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?


2 thoughts on “Thoughts on machine learning for customer service chatbots

  1. Pingback: Conversational Commerce, without the BS | A Customer & Brand Strategy Blog

  2. This is the classic problem of the cold start and of marginal utility (in the economics context). Firstly, there is absolutely no way that companies can either wait for an A.I to get the answer right, or bear the cost of months of incorrect answers to customers or employees. So vendors have to find a middle way, there is just no other option. The “other way” is to find markets, new types of customers, with some kind of free offers that “trains the A.I for free” and where “the costs of error” are borne by others. This is what Google did to train it’s voice recognition A.I with the free directory service. The attractiveness for VC’s is that if you get it right, there are huge, huge wins in being “the layer” for this kind of thing across all applications.


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