Automatic Complaint Classification Process Helped a South-East Asian Airline Turn Around Maintenance Activity






Customer Feedback Analysis & Prediction Framework

Feedback categorization into desired issue categories achieved a 90% accuracy.

5x organization-wide time-savings in NLP categorization project time, as our DS didn’t have to re-work on codes and metrics but rather on driving results. Previously, categorizing 200 feedbacks took the Airline about 10-12 hrs which is now reduced to 1-2 hrs. Simply put, codes and flow were modularized, so it was reused across the organization.

The engineering team, now, receives a live report to identify the aircrafts or facilities/services which need immediate attention based on complaints being made.

A South-East Asian Airline needed help in addressing customer complaints and improving their overall travel experience. Typically, the team would have to manually go through each feedback to identify a specific line of maintenance for each aircraft, which was a very time-consuming process.

TheMathCompany partnered with the Airline to reclassify issues, establish multi-level categorization to help the engineering team to develop an automatic complaint classification process with predictive capabilities. Natural Language Processing (NLP) techniques to identify issue categories and fast track this process was suggested as a solution.

Multi-label feedback categorization was a challenge that the Airline was yet to tackle. For this, we used multiple classification techniques to build a predictive classification model and an automatic complaint classification model for each category.

Data cleaning- Ensured all text was cleaned to remove unnecessary characters (stop words, punctuation, proper nouns, etc.). Lemmatization and stemming of words.

Feature creation- Document term matrix on text corpus and n-grams.


Two incongruous levels (L1& L2)

→ Built a model for each L2 category, post considering L1 classification

We added synthetic feedback based on words, for categories with a very low count of feedback (class imbalance).

For feedback with low confidence scores, we decided to consider them under the unclassified category which would be manually classified later and fed back to the model as input to train further for feedback categorization.

Deployment- The automatic complaint classification model runs monthly post collection of all feedback from various sources like emails, social media, website, etc. The UI gives the business a view of the feedbacks and categories they fall under. Users can review and re-classify incorrect feedback, which would be fed into the model to re-train the same.

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