The pandemic has also accelerated the pace of digital transformation and created an unprecedented wave of new users coming online from smaller towns and tier two and three cities. These new users make up a large population of the country, who are non-English speakers and rely on voice rather than text to communicate.
This has posed a formidable challenge for enterprises, as these users prefer to communicate in their regional languages and dialects. While many companies have decided to use chatbots for customer handling, chatbots come as a deterrent in that they may not deliver the human-like experience that customers have started to expect, and sometimes make demands. To handle rising call volumes, it makes sense for enterprise companies to employ multilingual voicebots and provide the best customer support. The Voicebot not only answers queries but can also perform complex tasks such as making recommendations, taking customers through a process, setting up new accounts, etc. Communicating with customers in their regional languages, when things remain unresolved or too complex, are unhindered to human agents. Apart from general, Voicebots can also help companies to analyze customers based on their speech patterns and get useful business insights like customer age, gender, region, dialect, tonality for better targeting.
Although other companies have different business objectives, they will still need to custom-build the Voicebot to meet the needs of their individual customers. This will allow them to get vital information about their customers in order to serve them better. Therefore, it is important to recognize that deploying Voicebots is not a one-size-fits-all solution, but an ongoing optimization and optimization process that will be the key to a successful deployment, which can have maximum impact long-term, regardless of ROI. I can be immediate.
For a successful partnership in deploying Voicebots, enterprises should focus on these most common challenges:
Cultural Change: Naturally, any enterprise will need time to adapt to a new process and it takes three to six months to adapt to it. It’s even more so for the end customer. An enterprise customer will not be familiar with the new way of interacting and may be surprised by suddenly switching to Voicebot, and it may take some time to get comfortable like any new experience. This is especially the case when Voicebot suddenly hooks up with a customer, without having tried and tested it on a small group. Although it is important to see how quickly you can develop a Contact Center Voicebot, speed of development should not be the only objective. Ideally, Voicebots should be implemented in a phased manner, starting with a smaller audience. This will provide an opportunity to monitor and evaluate each stage against predetermined success criteria and enable rapid improvement before the application is expanded. In addition, companies must map out specific use cases, products, and scenarios where they can see Voicebots delivering value quickly and immediately. Lastly, enterprises should see how the customer is responding to it. Most of the time the signals given by the voicebot do not elicit the desired response from the customer. The best way to get the right information from the customer is to monitor, tune and adjust signals, see results and measure success before deciding on the next step.
Turnaround times: No new process introduced in a legacy system will be flawless. This can only be accomplished over time. While technology has a way of adapting rapidly, the only way to optimize a process is by working on customer feedback and fine-tuning the flow. In fact, according to a recent study, 82% of customers said they would change products or service providers after a poor experience with a company’s customer service department. Hence, the quick turnaround time will make the customer happy as well as help Voicebot to adapt its solutions to suit the needs of the customer. One way to do this is by introducing an assisted plug-and-play or DIY system into the Voicebot system that allows customers not only to replace or flag any issues but also to teach them the craft with relevant suggestions, improvements. Shifts to a more mature self-service model.
Comprehension error: As enterprises adopt Voicebot to handle customer queries, reducing the error rate for speech recognition will be critical to building user trust. There are two types of errors: domain error, which is a knowledge gap in Voicebot understanding domain-specific terms, and user-understanding error, which is an error when Voicebot is not able to understand the input given by the user. This can be due to excessive background noise or too many inputs being given at the same time. A domain error can occur due to Voicebot not being updated with an understanding of the business domain. In the first three to six months, the system may encounter many or all of these errors. Fixing multiple issues at once can be difficult and overwhelming for teams on both sides. Therefore, it is best to analyze problems, allow the business to prioritize solutions, and ultimately fine-tune and tune systems to meet customer needs.
Written by: Amit Gandhi, Founder of NovelVox
