Lessons from an accidental community manager

How can AI improve VOC? Part 4: Conclusion

This article originally appeared here: https://marketing.toolbox.com/articles/what-is-voice-of-customer-data-and-how-can-ai-improve-its-value-definition-key-components-and-ai-powered-voc-techniques

Missed the first three parts? Check them out here

Introduction: https://www.trustinsights.ai/blog/2020/07/how-can-ai-improve-voc-part-1-introduction/

5 AI Techniques to collect VOC data: https://www.trustinsights.ai/blog/2020/07/how-can-ai-improve-voc-part-2-5-ai-techniques/

11 Components of VOC: https://www.trustinsights.ai/blog/2020/07/how-can-ai-improve-voc-part-3-11-components-of-voc/

Big Data + AI for VOC Analysis

With all of these components of the VOC, how do you analyze all of the data? That’s the last component of the customer experience, Big Data. Big Data is essentially having large amounts of information that require assistance from AI to collect, clean, analyze, and make actionable. Each VOC component alone is a large analysis project. Tackling all of the components together will require a lot of planning and coordination. Once you’ve put together a system to collect data on all of these pieces, you’ll have a comprehensive view of your VOC, broken down into discrete segments, topics, and conversations. Using AI to assist in this venture will help expedite the process and ensure more accurate information. Having one piece of the puzzle is a good start, having all of the pieces to make the full picture is ideal and will give you a strong competitive advantage.

Key Use-Cases for AI-Powered VoC

Once you’ve collected and analyzed your VOC data, the next logical step is to make use of it. The two primary use cases for VOC data are innovation and retention.

1. Innovation:

Based on the analysis you conducted in each part of the customer experience, what trends did you notice? What terms, words, ideas, and phrases kept showing up? Most of all, what did you notice that was unexpected? This is fertile ground for innovation, to identify things that customers desperately need but haven’t clearly articulated it. As a subject matter expert, you can infer what the actual need is based on your VOC data and produce the working solution. The business trope here is Henry Ford’s quip, “If I asked customers what they wanted, they would have said faster horses”. AI and machine learning allow us to identify those needs faster, more accurately, and at lower cost.

2. Retention:

The second use case of VOC data is retention. In almost every business, it costs more to acquire a new customer than it does to retain an existing customer. With outputs like sentiment analysis, emotion and tone detection, and other techniques, you can identify what customers are most unhappy about and fix it before you lose them. In turn, you’ll stabilize your revenue and reduce your overall costs for sales and marketing by increasing evangelism. Customers who feel their needs are being met and their expectations exceeded will do some of your marketing for you.

Like we said, VoC helps to avoid guessing at the needs of your customers, and instead enables you to use data to understand who they are and what they really want. We hope this feature helps you figure out the best ways to collect data about the Voice of your Customers, as well as gain insight on how you could leverage artificial intelligence to help drive optimal value from this valuable data!


How are you using AI to make your Voice of Customer data more valuable? Join the discussion at trustinsights.ai/analyticsformarketers


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4 thoughts on “How can AI improve VOC? Part 4: Conclusion

  1. Thanks for the great tips! I couldn’t agree more that your audience needs to see themselves in your content in order for it to be effective, and in order to make sure you’re succeeding in doing that, you need to conduct the appropriate research.

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