[{"data":1,"prerenderedAt":85},["ShallowReactive",2],{"work-customer-feedback-nlp":3,"work-next-\u002Fwork\u002Fcustomer-feedback-nlp":82},{"id":4,"title":5,"accent":6,"body":7,"client":50,"cover":51,"coverAlt":51,"description":52,"duration":53,"extension":54,"featured":55,"industry":56,"meta":57,"navigation":58,"order":59,"path":60,"quote":51,"results":61,"seo":68,"services":69,"stack":72,"stem":79,"year":80,"__hash__":81},"work\u002Fwork\u002Fcustomer-feedback-nlp.md","NLP for customer feedback analysis","#0E7490",{"type":8,"value":9,"toc":43},"minimark",[10,15,19,23,36,40],[11,12,14],"h2",{"id":13},"challenge","Challenge",[16,17,18],"p",{},"Product managers were reading a sample of reviews and guessing at trends. Urgent issues surfaced late.",[11,20,22],{"id":21},"what-we-built","What we built",[24,25,26,30,33],"ul",{},[27,28,29],"li",{},"Fine-tuned transformer classifiers for topic and sentiment, plus an urgency detector for churn-risk language.",[27,31,32],{},"A pipeline that ingests app-store reviews, support tickets and NPS comments daily.",[27,34,35],{},"A dashboard and automated weekly report highlighting movers, emerging topics and representative quotes.",[11,37,39],{"id":38},"results","Results",[16,41,42],{},"Every piece of feedback is now categorised, and the product team gets a consistent weekly read on what customers care about, with urgent issues flagged the same day.",{"title":44,"searchDepth":45,"depth":45,"links":46},"",2,[47,48,49],{"id":13,"depth":45,"text":14},{"id":21,"depth":45,"text":22},{"id":38,"depth":45,"text":39},"Consumer app · subscription (UK)",null,"Topic, sentiment and urgency models over reviews and tickets, feeding a weekly voice-of-customer report.","6 weeks","md",false,"SaaS & Technology",{},true,5,"\u002Fwork\u002Fcustomer-feedback-nlp",[62,65],{"value":63,"label":64},"50k+","reviews and tickets classified per month",{"value":66,"label":67},"Weekly","automated voice-of-customer report",{"title":5,"description":52},[70,71],"nlp","data-analytics-predictive-ai",[73,74,75,76,77,78],"Python","Hugging Face","scikit-learn","Pandas","Plotly","Prefect","work\u002Fcustomer-feedback-nlp","2023","teESibsYK1f8T-u8lvNDT0PApXnXs30CUEn2hYO9-i8",{"path":83,"title":84},"\u002Fwork\u002Fenterprise-rag-knowledge-base","RAG knowledge base for an enterprise services firm",1792973683879]