Leveraging data from conversational AI interactions to improve customer experiences

The catalytic effect the COVID-19 pandemic has had on digital transformation is well documented.

None more so than with customers now having a myriad of channels – run by both humans and AI – to engage with businesses daily, producing millions of bytes of data every time. There is no ignoring the fact that conversational artificial intelligence (AI) is becoming a driving force for business communication. In fact, the global conversational AI market is expected to grow from USD 6.8 billion in 2021 to USD 18.4 billion by 2026 (source).

However, it is one thing for enterprises to have systems in place to manage customer conversations, and another to have the means to decipher and extract value from the data provided. Having the ability to easily visualise, aggregate, and, most importantly, act based on the right data will be one of the most significant competitive advantages any company, regardless of industry sector, can have in the near future.

Data-driven development

Most businesses today understand that automating business processes is not just about putting a few bits and bytes in the right places. However, past experiences with voice and chatbots have often left customers with a negative impression of what they are capable of. Business leaders often perceive conversational AI offerings as readily available, off the shelf solutions that can immediately transform any legacy environment into one that leverages AI to its full potential. In reality, in order for a system’s potential to be maximised, itrequires customisation and continuous development as a strong brand is not exclusively defined by a good product or particularly creative marketing, but also by excellent service. 

Automating customer experiences is only part of the story. In order to increase and deliver better value, in-house developers must be provided with usable data that is able to be analysed and assessed at speed. Having access to this data means developers are able to better identify customer pain points when interacting with their business. Likewise, better data management facilitates a streamlined environment, enabling businesses to allocate resources to the relevant areas and, above all, save on costs that might have been spent on less impactful tactical engagements.

Overcoming the black box

Business leaders are well aware of the importance of levelling up the customer experience. However, they’re often flying blind due to the use of siloed virtual agents that do not allow for real-time overviews or the ability to drill down and analyse conversations. As a result, there is no foundation to learn from data, understand user behaviour, or improve conversational design. Thankfully, this is beginning to change; as business leaders shift to give their technology departments the digital tools they need, these highly technical teams are becoming more integrated into the day-to-day operations of the business and are therefore able to harness and analyse customer data. In this way, businesses are starting to use automated processes as tools to assist them, rather than having them operate in a vacuum. 

Mitigating risk

This is where the role of the conversation designer becomes integral for success – and needs to be fully integrated into the business in order to achieve results. They need access to customer insight data and the ability to pair this with the organisation’s existing processes and systems. Being able to leverage actionable insights to transform what was once considered ‘cold’ voicebot and chatbot engagements, into those that are more natural and conversationally driven becomes the priority.

By being able to identify customer friction points and optimise the AI processes to deliver quality engagements, conversation designers can make real-time changes and smooth out problem areas. As well as access to data, this also requires centrally-managed monitoring and reporting to deliver actionable insights that enable the business to understand the technology’s values and improve inefficient systems.

Automation brings significant value when it comes to data analysis and injecting those insights into conversational AI. But, human agent interventions will always remain an integral part of the process to provide the empathy and understanding that machines are yet unable to do. Pairing the development of effective conversational AI with the skills and experience of human specialists will ensure the organisation is on the right path for success.


About the Author

Sebastian Glock is the Senior Technology Evangelist at Cognigy, a leading conversational AI provider, where he acts as a mediator between technology & business. He has advised top-tier companies in Europe and North America in digital projects and is a seasoned speaker at international conferences and events.

Featured image: ©Metamorworks

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