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Building intelligent post-feedback workflows

Create custom dashboards or conduct deep dives on the feedback data using Blitzllama's Spotlight.

August 12, 2022
Rahul Mallapur

Fast-growing product companies build extensive in-product feedback collection prompts across the product journey but the post-feedback processes are often broken. Let’s consider the in-product collection and post-collection processes of a typical B2C subscription app.

The app has multiple in-product feedback touchpoints: NPS prompts for D30 and D90 customers, feedback after subscription cancellation, post-core loop feedback prompt (eg. every 25th meditation on Headspace, every 100th Youtube search, etc.), and “leave us a feedback” section. And then could be user experience surveys of newly launched features.

But the post-feedback collection processes are broken.

  • Teams use multiple tools like Typeform, Clevertap or in-house feedback prompts to conduct in-product feedback. The feedback data flows into different repositories. And mostly no one will have visibility into all the collected feedback.
  • It’s super time-consuming to categorize - the continuous and massive feedback data into authentic (“feature consumes 10 MB for 1 minute of use”) vs spam (“ewfie efeiown woei”), hence textual data is often overlooked.
  • High friction to map feedback data to quantitative user data (eg. to figure out what high LTV users say) as feedback tools and analytics tools are disconnected. Product and data teams have to download data from multiple tools and merge it in Excel or Google Sheets.
  • Non-product teams like strategy or user research teams have to rely on product or data teams to generate insights because they lack access to these tools, can’t run SQL queries, or work with complex Google Sheets.
  • If there is no ownership on a feedback channel (i.e. no one is responsible to check “leave us feedback” data), all the valuable insights will be lost or too late.

And so on …

All in all, the point is that post-feedback processes are pretty much broken in fast-moving companies. If remedied, then product, growth, and leadership teams in these companies can operate with high clarity on user needs and efficiently uncover opportunities for growth

Blitzllama’s Spotlight fixes these issues

The Spotlight feature adds an intelligence layer over all your in-product user feedback data.

1. A single feedback repository aggregates data across surveys and platforms.

2. Advanced NLP auto-categories textual feedback into actionable insights.

3. UI-based query builder makes it easy to play around with the data.

4. A customizable dashboard allows continuous monitoring of user issues in real-time across cohorts and platforms.

Using Spotlight to build intelligent post-feedback flows

A. Create dashboards for teams to actively monitor feedback trends across existing surveys.

A1: Product teams →

A2. Leadership team →

A3. Growth teams →

A4. Engineering teams →

B. Conduct deep dives by analyzing data across surveys, cohorts, and platforms. The UI-based query builder makes it intuitive for all teams to dig into feedback.

B1. Cohort-wise NPS ratings across surveys

B2. View all the feature suggestions in the last 30 days. Especially useful for engineering and product teams.

B3. Download a list of users who gave 1-star reviews for feedback. Useful for user research and product teams.

B4. Check out what users are saying at a specific point in the app. Product and Analytics teams can use it to build a hypothesis on funnel dropoffs.

Would you like to add no-code and intelligent feedback monitoring to power your teams?
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