1. Challenges
Amidst a backdrop of declining customer engagement and feedback, our client, a major telecom company, grappled with outdated communication tools that failed to capture real-time sentiments of their vast user base. This communication lag meant that customer concerns often went unnoticed or were addressed belatedly, exacerbating dissatisfaction levels. The pressing need was to bridge this communication gap, ensuring that customer feedback was not just captured, but also acted upon promptly and effectively.
2. Approach
To tackle these challenges head-on, a deep dive into the problem was initiated. The strategy revolved around the creation of a comprehensive platform that included APIs, a web portal, and native SDKs. The web portal was envisioned to be user-centric, allowing authorized personnel to swiftly design, create, and dispatch custom survey forms. To further amplify the platform’s capabilities, an NLP-driven analytics system was integrated to monitor and interpret user feedback in real-time. Machine learning models, especially Deep Neural Networks (DNNs), were trained on diverse datasets to ensure a holistic pattern recognition and predictive analytics approach.
3. Solution
The outcome was a robust platform that comprised APIs, a web portal, and native SDKs. The web portal, designed for ease of use, empowered authorized staff to quickly design, create, and send out custom survey forms. Feedback data, collected via the SDK, was processed using NLP algorithms to extract insights and gauge sentiment from user responses. Predictive models, developed using TensorFlow and Keras, were deployed to anticipate potential issues based on historical data and current feedback trends. In situations where internet connectivity was compromised, the data was stored locally and later synchronized with the cloud service through APIs. This centralized data hub, augmented with AI capabilities, equipped teams to analyze reports, forecast potential challenges, and address concerns via the platform’s native push-notifications.
4. Success
Following the successful rollout of the AI-enhanced platform and mobile applications, company personnel were now equipped to design, create, and release surveys tailored to their target audience. The NLP-driven analytics offered profound insights into user sentiment, enabling real-time feedback on product launches and services, and bolstering business-customer engagement. The predictive models provided the company with a roadmap to proactively tackle potential issues, ensuring a frictionless user experience. The platform quickly became a preferred alternative to traditional feedback mechanisms, solidifying its place in the company’s communication arsenal.
