.

 

Of course other companies try to ask closed-ended questions. Basically they ask, how good are we at quality? How good are we in speed, at quality of service or support . This is typically what less and less companies are doing, because you need to torture your customers with lengthy questionnaires. But actually what you want to do is to have a one minute questionnaire and ask everyone.

 

That’s basically what you can do with the NPS approach. That’s basically the key method companies are using. But actually this message is broken and it always has been broken. It’s useful to do it. It’s also useful to have this customer feedback and give it to the frontline. But…

If you have not so many customer feedback, it’s the only thing you can do. You can work with it, but actually this method relies on a big misunderstanding.

 

The misunderstanding is, you ask the customer, why did you rate that way? Then you simply count, when you do the analytics. What’s the most often mentioned topic and obviously the most mentioned critique must be the most important one to be fixed and the most mentioned positive thing is what we do wonderfully. Right?

 

Actually this assumption is wrong.

 

Why do we know it? Because we can take what people are saying. We quantify that basically. Producing binary variables, and then predicting whether or not we can predict the rating and the outcome. What you see is that the true impact of topics – what people are saying – is nearly uncorrelated with the frequency of mentioning.

 

You can, we’ll explain this phenomenon.

 

Once you accept that there are even other things, what companies are doing, to mitigate this. They split the frequency and compare what detractors say versus what promoters say. Even this is prone to so-called spurious correlation, spurious correlation is basically a correlation, which is not causal , for an example – if you look at the C-suite of companies, they have typically much larger shoe sizes. You split C-suite versus other employees. And so you see that they have larger shoes.

 

So is shoesize now truly driving the likelihood to become a company leader? No. In this case it’s of course its ridiculous. The reason: typically leaders are still men, not women, and they have larger shoes. So there are cool correlating facts, which not necessarily are the cause, there is simply another correlation or another expression or another outcome of a causing topic.

 

You can find out which one is causal, mutually explaining outcomes and therefore you need modeling analytics and multivariate modeling. It’s basically not possible with simply splitting, slicing and dicing data.

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#2 Interview

How to Measure the Impact of NPS?

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: May 18, 2021 * 2 min read

CX measures like NPS or net promoter score, are always abstract measures. Did anyone ever try to measure the impact of it on the bottom line?

That’s a really interesting question because most people know the Harvard business review article from the seventies, where someone obviously wrote that keeping customers is five times cheaper than gaining new customers.

 

Actually this is a modern myth. There was no study around it. He had just a thought experiment in this article, but some consultants just needed this argument. And everyone since than just knows the facts, which is basically not true. Actually, I don’t know many articles or even studies around it.

 

The only thing I know, is a study we did by ourselves. We did a study around that. What’s really interesting is, that when you look at score measurements like NPS, they typically do not correlate with churn or other impact figures, but when you do the modeling exercise, then you can prove, that it is there, there is an impact to churn and to upsell.

 

It needs modeling because upselling and churning has lots of reasons and to really identify the reason or the impact of the loyalty, it needs, that you clean out the data from other impacts. One example: a recent study for insurance company, where more customers with higher income tend to be more skeptical or less loyal in their statements.

 

When these people are more loyal, they will upsell even more because they have more money. So this fact makes customer value correlate negative NPS, even the causal impactis positive. Because of this fact, there’s inherent negative correlation. But the NPS itself has a positive impact, which is basically adds up to zero correlation.

 

Correlation is not causation. You need modeling to find out whether actually your CX measurement really has causal impact to financial results. That’s basically what you can find out. You can really find out that one NPS point is 10 bucks higher customer value. It’s a number where you really can work on, know where you can really find out, when I do this, NPS would rise up five points. This means this is worth 10 million investment. It’s very helpful to have such an exercise done.

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#1 Interview

Is NSP the Right Measure?

Is NPS the Right Measure?

 

There are always discussion around this, that it’s actually not a great measurement.

 

This is what you hearing from insight professionals – from those who are very deep into it because there are arguments that if you use a Likert scale, where you have one, two, three, four, five, and every scale point well described, this is a better measurement of what’s happening.

 

This is probably true. Form a scientific point of view there are better ways to measure it out there. Also the NPS score is a share value. It computes, what kind of share our promoter share. If you have a share computation, it is very fragile for lower sample sizes.

 

There are some methodologically critics, but actually if you look at other things it’s quite a good measurement. So first of all, it is easy to explain. In most cultures 0 to 10 scale is intuitive. It’s easy to execute. If you look at the numbers, how well this correlates with other scales, it really highly covers it.

 

I don’t think that this is the key thing to argue about . NPS is one of many measures, but it is easy to execute and its well known. You can benchmark yourself against others.

 

I don’t think that’s it is a killer criteria that NPS is not the best measurement method.

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CX.AI for Dummies

CX.AI for Dummies:

The NEW Way to Use Your Satisfaction Survey Data To Do the Right Things

Companies want happier customers. Satisfied customers will buy more, pay higher prices, recommend more to friends, and stay longer with the company.

That’s why every company is asking its customers for their satisfaction. In such a survey, most customers also tell in their own words why they are satisfied or unsatisfied.

Large companies get so much feedback. It’s impossible to read it all. But not reading is not an option. It would be a customer assault and a waste of pure gold.

 

Stop Wasting Customer Feedback

That’s why companies use intelligent software to read this text feedback. As an outcome, the software puts a tag to every customer feedback. These tags describe the themes that had been mentioned, such as “great service” or “too expensive”.

Now companies are counting those themes to see which are most often mentioned. They assume that those most frequently mentioned topics are most important. This seems to be true because the topics are the answer to the question, “please describe WHY you are satisfied or dissatisfied.”

 

Reading Between The Lines

Unfortunately, a fair amount of essential topics are not raised as frequently. Instead, clients mention topics often that come first to their minds. For example, Home Speaker clients are referring to “great sound” first. Insurance clients are saying “great service” first, and restaurant guests are noticing “great taste”. Interestingly, improving those topics will not necessarily make customers much happier.

Imagine you go to the doctor with pain in your stomach to find out why. He may ask you why you have this pain. But should he take your ideas for granted?

Or, you go to a psychologist because you fear spiders. He may ask you, “why do you fear spiders”. But should he take your answer for granted?

Human Become an Expert with Training – so does Artificial Intelligence

A doctor needs long years of studying and practicing to understand underlying reasons based on what patients tell them.

In the same way, intelligent software can study what hundreds or thousands of customers had been saying. It can find out which topics are a clear indicator of satisfied or dissatisfied customers.

For instance, many speaker customers give “great sound” as a reason. A fair amount of them, however, have not a very high satisfaction score. In contrast, not so many customers give “reliable music streaming” as a reason, but all of them are very satisfied.

Which topic do you believe is more powerful to make customers happy?

 

CX.AI takes your customer feedback to find the hidden truth

It delivers insights on what makes customers happy, companies successful, and insights leader powerful again.

CX.AI uses an intelligent software that tags customer feedback. It does this with a precision as if a human would do it. But at the speed of light.

CX.AI further uses an intelligent software to study all customer feedback. Just like a psychiatric doctor studies patients for years and years. But at the speed of hours.

 

Reliability equals Profit

The best thing with CX.AI is that it is so reliable.

Imagine, a doctor would use not very reliable knowledge and therefore would give the wrong medication to every second patient. Would you go to this doctor?

CX.AI has four times higher reliability than conventional approaches. Let me ask you: What reason could justify NOT using reliable insights? What reason justifies taking the wrong medication?

Why would you want to make your customers NOT happier by a factor 4 higher reliability?

Why would ever someone want customers that buy NOT more? … that not pay higher prices? … that not recommend more to friends, or not stay longer with the company?

You know the answer.

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