Easily Ingest Historical Review Data
Input data from different sources and aggregate customer reviews. Automatically detect text language and perform automatic spell checks with ready-to-use Text Preparation recipes.
Combine Traditional NLP Models and GenAI to Generate Insights
Be it to summarize reviews, extract topics, or qualify sentiment, leverage the provided NLP and GenAI approaches and quickly adjust to needs. Take a decisive step towards structuring your understanding of customer feedback, and build replicable approaches across lines of products and services.
Analyze Various Product and Service Reviews Over Time
Explore reviews through a series of interactive and predefined dashboards. Filter by product or service ID, product category, or use the Reviews Explorer App to analyze product ratings over time and by country.
Learn How Customers Describe Your Products and Services
Explore reviews on specific product categories or SKUs. Select a product and get a list of the most popular keywords customers use to describe them.
Answer Key Customer Satisfaction Questions
The Dataiku Solution for Customer Satisfaction Reviews helps answer a broad range of questions like:
- How do I analyze past reviews to understand what customers think about my products and services?
- How can I study past reviews to evaluate their purchasing experience?
- How can I use the reviews explorer to find relevant reviews on specific topics?
- How can I align scores to feedback in reviews to refine my understanding of feedback received?
Turn Reviews Into Customer Loyalty
Deploy analysis of past reviews to explore customer sentiment. Zoom on review types and explore specific keywords with an NLP-powered search engine. Turn data into actionable insights and integrate them across selling strategies to develop customer loyalty.
The Total Economic Impact™️
Of Dataiku
A composite organization in the commissioned study conducted by Forrester Consulting on behalf of Dataiku saw the following benefits:
reduction in time spent on data analysis, extraction, and preparation.
reduction in time spent on model lifecycle activities (training, deployment, and monitoring).
return on investment
net present value over three years.
