Battle tested: Continuous testing helps chatbots thrive

If you’ve been interacting with your favorite brands lately, you’ve likely noticed that chatbots seem to be everywhere. We are a society obsessed with instant gratification. We want answers immediately and often that means rolling the dice on using a chatbot to see how close they can get to what we are looking for. As the use of chatbots expands, so can the number of chatbot failures each day. At the same time, customers’ expectations of what a 'good' chatbot experience is has never been higher. And not meeting these stringent expectations means disappointing the customer, which can also result in loss of business, or worse, damage to your brand.

No matter what industry a chatbot operates in, connecting and communicating with people is its primary function. The formula for success for chatbots is the same as it is for traditional customer service channels: quick and effective service. Even though conversational AI has made great technological strides, the user experience is still lacking, especially when it comes to handling natural language processing (NLP), latency, data security, and other issues.

As executives and their teams in charge of the customer experience (CX) look to navigate these challenges, let’s examine how the key to success lies in embracing a continuous testing approach for optimizing chatbot performance.

Adopting a Continuous Testing Mindset

The customer’s relationship with modern contact center Interactive Voice Response (IVR) systems and chatbots is accepted and appreciated for the convenience they bring, especially when it feels like an efficient and authentic human response in addressing their needs. It’s as if the customer is saying, "I don’t mind talking to a machine, unless it starts acting like one." 

To drive a growing number of use cases in finance, healthcare, telecommunications and especially retail, companies lean heavily on app developers with expertise in artificial intelligence (AI), machine learning (ML) and conversation design to build increasingly advanced chatbots. That said, organizations can’t just flip a switch at the outset and expect the system to somehow work perfectly. The technology may be in place, but it needs to be trained and tested on an ongoing basis.  

In fact, it’s crucial that the testing of chatbot performance be continuous. There is no such thing as a single testing phase when bringing a chatbot to life. Testing must instead be part of daily business, just like coding, design and monitoring. As companies continue enhancing chatbot features and performance to meet client needs, teams must understand that the cycle of testing is a process that never ends.

For instance, there may typically be only four or five “intents” customers have when engaging a chatbot, but those intents may be phrased in many different ways. An example is if a customer states their insurance policy number in groups or permutations a chatbot could misinterpret them and provide inaccurate information regarding the coverage of their insurance policy, only to realize the error when it's most crucial.

To add further complexity, when contact centers serve a multi-national or even global customer base, the variation in phrasing and the number of languages the chatbot must navigate grows exponentially. Chatbots that are voice or text enabled must be smart enough to operate securely while navigating countless variations in spelling, a wide range of spoken accents and vernacular, background noise, static from bad connections and more. This requires comprehensive testing that may include automated NLP score testing, conversational flow testing, security testing, performance testing and monitoring that analyzes experiences from end-to-end across all channels. 

Data security is another critical priority that only continuous testing can address. As chatbots become smarter and more trusted by customers to meet their needs, people are getting more comfortable giving their personal data to chatbots. This increases the number of situations where chatbots are working with personally identifiable information (PII) or other highly sensitive data. Testing is required to ensure the handling of this data remains secure and compliant across all use cases.

Optimizing the Testing Approach

Given that testing needs to happen at scale, in production-grade settings and on an ongoing basis, there’s no way to do it manually. That’s because use cases are infinitely different, and a high volume of tests is often required to pinpoint the particular changes or adjustments that make the biggest positive impacts. 

This may all seem daunting for top executives who worry that continuous testing means getting bogged down in complexity. Fortunately, this is not the case. By honoring best practices and making the right strategic technology investments in automated, AI/ML-driven systems, C-suite leaders responsible for the customer experience and the teams they manage can deploy continuous testing in a way that’s both seamless and scalable.

The best approaches include end-to-end testing capable of generating synthetic traffic to mimic real-time customer interactions. Ideally, challenging scenarios, like unexpected user inputs, should be included in the test scenarios alongside continuous monitoring. 

Automation is key, especially in times of peak demand such as healthcare Open Enrollment, Black Friday, Cyber Monday, and holidays throughout the year. Load testing up to 1,000 bot requests per second, or more, can help ensure companies will be able to handle all the traffic needed. And in an era of flexible work and millennial/Gen Z audiences interacting with bots after business hours and on weekends, testing must be designed to analyze and optimize how a chatbot performs 24 hours per day, 365 days a year.

Finally, it’s vital for organizations to ensure that the training data is clean so their chatbot’s natural language understanding (NLU) engine can work optimally in the first place. This helps pinpoint flaws immediately and offer clear and proactive steps to improve chatbot reliability and performance.

A Continuous Testing Approach Enhances Chatbot Performance

However, when organizations move continuous testing earlier in the chatbot development cycle, it finds issues faster before harming their reputation with customers, saves time on the back end, leaves room for more improvements, and is the only option to innovate fast. 

Image credit: phonlamai/

Christoph Börner is Senior Director of Digital at Cyara.

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