You might occasionally encounter customer feedback with an incorrectly predicted Reason, or user sentiment due to the probabilistic nature of machine learning. We are happy to announce that you can now directly edit predictions and make corrections, and your edits will improve the accuracy of predictions across your Enterpret instance.

Moreover, our models would learn from your corrections to avoid similar mistakes in the future, and improve predictions on other records.

Looking forward to your thoughts and comments!

Twitter, the most popular platform for free discussion, can be one of the best places to look to gather public opinion and gauge product sentiment. However, like with any conversation, looking at a single tweet in an entire thread is often unhelpful without its context.

With that in mind, we are delighted to introduce Twitter threads! Now see all tweets on Enterpret, along with the entire conversation thread leading up to it.

Additionally, you can view all the tweets of a conversation and their predictions by simply filtering with Conversation ID - which is the tweet ID of the root tweet in the thread.

We hope this helps with keeping in sync with your customers' needs! Do tell us what you think!

Gong is an excellent tool for sales insights, but it’s a black box for product teams. Enterpret opens that black box and builds awareness about what your customers share on Gong.

With this new integration, Enterpret automatically ingests Gong calls every few hours, along with all the metadata present in Gong from Salesforce. It then summarises it, and just like every other feedback ingested in Enterpret - the taxonomy is updated, and the feedback is tagged with it. Some of the capabilities are:

• Analytics on the content of Gong calls
  ‍
  See how tracked keywords are trending over a period of time. Quickly identify the top reasons of feedback. Slice and dice them by Salesforce metadata properties like opportunity stage, name, etc.

• Do self-serve research
  ‍
  Identify reasons of feedback for competitors and features alike. Find Gong calls where your feature was talked about negatively or a competitor was talked about positively. Listen to the snippet directly in Enterpret and share it with your team.

As always, we greatly value your input on what we’ve shipped, as well as what we’re currently working on. 🙏

Introducing a more powerful natural language search!

Now search for multiple texts at a time using the any of and all of operators.

Additionally, search for feedback matching the exact query using the exactly matches operators.

We hope these improvements make searching for relevant feedback easier. Let us know what you think!

We're happy to share that we've made finding help on Enterpret easy!

At any point on the Enterpret dashboard, you can click on the (?) on the bottom right of your page, and access contextual help documents.

Moreover, you can always find help and documentation at helpcenter.enterpret.com or reach out to our customer success lead Jack Divita at jack@enterpret.com

We'd love your feedback on how we can make finding help on Enterpret even easier. Looking forward to hearing from you!

There are some data sources where the structure of customer feedback and associated metadata can differ greatly from integration to integration. Such as Snowflake tables, custom CSV File Uploads, and Salesforce objects.

We're excited to announce that we've made analysis of feedback from such sources easier by making the source name more meaningful. For example, if you integrate a Snowflake table named "Churned Users", records from this integration will show up in your analyses under the source SNOWFLAKE Churned Users, allowing you to easily distinguish this integration from other integrated Snowflake data tables.

We hope that this will make analyzing customer feedback from these sources easier. Looking forward to your feedback and suggestions!