We’ve all been there.
Surfing a phone company or bank’s website, trying to get an answer from a chatbot only to have the interaction end in frustration. Ultimately this leads to a futile click-fest in search of answers or - heaven forbid! - having to pick up the phone and call customer service.
What's painfully clear from this kind of experience is that there’s a real need for businesses to have sufficient scope and capabilities within support channels so that customers don’t have to waste time searching for simple answers.
A conversational AI platform can be a powerful tool for automating support and providing great user experiences. However, while in theory, this should be simple enough to achieve, we find that most existing chatbot systems are far from perfect.
Below are five key reasons why a typical chatbot fails and what you can do to avoid the same pitfalls:
1. Failure to scale
There’s no escaping the simple fact that customers will always expect you to be able to answer all of their questions. While this is, of course, an unrealistic expectation, you still want to attempt to get as close as possible to meeting this demand. Which is why there’s no point going live with a virtual agent that has a limited scope.
We often encounter chatbots that are only capable of answering 10 or so questions and offer limited conversational skills. This can be down to the fact that either the system is built on legacy technology or it requires specialized resources and technical personnel to get it up and running. This quickly results in a drained budget and a project that never even gets off the ground.
The key to avoiding this issue is to build out your virtual agent using a system that is designed to scale and do so well. This should include pre-built content in the region of thousands of industry-specific intents (not just 10-20) to really get your project off to a flying start. It should also be extremely user-friendly, with an interface that enables existing staff to leverage their expertise and easily develop and maintain conversation flows. Remember, you don’t need to empty your budget on an army of data scientists if the underlying software is capable of sufficiently empowering existing staff.
2. Failure to provide natural conversation flows
Syntax and rules-based chatbots are nothing new. While they are competent at offering simple conversation flows, and may even make it possible to click your way down to a correct answer, these solutions provide a sub-par experience that, ultimately, ends up frustrating customers and giving off a negative impression of your brand.
Conversational AI offers an alternative to these basic solutions. Thanks to machine learning and advanced neural networks like automatic semantic understanding, a virtual agent is able to interact with customers using natural language and in a manner resembling what they are accustomed to with human customer support.
3. Failure to personalize
If your chatbot is only capable of answering FAQ-style questions that customers can easily search for on your website then it’s not doing a good enough job. This ‘informational’ phase certainly has its benefits, but by only identifying pre-made info and serving it back you’re missing out on the true power of conversational AI chatbots.
By giving your virtual agent more transactional capabilities, where you access existing data from backend systems through authentication and API integrations, you are able to perform actions on behalf of your customers and provide personalized service at scale. This helps save time and money while delivering a great customer experience - a clear win-win.
4. Failure to go live
A common thread among chatbot projects that are complex to build and maintain is that many of them never see the light of day. This can be attributed to any of the points that we’ve mentioned thus far and can often lead to years and years of development hell, increasing the overall cost of a project and effectively nullifying any positive outcomes in terms of a business case.
Any competent vendor should always assess the scope of a potential virtual agent and help you build a solid business case with a clear plan for how to successfully bring your project to launch.
5. Failure of channel mix
A virtual agent that ticks all the right boxes should have no problem converting large amounts of traffic from other channels and delivering a solid return on investment. We find, however, that many companies are hesitant to use automated online chat as a primary channel for customer support - whether it’s due to issues with limited scope, a lack of natural conversation flows or feeling that it doesn’t provide ‘real’ value.
In these cases, it’s easy to see why the chatbot and business case fail horribly. A good virtual agent (powered by conversational AI) thrives at high traffic volumes and is perfectly suited as a principal customer service channel. To take full advantage you need a vendor that offers some key functionality, including scaling to human chat capabilities, a clear understanding of customer intent and sentiment analysis.
Put great customer experiences first and the rest will follow
Many chatbots fail as a simple result of choosing the wrong technology platform. Either by being based on legacy technology or due to the platform being so complicated it requires enormous resources to build out, companies end up with a limited solution that never reaches the full potential of what is possible with chat.
Conversational AI offers a far better solution to maximizing the customer experience through chat than syntax and rules-based chatbots. Thanks to machine learning and natural language technologies, a virtual agent built using conversational AI offers interactions that are human-like and achieve actionable outcomes for customers.
Remember, customers will always expect you to be able to answer all of their questions.
Conversational AI can help you get closer to meeting their expectations by providing the following:
- Pre-built, industry-specific content for a quick time to launch
- A scalable intent hierarchy that can be easily adapted to your organization
- Transactional capabilities that allow a virtual agent to perform actions on a customer’s behalf
- Intelligent hand-off to human support thanks to automatic semantic understanding
- User-friendly software that makes it easy for existing employees to build and maintain conversation flows