Presented by DefinedCrowd
Companies like MasterCard are implementing AI strategies that transform how customer experience is done. Join MasterCard Lab’s VP of Artificial Intelligence & Machine Learning and others for insights on why AI is essential for fintech companies now, and developing an AI strategy going forward.
Winning customer loyalty requires an experience that is always personalized, efficient, and seamless no matter what channel consumers are engaging in — at scale. That’s where artificial intelligence (AI) and natural language processing (NLP) comes in.
The technology has evolved to the point where any company can leverage it to power chatbots that can communicate accurately and efficiently with your customers, 24/7, or analyze customer data to gain the kinds of insights that fuel better business decisions.
And according to a survey on AI in Financial Services conducted by the World Economic Forum, 77% of business leaders expect that AI will become essential to their business within the next two years.
How NLP delivers superior customer service
NLP-powered chatbots are a big level up from the rules based chatbots of old. They deliver fast and accurate support, and can answer customer enquiries up to 80% faster than a human customer service agent. By handling the lower-level questions that require more straightforward responses, these chatbots can free up human customer service agents for more interesting, fulfilling work that is complex and multi-faceted.
NLP-enabled chatbots are also valuable omni-channel solutions, enabling you to track your customer’s conversations throughout their journey, as well as save their searches and preferences. They can function as a virtual assistant to make a customer’s experience far more interactive, and engagingly personal.
NLP technology can guide a customer to complete a credit application, for instance, or offer personalized recommendations to someone shopping for a checking account. It can upsell them as well – for example, adding a savings account — or instantly answer a customer’s questions about products as they browse.
By collecting and interpreting rich customer data, NLP has the ability to provide returning customers with personalized recommendations for similar or related products based on data gathered from previous orders in their purchase history, even pulling in data about weather, location, or time of year to thoroughly personalize the experience and products offered.
Mining customer feedback for data-rich insights
But while NLP’s most frequent use case is AI chatbots and virtual assistants, the technology is also a valuable tool for businesses to turn customer data into powerful business insights.
The technology can analyze customer feedback, looking at the frequency of words or word groupings, or using sentiment analysis to understand a customer’s intent. It can help you systematically identify, extract, quantify, and analyze customer data to stay in tune with consumer reactions, style, and preferences.
It can also reveal a host of customer issues. That includes customer likes and dislikes, pain points, needs or requests, how well your company is living up to its customer promises, and how your product or service is performing. It can even help you nail down what your customers think your unique selling point is, your competitive differentiators, and more.
For a look at how companies like Mastercard are leveraging AI and NLP for customer service, the powerful results achieved, plus a look at the challenges of implementing an AI strategy, don’t miss this VB Live event.
Don’t miss out!
Takeaways:
- Understand the different types of AI initiatives a company can launch to improve CX based on NLP and Voice technologies
- Know how to develop those AI initiatives and the role of data on training AI/ML models
- Get to know a case study from a fintech company (Mastercard)
Speakers:
- Dr. Steve Flinter, VP of Artificial Intelligence & Machine Learning, Mastercard Lab
- Dr. Daniela Braga, CEO, DefinedCrowd
More speakers to be announced soon.
Author: VB Staff
Source: Venturebeat
