Short-tail, long-tail and human-tail chatbot

Using the short-tail, long-tail and human-tail method described in this post will result in a chatbot that can save wast amount of time and help people focus on the quality work instead of frequently re-occurring task. Also, chatbots are companions, not human replacements.

I am not that overwhelmed by the hype of chatbots as a buzzword for AI. I see chatbots as an interface. It might be considered an evolution in terms of UI / UX, but as an example of AI, I am not convinced. So, what is the use-case for a chatbot then, in terms of AI? This is how I see it.

I have written about my thoughts on why I think a chatbot is a stupid example of AI, so will not go into that much further.

I am dividing the chatbot use-case scenarios into three different stages:

  1. Short-tail
  2. Long-tail
  3. Human-tail

short tail long tail chatbot

This chart simplifies my description. As seen in the chart, a well-implemented chatbot can save wast amount of time and help people focus on the quality work instead of assisting on simple tasks that re-occur very frequently.

All of above can have a chatbot as an interface, but can also be integrated into other existing software, be a classic webpage or an app, it does not matter, but for me, this describes the use-case for a chatbot pretty clear.

What is a chatbot?

This is also a term that is up for interpretation, but for me, a chatbot is a software that can understand the human language, understand the meaning and intention of what is said, identify entities and then respond in a way we understand as well as with the appropriate language for the domain.

Short-tail of a chatbot

This is the most common use-case and use-case with the least AI in. Short-tail answers simple, repeatable tasks, that are common and easy to foresee. Examples are:

  • What are your opening hours?
  • Can I book a table for 2, tomorrow at 8pm?
  • Who plays Harvey Spector in Suits?

From a customer service perspective, short-tail are often replacements for FAQs (internal or external) or the most prominent features on your company site.
Examples:

  • What is the wifi password
  • How do I configure the printer at 5th floor?
  • Show me product X for women in red.

As you see from above examples, this is not that much of AI except that the bot needs to be able to understand the intent of your text and potentially identify a few entities (like color, names, hours, dishes, sizes, product names etc).

Most chatbots we see today are in this category, not all have the ability to understand the meaning and identify entities, but still, those more “stupid” bots also fall in the category.

Short-tail chatbots are essentially the replacement for site-search and forms on sites.

Long-tail of a chatbot

Now we are starting to touch AI (or augmented intelligence) and the chatbot might provide more value than just being a more productive interface. The reason for this is that the long-tail chatbots can answer questions that are not common and questions that might be buried deep in all the unstructured data (80% of all data in the world is unstructured) we have, usually impossible to find since up until now, our search-features have not been able to understand, reason and learn knowledge in specific domains, today that is possible, that is what we tend to call AI.

This is a chatbot that actually tells us things we do not already know.

A short-tail chatbot only makes a process a bit more effective and streamlined in a simple user interface. A long-tail chatbot actually provides real knowledge and makes it available on-the-glass for us.

A long-tail chatbot takes much longer to implement given that we have to train the bot on the domain that it is going to work in. This is done with subject-matter experts. Often a new ML-model is needed for the bot to be able to fully grasp the domain and be able to understand, learn and reason. The ML-model is often also needed for the bot to be able to understand the more detailed and niched questions that might be asked. This since we still need the bot to be able to understand the meaning and intention of what the user is asking. Since long-tail bots usually are applied in a narrow field and with depth in that narrow field.

Human-tail of a chatbot

Remember the last call you had with a call-center? As soon as a question you have is not solved quickly you tend to end up in two scenarios, either you get angry or you are transferred to the manager (or you are informed that this is above the operators pay-grade and they need to talk to the manager etc). Let´s put this in the bot scenario.

  1. You get angry!
    A bot can today sense emotions and notice that you are either using a bad language (which we tend to use more frequent with a bot compared to on the phone) or that you simply are starting to show some frustration and irritation.
  2. The question requires manager assistance
    At a certain stage the bot might be given a question that simply is above the authority of the bot, what shall the bot do?

In both above cases, it is hard to train a bot to act accordingly since emotions are very hard to communicate in a chat, and even harder if you are a bot.

This is where the human-tail comes in.Human-tail is simply when a bot senses that it can no longer manage the conversation with a positive outcome. It is time to hand it over to a human. Some tasks are simply better suited for humans (still).

Natural human-tail scenarios need to be implemented in the bot as well. This can be done by alerting a human to take over the discussion and when the issue is solved, hand it over back to the bot. The human can see the entire conversation as well as emotions and all the different products, agreements and other details that have been either collected or pull from internal sources. Another scenario can be that for certain topics you get the option to be transferred to a human instead of the bot, this by choice of the user, not automatically.

Personally, I think the human-tail is as equally important to build a great bot, from an end-user perspective.

Augmented Intelligence

I have written about augmented intelligence many times, but most AI and cognitive solutions are implemented to complement and elevate humans, not replace. Therefore I like Augmented Intelligence better than Artificial Intelligence that often insinuates that AI is replacing humans.

In the above three, this is very clear.

  • Short-tail
    The bot simply removes the easy to solve scenarios from our lives and lets us focus on the scenarios that require more cognition.
  • Long-tail
    We, as humans, cannot remember everything and cannot learn everything. In the long-tail scenario, the bot is helping us with the things we do not know or that we simply have forgotten about. elevating us as humans.
  • Human-tail
    The bot acts as a first-line support, a meat-wall (or a computer-wall) to put it bluntly. We only get calls / chats which specifically require human capabilities. We are still better suited to manage a situation where emotions play a large role or to calm an angry person down. We also tend to be better at getting an angry person to become happy again, by explaining etc, a bot can occasionally be a bit rigid when it comes to what is right and wrong.

Photo taken August 2017 by me, in Vägerödsdalar on Skaftö, Sweden. It shows a direction pole for Bohusleden near our summer house.

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