Why does your chatbot suck?

You may have wondered why the significant investment you’ve made in your virtual assistant/chatbot has not yielded much by way of positive feedback from customers. Or perhaps you’ve met and tried a few different chat solutions that promise to reduce your customer support load, but turn out to be disappointing.

The big question is: Why does your chatbot suck? And is it augmentable?

The premise behind why the chatbots of today are not up to par is largely due to the older paradigm of using keywords as a way of understanding what customers are asking. This means that a lot of questions are misinterpreted – and the chatbot may then offer a buffet of choices so that the customer still needs to guess at which will help them solve their problems.

Not anticipating edge cases

Mainly, however, there is a huge grey area where your chat provider may not have considered when they were discussing how to train and build your bot. These are the edge cases that your bot might not be equipped to handle.

Multiple questions

Example: “Do you know if I can upgrade my plan? If I do, how much will it cost?”

Our thoughts fire in succession, so it’s also nor1mal that we ask questions in the same way. You may have noticed that customers write a long question with multiple parts, but the chatbot is unable to sufficiently answer to each of these questions.

With multiple questions, your chatbot needs to understand that there is a segregation between the question parts, and perhaps what the relationship is between sentences. Here is where context becomes extremely important (read on to find out more about context) in figuring out and attending to each of the questions posed by the customer.

Complex questions

Example: “I’m trying to get it to work, and I’ve tried turning it off and on, cleaning the port, and doing a soft reset. Can you help me?”

Troubleshooting questions are usually too complex for most chatbots. As it is, it is pretty hard for most chatbots to understand standard questions and answers in context and give a satisfactory answer.

Special training is required here. There needs to be an inbuilt decision tree that captures a range of potential solutions, and use the chatbot’s natural language understanding engine figure out which steps the customer has already tried, and then filter them to the right direction.

Situational questions

Example: “I want to access my order info, but your site is down and I can’t sign in.”

Temporary situations such as website being down, a flash sale, or limited offers, require special information that the chatbot may not originally have known when you first taught it the basics of the questions and answers it’s expected to know.

Imperative in this situation is having a flexible enough system that allows you to add these new cases that you want the bot to be able to handle (especially during those festive seasons!) so that you can take care of more customers, faster.

Feedback or feature requests

Example: “I really think that you should add this awesome new feature, it would help me so much.”

These are important bits of information that could help with product-building, so you want the chatbot to capture as much detail as possible from the customer, such as “Could you describe what you envision it to look like?” or “What else do you think we could change so that it suits your use case?”

Most of the time, people go to the same channels for support as well as for recommendations and feedback, so it’s important to keep these aspects in mind when designing the chatbot’s knowledgebase and language generation engine.


Example: “You’re useless.”

It happens – people get frustrated and start hurling insults at the unsuspecting chatbot. The same advice holds from customer service 101: Don’t take it “personally” (heh) and think of the different ways you can resolve the issues instead.

This is also a juncture where the bot needs to recognize it may not be doing a good job, and hand off the conversation seamlessly to a human agent who may be better equipped to handle the customer.


Example: “Where were you born?”

You’ll be surprised how many people ask chatbots questions like this. By not preparing the bot’s “backstory” you may inadvertently be churning out a lot of “I don’t know what you mean”-types of responses that makes your chatbot look dumber that it actually is.

This is a great place to play up your brand personality and add in content that would surprise and delight your customers. The brand’s backstory can really shine through if you take the time and effort to teach it.

Ignoring context

There are a few elements of context that most chatbots these days ignore. First, language context – There are plenty of examples of assistants not understanding simple sentences like “Call me an ambulance” or being very unhelpful with their suggestions. These, as briefly explained above, are examples that show just how difficult it is to tag and parse natural language, especially when they come in many forms from different people. The ambiguity of sentences in dialogue make it especially difficult to decipher without large amounts of training data.

Second, it ignores the customer’s context, who may have asked the same question before, or just got off the phone with your support helpdesk. Being asked to repeat their security answers and issues over and over when being transferred among service agents vexes most of us greatly.

Situational context is also largely disregarded. When you have a new product launch, or when your site is down, the bot is isolated and doesn’t understand it needs to respond to people with new and urgent questions (see following section.)

Lack of business integration

A major reason for the reason customers get frustrated at bots is that they can only answer commonly asked questions, and have no real access to their accounts or past histories. Customers are not able to conduct any meaningful business with your bot, or even ask questions specific to their orders or plans.

All in all, non-integrated bots are a huge lost opportunity for engagement and marketing over time. By having a chatbot that understands your customer’s needs, it will be able to provide tailored, predictive replies to what it anticipates the customer could want. For example, knowing that someone has just placed an order with you, your bot can attend to delivery status requests, or provide tailored recommendations to your newly acquired customer.

It is important for your chatbot to also be integrated with the CRM, ticketing and fulfillment business processes and systems so that you can accurately and swiftly catalogue and assess a customer’s status at all times. This could turn out to be a huge competitive advantage for your business, especially right now.

No escalation protocols

It’s inevitable that sometimes the bot will be stumped. Many bots simply repeat over and over “I don’t think I understood that. Could you please rephrase?” – annoying customers to their wit’s ends. You need to identify the right times that it doesn’t know what to say, and quickly divert the conversation to someone (probably human) that respond to the customer.

When identifying this escalation point, either through a combination of time cues or a low confidence answer that the bot is about to give, have a friendly message at hand that tells the customer they’re being passed to a subject matter expert so they will be put at ease that their problems are being solved.

All the rules of good customer support apply here!

Long implementation process

In addition to these challenges, it may also be hard to add new questions and answers to your bot, as and when you have new knowledge that you want to teach it: It may require a long process between you and your chat provider. It could involve a lot of dawdling around before information is added.

As part of its capabilities to learn, a bot must be able to quickly ingest and understand your previous interactions with customers and hypothesize what its answer may be to them. This machine learning process should be fast enough so that implementation can be swiftly up for testing by your sales and support staff, and even a select group of your customers.

What now?

Underpinning all of these factors is the fact that natural language processing is a high technical and complicated space, involving areas such as tagging, parsing and specific expert areas such linguistics. As most of the points above may not have been considered when you worked with your chatbot provider, now is a good time to reevaluate how your chatbot is doing and what you can do to improve it.

At KeyReply we make it ever easier to get intelligence and all of these great integrations behind every channel your customers could possibly want to reach you on (Messenger, Telegram, WeChat, etc.) This flexible arrangement means there is a ready and smart chatbot that can take care of your customers 24/7, wherever they are. Sounds good? Request a demo at keyreply.com/iq.