Many businesses are now using chatbots that can communicate via text or speech for the first time, or they already have chatbot systems in place. By 2027, 25% of businesses are expected to use chatbots as their main method of client communication, according to Gartner. This is a result of the shift in consumer behaviour away from conventional customer care channels and toward interactions that are mostly digital.
Due to the immediate satisfaction that consumers now expect from companies and their customer service representatives as a result of the current digital landscape, chatbots are the perfect tool for brands to quickly satisfy their customers. The need for immediate customer support is growing, and as a result, chatbot systems have advanced and consumers’ dependence on them is growing. Higher performance expectations for bots have emerged as a result of this growth in usage, and if they are not satisfied, consumer happiness and brand loyalty will suffer.
In the sections that follow, we’ll examine three key issues that arise as chatbots become more and more popular and are used to serve larger and more varied customer bases, as well as how business leaders, developers, DevOps teams, and customer experience (CX) teams can address these issues using a continuous testing strategy.
Three major issues as chatbot usage increases: The Pain Points of Adoption
The underlying systems that manage natural language processing (NLP), latency, data security, and other operations need to be stronger as chatbot use among clients around the world soars. This is a result of a population of consumers that is expanding and becoming more diverse, many of whom are disclosing more of their personal information to cater to a larger range of customer needs. Three main difficulties can be used to outline common chatbot pain points.
The first difficulty is that chatbots must compete with a broader and more varied consumer base.
Voice- or text-enabled chatbots must be intelligent enough to function safely while traversing innumerable spelling variants, a variety of spoken accents and vernaculars, background noise, static from poor connections, and more. Even when using a chatbot, most customers may only have four or five “intents” in a single language, but they may express those intents in hundreds of various ways. Now that use is increasing globally, there is a greater danger of bad connections and an exponential increase in the number of languages and phrasing variants that a chatbot must comprehend and interpret.
Challenge #2: Consumers are giving chatbots access to more private and sensitive information.
People are becoming increasingly at ease disclosing their personal information to chatbots as they become smarter and gain the consumers’ increased faith in their ability to suit their demands. This increases the instances where chatbots are handling extremely sensitive data or personally identifiable information (PII). There is increased need to guarantee that a chatbot system’s handling of this data stays secure and compliant across all use cases and geographical locations, particularly in light of GDPR and other data privacy legislation being implemented globally.
Challenge #3: Modern chatbots must be accessible and reliable 24 hours a day.
Chatbots cannot afford to work in shifts in a time of flexible work and audiences from generations millennial and Z interacting with bots on the weekends and after normal business hours. Users may be dispersed over many time zones or the entire globe, so there is always a substantial number of clients online, awake, and in need of prompt assistance. As a result, chatbots need to be able to withstand traffic spikes and large loads round-the-clock.
The executive conclusion from these problems is that investments in modern chatbots need to be made in dynamic systems that can adapt to changing user usage and performance needs.
Top Chatbot Performance Requires a Continuous Testing Mindset
Fortunately, C-suite leaders who are in charge of customer experience (CX) and the teams they oversee can implement chatbots that can handle the aforementioned challenges by adhering to a few best practises and making the appropriate strategic technology investments in automated artificial intelligence (AI) and machine learning (ML)-driven systems. Adopting a continuous testing method is perhaps the most important factor for success.
For instance, a corporation undergoing digital transformation may move a sizable amount of data, reimagine intricate integrations, and rearrange procedures while relocating its contact centres and supporting systems to the cloud. Continuous testing is essential to guarantee that an organization’s systems continue to function properly during the cloud migration process and to maintain application stability and existing integrations in the contact centre. Development and testing aren’t independent activities when continuous testing is used. As developers submit code, testing is automated, and quality assurance is involved.
End-to-end analysis across all channels is part of the most successful continuous testing strategy. It is best to simulate real-world consumer encounters while testing chatbot behaviour. This should incorporate difficult situations like unforeseen user inputs and use spikes at arbitrary hours of the day and night. Automation is essential, particularly during periods of high demand like Black Friday, Cyber Monday, open enrollment, etc. Companies can help ensure they can handle the increasing traffic by load testing up to 1,000–100,000 bot queries per second, or more. The testing strategy should ideally be thorough and incorporate automated NLP score testing, conversational flow testing, security testing, as well as performance testing and monitoring.
Executives can enable their staff to create and maintain chatbots that are continuously optimised and are a crucial component of delivering excellent CX by encouraging the continuous testing methodology across contact centre operations. Performance and dependability will significantly improve as a result of systems scaling and adapting to the changing needs of a growing client base.
High-performing chatbots and the constant testing that keeps them optimised at those performance levels elevate chatbot reliability and value, which benefits any firm by reducing risks and increasing revenue. Continuous testing experts reduce downtime from high-severity (SEV 1) errors by 90%, saving their organisations $2.2 million in costs over a three-year period. An organization’s capacity to innovate more quickly, provide effective and profitable customer service, and identify difficulties early is strengthened when continuous testing touches every stage of the development process.
