Market Research

Case study; Global market research agency

A global U.S. research agency uses Relevance AI to quickly extract key themes and insights from their survey projects with open ended feedback.

Problem

The agency estimated that hand-labelling data would take days.

200X

faster compared to hand-labelling

Solution

With Relevance AI, their team was able to get accurate results within an hour.

<1hr

from problem start

Consistency

More data would be processed at scale with better consistency.

>250k

responses analyzed

Power-packed-with features

Relevance AI helps you clean your data, write complex queries, run models, and ship beautiful dashboards and apps.

Visualizations

Tableau-like charts purpose built for unstructured data.

Semantic search

Semantic search and filter to drill down.

Auto taxonomy

Finds clear cut categories automatically, or BYO.

API

Everything is accessible via an API.

API

Everything is accessible via an API.

Visualizations

Tableau-like charts purpose built for unstructured data.

Semantic search

Semantic search and filter to drill down.

Auto taxonomy

Finds clear cut categories automatically, or BYO.

Semantic search

Semantic search and filter to drill down.

Auto taxonomy

Finds clear cut categories automatically, or BYO.

Visualizations

Tableau-like charts purpose built for unstructured data.

API

Everything is accessible via an API.

Bring your data to life. Start free.

On top of some mind-bendingly useful AI tools, key features include:

With Relevance AI

Customize state-of-the-art models by training with your data & adapt to changing data or goals with a a few lines of code.

Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.

Label thousands of data points programmatically in hours while keeping your data in-house and private.

With Relevance AI

100% manual effort | Hand-labelled coding is expensive, with usually no way or time to iterate, adapt or reuse.

NPS text analytics tools like Medallia, Kapiche | Vendor models often don’t work on your data, no way to customise, adapt, or audit.

Rules-based approaches often don’t perform well on complex data or adapt easily to data or goal changes.

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