For a major telecom, feedback that arrived too late meant customer concerns turned into churn before anyone noticed, and the old communication tools could not keep up.
“Feedback is only valuable if you can act on it while the customer still cares.”
A major telecom company was seeing declining engagement and feedback, with outdated communication tools that could not capture the real-time sentiment of a vast user base. Concerns went unnoticed or were addressed late, worsening dissatisfaction.
ML arteka built a platform of APIs, a user-centric web portal, and native SDKs that let staff design and dispatch custom surveys, with an NLP-driven analytics system to interpret feedback in real time. Deep Neural Networks trained on diverse datasets and predictive models built with TensorFlow and Keras anticipate issues from feedback trends, and an offline-first design stores data locally and later synchronizes to the cloud, with native push notifications for action.
The result: real-time sentiment insight, self-serve survey creation, predictive issue detection, and a platform that became the preferred alternative to traditional feedback.
Business Outcomes
Real-time NLP and predictive models turned slow, unheard feedback into a live decision input.
NLP reads sentiment as feedback arrives.
Staff run feedback campaigns themselves.
Predictive models anticipate problems.
The Transformation
From slow, unheard feedback to a live decision input.
- 1
DiscoverUnderstand the Communication GapA deep dive into why feedback was slow and unheard, and where the outdated tools fell short for a vast user base. - 2
BuildAPIs, a Web Portal, and SDKsDelivered a user-centric platform so authorized staff could design, create, and dispatch custom surveys, with feedback captured through the SDK. - 3
UnderstandReal-Time NLP SentimentIntegrated an NLP-driven analytics system to monitor and interpret feedback as it arrives, extracting sentiment from responses. - 4
PredictAnticipate and ActTrained Deep Neural Networks and deployed predictive models with TensorFlow and Keras, with offline-first capture and native push notifications for action.
The Business Challenge
Feedback That Arrived Too Late to Act On
Declining engagement and outdated tools left a major telecom unable to hear customers in time.
Outdated tools
Existing tools could not keep pace with a vast, active user base.
No real-time sentiment
The business could not read how customers felt as it happened.
Concerns handled late
A communication lag meant issues were addressed belatedly, if at all.
Declining engagement
The gap between customers and the business was widening.
Business Outcomes in Detail
What the Numbers Mean
NLP reads sentiment as feedback arrives.
Staff run feedback campaigns themselves.
Predictive models anticipate problems.
Technology Snapshot
Capture, Understand, Predict, Act
The distance between what a customer feels and what the business does about it is where loyalty is won or lost. Real-time sentiment, delivered where staff can act, closes that distance.
Executive Questions and Answers
The questions leadership tends to ask when evaluating an approach like this.
Sentiment
How is this different from a normal survey tool?
A survey tool collects responses. This platform reads them in real time with NLP, predicts issues from the trends, and pushes the signal to teams to act, closing the loop from capture to action.
Predictive
What does the predictive capability do?
Deep Neural Network models built with TensorFlow and Keras anticipate potential issues from historical data and current feedback trends, giving a roadmap to act before problems spread.
Reliability
What happens when connectivity is poor?
Feedback is captured and stored locally, then synchronized to the cloud through APIs when the connection returns, so responses are not lost in the field.
