Millennials or not, customers expect high-touch, anticipatory, individualized, and timely engagement that is delightful, every time.
Needless to say, the success of a business is directly proportional to its ability to acquire customers, nurture them, keep them happy, address their concerns, and consequently make more money from them. We believe that companies can do these remarkably well if they start by listening more to their customer.
Using NLP, Computer Vision, and other ML techniques, it's possible to build predictive customer analytics solutions that understand the true intent of your customers' conversation, create and deliver timely and relevant marketing messages to upsell and cross-sell products and services, improve the efficiency of customer service, build more customer-tailored products while reducing churn and increasing loyalty..
Here are some customer engagement use cases that can be game-changing for your company.
Analyze unstructured feedback via any communication channel (email, call center, surveys, social media) and manage customer experience like none other using NLP.
Know who and what your customers are discussing and when, and how they feel about your products and services. Understand customer pain points like never before.
We include multiple behavioral data, purchase process, marketing stats to create machine learning based models that can predict churn analysis (with almost 90% accuracy or more) for micro segments of your customers.
Voice of the Customer
Use customer predictive analytics to start making sense of the chatter across all digital platforms into actionable insights for marketing, product development, operations, human resources, and customer service.
Analyze thousands of social media comments, online reviews, and free-text survey responses in minutes. Hear what they’re saying in their own words. Understand exactly how customers feel, and why they feel that way.
Visual Emotion Analysis
Use Facial AI analytics to understand customer reaction to various products and promotions instead of usual textual survey.