An uplift in customer sentiment and understanding

Customer opinions are more visible and prevalent than ever before. Measuring, responding and adapting to customers is crucial to reputation and survival. AI to the rescue!

You may already be spending huge amounts of money tracking customer activity and behavior. But this can be an incredibly time-consuming task when done manually, and all that data will only tell you so much without AI to lend deeper insight.

Deep learning can unlock so much more by assessing what people mean when they email, tweet or interact with your brand. With an accurate ability to extract emotional insight from straightforward text, customer sentiment supports more successful customer service, product planning and chatbot implementation. AI enables a systematic understanding of customer sentiment based on the explosion of customer data available to all companies today.

Extracting emotion from data to better serve customers

When we talk to people in person, we understand their mood and tone. Machines don’t have this very human skill, but you can use AI techniques to analyze patterns in the text that can help you draw conclusions about the mood of the customer. In this way, you can think of deep learning as translating human emotion from text and images to parse the sentiment of customers. Are they happy about a new product? Confused about how to use a service? Rageful at an ineffective chatbot?

One way or the other, there’s sentiment in their words and behavior, and sentiment analysis is the process of identifying it and finding patterns in it. Knowing the emotion behind communication helps companies develop products that hit the mark, fine-tune customer service automation so it really works and enhance the customer experience across the board.

Within AI there is also vast potential for automating customer-facing tasks to improve the customer experience, and to make more sophisticated recommendations to consumers. For example, AI drives most of the recommendations people receive on ecommerce sites by finding patterns in past purchasing behavior as well as other types of social and web-based behaviors.  Over the past handful of years, AI has been used for automating customer-facing tasks to make more sophisticated recommendations.

Another customer-experience door opened by deep learning: We can now conduct visual search to improve the customer experience. For instance, a customer sees a couch at Peltarion headquarters they really like. They snap a picture with their phone, then navigate to their favorite retail website. By uploading the photo to the search bar, they find that couch or others like it — no words needed.

With the advent of deep learning, new abilities are becoming easier. While machine learning enabled some level of capability in this domain, deep learning handles context and sentence structure much better than machine learning models could, so sentiment analysis of text and audio is simply better than ever before.

Get more insight into how Peltarion supports customer sentiment 

The Peltarion Platform is set up to support deep learning methods using text, language and rich media, and to replicate groundbreaking research results while enabling non-experts to both apply and better understand the deep learning process.

If you make customers unhappy in the physical world, they might each tell six friends. If you make customers unhappy on the internet, they can each tell 6,000 friends

Jeff Bezos

02/ Read about industries using customer sentiment