Conversational Commerce, without the BS

I’m investing substantial time and effort in helping people to better understand what all the hype around AI and conversational commerce technologies like chatbots and intelligent digital assistants is about. It can be rather overwhelming in my experience. And I don’t blame you. I’m overwhelmed at times by all this news on A.I. induced jobless futures and fully automated and self-learning machines as well. It looks as if A.I. will change the world before we can even say “what?”. But we know better.

Why use statistics if we can apply math?

I am convinced Natural Language Processing (NLP) and Machine Learning techniques can do great things, and will be able to do even greater things in the future. But it will take some time and many learnings before we get there. Progress has to be made, and will be made for sure. Just not through claiming ever greater disruptive breakthroughs that are based on assumptions, not evidence. Progress will also not be made by seeing A.I. and machine learning in specific, as the panacea to all problems.

As I’ve been saying in this post: Stop asking the machine to find the most probable answer if you know it with certainty yourself. Just provide the machine with the answer and instruct it to provide it when the question pops up. In most cases this will be a faster fix than asking the machine to learn the answer by comparing 100k+ inputs (Q’s) and outputs (A’s) to come up with the best estimate for the answer. This is specifically true since machine learning algorithms tend to make mistakes and therefor require human supervision.

Invest in relevance, not long tail understanding.

Also, NLP has intent recognition rates of 85% to 95%, depending a bit on the width of the application and the time spent on improving it (should be on the high end after a year). You can of course spend hundred thousands of dollars into getting it to 99,8% or better. But should you? In my experience the return does not often justify the investment. What does justify the investment, is making the answers more relevant to the customer/user by using context and personalisation.

Think of it, what is the reason your customer still makes a call after he’s read the airlines luggage policy? Right, he wants to know what that means to him as an occasional flyer with a last minute discounted ticket on a transatlantic flight. Better NLP will not provide him with the answer he’s looking for. A more personally relevant answer will. And of course the threshold for your company could be at 96% or 84% intent recognition. The exact rate is not the point, the point is you should know when to apply other strategies to improve the customer’s experience and get their jobs done.

AI has reached the peak of inflated expectations

Gartner believes that AI has now passed the peak of inflated expectations. That’s good news for all of us, but mostly for you. Because now you can start using AI, without the BS. And you can start using it to solve your problems and those of your customers, not the problem of a company that lacks experience in the field and has a ‘feeling close to certainty’ that conversational artificial intelligence will change the way the world spins.

 

Thoughts on machine learning for customer service chatbots

I’m thinking out loud a bit about machine learning strategies for customer service chatbots. Bear with me. More question than answers, because some of the strategies I see, I just fail to understand. I get the impression that some try to find a machine learning solution to a problem that is hardly there.

Machine learning takes too long to find an answer

Let me start with putting out here that machine learning is not very effective when it comes to finding the right answer to a question.

In a customer service context, machine learning can be useful when it comes to parts of natural language understanding, just not so much in providing the right answer. Because, once you understand what the customer is asking, your company should be able to provide an answer, start a process of getting one, or get the job done with straight through processing.

And if you know the answer, there’s no need to wait for the machine to estimate it. You can directly instruct the chatbot to provide the answer relevant to the question, no?

If so, why do so many startups use machine learning strategies that require tens of thousands of input and output examples to do this? That way it takes months for your chatbot to answer FAQ’s automatically. Let alone how long it takes for the less frequently asked questions.

Of course, you can also use the numerous examples to have the chatbot understand all the variations customers use to ask the same question. But that’s what natural language processing can already do. Maybe you need to help it identify synonyms, industry specify language, stop words etc., but in general this should be covered.

You can have the machine identify gaps, suggest fixes and have humans make it perfect

Much of this ‘helping the machine’ is done by humans. Machines can identify where it goes wrong and even suggest how to improve or fix it. This isn’t supervised machine learning nor reinforcement learning. It’s effective, but human work.

And, here’s the thing: You can wait for 18 months to let the machine have its own way, or you can have the machine identify gaps, suggest fixes and have humans make it perfect. And you can do all that the same business day!

So, on the next day, not the next quarter or year, your chatbot will have the right answer to the question and your customer is happy. How would that score on agility? So please tell me, why the wait? To prove that tech can do this? To prove that you can do this nifty technological trick?

I frankly would not care about that. I would care about serving my customers, fast and right, the first time. How about you?

Customer Experience Management is Failing

Many enterprises are investing millions per year to get their digital transformation agenda’s going. Agenda’s that consist of digitizing processes, building self-service portals and improving the customer service experience through adding a plethora of digital channels like WhatsApp.

These programs have been mainly focussing on the cost-side of things. Their business cases are entirely based on cost-saving potential. Nothing wrong with that of course. I do believe though that the time has arrived to start focussing on the opportunity-side, the growth, revenue, and customer loyalty-side if you will, of digital transformation.

The has arrived to start focussing on the opportunity-side, the growth, revenue, and customer loyalty-side, of digital transformation 

I recently experienced an aha-moment when it comes to how these opportunities are already sitting under our noses:

Last week I was having this workshop with one of our clients to discuss a new project that they wanted our help with. CX Company has been working with this client for several years, and successfully reduced many live contact center contacts with the help of our chatbot/virtual assistant technology platform. But we did not discuss before how our technology could enable online sales and increase conversions, how it could make choosing easier, more seamless and the customer experience more personal and warm. I’m glad we now did.

My “Aha-moment” came when we were discussing numbers. The numbers were website visits and top-tasks their customers were trying to get done when there. What struck me was that these numbers were at least 10x larger compared to contact center volumes.

We can only come to the conclusion that there is this (big-data) swamp of interactions that we know very, very little about 

We know, in this and other cases, the Virtual Assistant (VA) handles around 30% of total service queries in an automated way. I hadn’t realized before though that in the time we reduced interactions with the contact center, digital interactions exploded at this scale.

If we count how many of those digital interactions are dealt with by the VA, add how many result in actions by customers in self-service portals and add to that how many interactions are sales-transactions, we can only come to the conclusion that there is this (big-data) swamp of interactions that we know very, very little about. And if we know very little about them, we are also not influencing actively how value is created with them, for our Client nor their customers.

If we are not working on 80% to 90% of the interactions people have with our company than we should conclude that Customer Experience transformation is failing 

If we allow us to let those numbers sink in again, we could argue that Customer experience transformation is failing. If it is our job to help customers get their jobs done better, if it is our job to help people meet their desired outcomes when getting the job done AND we are not including 80%-90% of the interactions people have with our company, than we are not doing a good job, are we?

I hear you saying: “But people are satisfied with the website, no? And we are collecting data to retarget them with advertising, plus we use (advanced) analytics and a/b-testing to get more conversions from incoming traffic. So we are doing a lot already, no?”

Customer experience management is not about tinkering with nudges to “help them convert”. It is about helping people swiftly and easy fulfill the journey of getting their job done. 

It is not (good) enough in my opinion. Customer experience management is not about satisfaction at touch-points, nor about tinkering with “nudges” to get them to convert in your funnel.

Customer experience management is about helping people swiftly and easy fulfill the journey of getting the customer’s job done. And if we do not know why an interaction took place, what the customer’s intention was to get done and whether it was successful, we are failing at our job.

We are missing out on 80% to 90% of the opportunities, right in front of our noses, to help the customer do what she intended to do, better. 

I believe we are missing out on what is potentially the greatest opportunity for marketers and customer experience professionals since the computer entered the office. We are missing out on 80% to 90% of the opportunities, right in front of our noses, to help the customer do what she intended to do, better. And we are missing out on the opportunity to co-create value with these interactions for ourselves too.

So, what are you doing to actively engage with your customers on your website to understand what it is they are trying to get done at each and every of these interactions? What are you doing to turn that insight into value for them, and yourself?

I can think of a couple of interesting ways. Ways that CX Company’s Customer Experience Automation platform, DigitalCX, can enable. I’ll discuss them here in a future post.

What a great journey I’m on :)

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