Keeping up with customer feedback: using AI to make the most of your data

Once customers choose a product or service, the continuing success of their relationship with that company depends on customer support. Get it right and win loyal customers for life. Miss the mark and you lose customers, who may then broadcast their dissatisfaction and do more damage to your brand.

The noise level is rising, and so are customer expectations

Customers are using multiple channels to give feedback. They may air concerns via surveys, online chats, calls to a support center, or in person. They also take to social media platforms like Twitter and Facebook. This qualitative feedback is crucial to understanding the needs and concerns of your customers.

Volume and channel are only the beginning of the challenges. Language is creative, cultural and contextual – and it evolves constantly. Sentiment can change quickly and may be tied to a particular demographic.

Three priorities for effectively managing customer feedback

Regardless, when customers are experiencing issues, there are three critical factors that companies need to focus on in order to prevent and resolve issues:

Speed – You have to identify the problem early, and fix it fast. Customers are able to contact you quickly, resulting in an expectation for a quick solution.

Accuracy – Understanding the nuance of customer problems is essential to implement the right resolutions, for all customers.

Consistency – No matter where or how your customers contact you, the quality of their experience and the resolution to the same issues must be consistent.

Artificial intelligence meets customer feedback

Customer care executives have adapted to these changes in customer feedback channels and expectations by leveraging innovations in artificial intelligence, deep analytics, and natural language processing. These innovations allow them to help their teams get in front of customer issues, establish a common experience for all customers, regardless of channel, and contribute to a culture that’s customer-inspired.

Because AI-based approaches use machine learning and natural language processing to handle new data, processing time is more automated and thus faster. New words, slang, acronyms, and misspellings are identified and learned through context and machine learning, not as a result of analysts manually defining each term so the software can understand it. In contrast with other methodologies, analysts don’t need to spend time inputting long lists of new data sets for every customer service problem that arises. This means data is analyzed faster, giving companies the ability to quickly identify and fix any customer issues.

AI-based methodologies are also more accurate. They take a bottom-up approach to analyzing data. In other words, such approaches build structure and categories based on what’s in the data. Again, this is very different from traditional analyses, which often start with analyst-determined categories and then assign every piece of data to one of those categories.

Having topics and clusters based on what is actually in the data—rather than predetermined categories—gives you a clear and more accurate picture of what is really happening.

Finally, because AI-based techniques are more automated and less dependent on analyst intervention, they can provide more consistent results. All data is treated and processed the same way, removing the potential for human bias to be introduced and affect the results or consistency.

The use of AI in text analytics make it possible to hear your customers on multiple platforms, and address their concerns quickly and efficiently. That ability translates into significant and measureable value to their company’s overall, in terms of higher customer satisfaction, reduced churn, and increased revenue.

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