Consumer feedback & marketing
NUT Botanicals
Multilingual customer sentiment analysis
Measured results
+30%
customer satisfaction
+25%
product quality
−20%
complaint volume
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 8
The problem
Customer feedback was scattered across social platforms, marketplaces and surveys, with no way to read it as one signal or act on it in product decisions.
What was built
A sentiment classification engine that aggregates every channel and reports it against product lines.
Technical approach
- Aggregation
- Social media, e-commerce platforms and survey responses collected into one pipeline.
- Classification
- Naive Bayes, SVM and BERT models trained on the client's own labelled feedback.
Measurement framework
Deployment evidence is drawn from the published engagement. The delivery and measurement plans are case-specific recommendations grounded in the documented solution and results.
- Evidence status
- The source documents a deployed feedback-intelligence capability spanning customer channels, supported by reported changes in satisfaction, product quality and complaint volume.
- Indicative delivery plan
- Connect feedback channels, agree the sentiment and issue taxonomy, validate labelled samples, route insights to product and service owners, then monitor corrective actions.
- Recommended baseline
- Recommended: a matched pre-deployment period for customer-satisfaction score, product-quality indicators and complaint volume across the same social, marketplace and survey channels.
- Recommended measurement method
- Use a phased rollout or matched-period comparison with stable category definitions. Audit a stratified sample for precision and recall, then link operational changes to dated product or service actions rather than model output alone.
Recommended items are not presented as the original study method. They should be replaced with the confirmed project methodology when the underlying records are approved for publication.