How AI Can Help Customer Feedback Analysisby David Hunt Data Geek and Analytics Fanatic
AI and analytics are so inter-related in today’s data frenzy enterprises that companies are taking the correlation of these two terms to a different level. Advantages of using AI to analyze customer feedback are that it’s faster, more accurate and more scalable than manual tagging. And these factors have many knock-on effects such as more actionable insights, faster responses and resolutions, better customer experiences, lower churn rates, and higher profit margins, to name a few.
AI-powered customer analytics enables customer support agents to focus on more important tasks, whether that’s delivering more personalized responses to customers or creating reports that can be shared with stakeholders. It also means that you can analyze all customer feedback in seconds rather than in weeks or months.
We all know the drill. Monotonously scrolling through survey responses, reviews, social media comments, tagging them, resolving them, maybe skipping a few, taking a coffee break, back to scrolling – then before you know it it’s time to sign off and you’ve only got through 200 hundred more pieces of feedback. This is where machine learning can help. Once you’ve trained your machine learning model, you can let it run automatically in real-time, 24/7. Leaving teammates to focus on the results and deal with urgent issues immediately.
Consistent CriteriaYou’ve defined your tags, you’ve trained your machine learning model, and all that’s left to do is obtain the results. Analyzing customer feedback is simple if you’ve put the time and effort into establishing the correct criteria, and because machine learning models use the same criteria to analyze every single piece of customer feedback, you can rest assured that your results are going to be consistent, more accurate and 100% actionable.
Created on Nov 27th 2020 03:34. Viewed 262 times.