Organizations are adopting the use of voice or text chatbots, with some already having implemented the technology. According to Gartner, by 2027, 25% of organizations are expected to make chatbots their primary means of customer communication. This shift is due to the change in consumer behavior, which now favors digital interactions over traditional customer support methods.
Due to the fast-paced digital environment, consumers now expect immediate response and resolution from customer support. Chatbots provide a quick solution for brands to meet these demands. With the growing need for instant customer service, chatbots have become more advanced and are increasingly relied upon by consumers. This increase in use has led to higher expectations for chatbot performance. If these expectations are not met, it can negatively impact customer satisfaction and brand loyalty.
They’ll examine three major challenges associated with the increasing usage of chatbots as they serve larger and more diverse customer groups, and how a continuous testing approach can help business leaders, developers, DevOps, and CX teams overcome these challenges.
With the growing global popularity of chatbots, the systems behind them, such as those handling natural language processing, latency, data security, and other functions, must be robust. This is because of the expanding and diverse user base, many of whom are sharing more personal information to get a wider range of customer needs to be met. Three major challenges encapsulate the common pain points in chatbots.
Voice or text-enabled chatbots must possess the intelligence to securely operate while dealing with various challenges such as variations in the spelling, diverse accents and dialects, background noise, poor connections, and more. Though customers may typically have only four or five intentions when interacting with a chatbot, these intents can be expressed in numerous ways. With the global increase in chatbot adoption, there is a higher likelihood of poor connections and a significant increase in the number of languages and phrasing variations that the chatbot must comprehend and process.
As chatbots become more intelligent and trusted by customers to meet their needs, people are becoming more willing to share their personal information with them. This leads to an increase in situations where chatbots are handling personally identifiable information (PII) or other confidential data. With the implementation of data privacy regulations like GDPR worldwide, there is added pressure to ensure that chatbot systems maintain secure and compliant handling of this data in all use cases and regions.
In the age of flexible work and with millennial and Gen Z audiences interacting with bots outside of traditional business hours and on weekends, chatbots cannot be limited to working in shifts. Customers may be located in multiple time zones or worldwide, leading to a large portion of users who are awake, online, and seeking quick and efficient service at all times. Therefore, chatbots must be capable of handling spikes in traffic and high volumes of requests 24/7.
The main conclusion from these challenges is that contemporary chatbot investments should be centered around flexible systems that can adapt to changing customer usage and performance needs.
Fortunately, by following best practices and investing in automated artificial intelligence (AI) and machine learning (ML) systems, leaders responsible for customer experience (CX) and their teams can develop chatbots that can handle these challenges. The key to success is to adopt a continuous testing approach.
For instance, during digital transformation when a company moves its contact centers and supporting systems to the cloud, a large amount of data is being transferred, complex integrations are being re-evaluated, and processes are being re-architected. To preserve the stability of the application and existing integrations in the contact center, continuous testing is essential to guarantee that the organization’s systems continue to function properly throughout the cloud migration process. Continuous testing, development, and testing are not separate activities. Testing is automated and quality assurance is integrated as developers submit their code.
An effective continuous testing approach involves thoroughly analyzing all channels, including chatbots, to mimic real customer interactions. This should cover scenarios like unexpected user inputs and high traffic at unpredictable times. Automation is essential, especially during peak demand periods, and companies should aim to test the bots’ capacity to handle large numbers of requests, such as up to 100,000 requests per second or more. The testing should be comprehensive and include automated evaluations of NLP scores, conversational flow, security, performance, and monitoring.
By implementing continuous testing, companies can improve the performance of their chatbots and minimize risks while boosting revenue. Through continuous testing, organizations can avoid 90% of the downtime caused by high-severity errors, leading to cost savings of $2.2 million over a three-year period. When testing is incorporated into every stage of the development process, it allows for earlier identification of issues and enables the company to innovate faster and provide better and more profitable service to its customers.
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