Understand customer feedback at scale

OmniAI makes it easy to transform and enhance customer feedback data across your warehouse. Unlock more value from your data across millions of rows.

Hero image


Great data without a great way to use it

Companies often collect user feedback and reviews without the ability to extract quantitative information from that data. It's critical to know what your customers like/dislike about your product, but impossible to make quality decisions without quantified data.

Spot checking the occasional review can even be a net negative, as you risk sampling bias and overreacting to false signals.


Decoding customer feedback with OmniAI

With OmniAI, transform this unstructured feedback into structured, actionable insights. Connect your data source, define key metrics like sentiment and complaints, and let OmniAI handle the heavy lifting of data processing.

It's about turning raw feedback into usable data with minimal fuss. In this case study, we’ll do the following:

  • Grab 10,000 recent reviews for HubSpot
  • Store the resulting data in the Omni warehouse
  • Perform analytics on performance over time

    Define your schema

    Here, we’ve defined a few values (COMPLAINTS, SENTIMENT, SWITCHED_FROM, SWITCHED_TO) along with their various types & options. Note, we could leave options undefined if we want a more freeform answer.When we run the sync, we’ll have a table that combines our existing data, with the newly extracted columns.
    2 description: "User sentiment about the product"
    3 options:
    4 - "POSITIVE"
    5 - "NEGATIVE"
    6 - "NEUTRAL"
    7 type: "String"
    10 description: "Complaints the user had about the product"
    11 options:
    12 - "COMPLICATED"
    14 - "EXPENSIVE"
    18 - "BUGS"
    19 type: "String[]"
    Transform data image

    Transform your data

    Now Omni has generated a new table combining the original data set with the extracted values. From here, we can query this table directly with SQL, or ingest it into a variety of BI tools.For example, the review containing “The reporting might as well be non-existent. It is not even close to parity with a CRM like Salesforce…” was tagged with SALESFORCE in the SWITCHED_TO field.


    Learning from the results

    Now that we’ve turned our qualitative data into structured values, we can query those values and extract insights that weren’t possible from the raw text alone.

    In this case study, we looked at the frequency of certain competitors in the SWITCHED_TO category. This looked at any review where the user mentioned switching away from HubSpot to a competing platform.

    [@portabletext/react] Unknown block type "image", specify a component for it in the `components.types` prop

    In the last year, there’s been a pretty steep decline of reviews recommending Salesforce over HubSpot (i.e. “we migrated to Salesforce”).

    E-Commerce resources

    See how OmniAI helps different use cases in e-commerce.

    Case study icon

    Tagging high-risk products

    Analyze product descriptions and reviews to identify and flag potential hazards, ensuring customer safety and compliance.
    Case study icon

    De-duplicating products

    Detect and merge duplicate product listings to maintain a clean, user-friendly shopping experience.
    Case study icon

    Sentiment analysis in reviews

    Extract and analyze customer sentiment from reviews to inform product development and marketing strategies.

    Ready to try OmniAI?

    Book a demo today and see how our solution can transform your workflow.

    Join our Slack to connect with the Omni team and engage with our community.
    Set up your environment and connect to our API.
    © 2024 OmniAI Technology, Inc.