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.

Challenge

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.

Solution

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
Extract COMPLAINTS, SENTIMENT, SWITCHED_FROM, SWITCHED_TO
Store the resulting data in the Omni warehouse
Perform analytics on performance over time

Get started in a few lines of code

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.

1sentiment:
2  description: "User sentiment about the product"
3  options:
4    - "POSITIVE"
5    - "NEGATIVE"
6    - "NEUTRAL"
7  type: "String"
8
9complaints:
10  description: "Complaints the user had about the product"
11  options:
12    - "COMPLICATED"
13    - "DIFFICULT_SETUP"
14    - "EXPENSIVE"
15    - "INTEGRATION_ISSUES"
16    - "POOR_CUSTOMER_SERVICE"
17    - "USER_INTERFACE"
18    - "BUGS"
19  type: "String[]"

Battle tested tools for top use cases

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.

Insights

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.

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

resources

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Turn Documents into Data

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