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Value Discovery

Value Discovery is Valtrics' AI-powered feature that helps you identify the right KPIs, spot measurement gaps, and get actionable recommendations for your products. Instead of guessing which metrics matter, let Valtrics suggest them based on your product context.

How Value Discovery Works​

Value Discovery analyzes your product's attributes — its lifecycle stage, type, existing metrics, and industry patterns — to generate tailored recommendations. It doesn't replace your judgment; it augments it by surfacing things you might not have considered.

The AI considers:

  • Product lifecycle stage — Different stages need different metrics
  • Existing KPI trees — What you're already tracking
  • Gaps in coverage — Areas where you have no metrics
  • Industry benchmarks — Common metrics for similar products

Accessing Value Discovery​

Value Discovery is available in several places:

From a Product​

  1. Open a product from the Portfolio
  2. Look for the Value Discovery section
  3. Click Get Recommendations

From a KPI Tree​

  1. Open a KPI tree
  2. Click the Suggestions button
  3. Value Discovery analyzes the tree and suggests additions or improvements

From the Sidebar​

Navigate to Value Discovery directly for a broader, portfolio-level analysis.

The Value Discovery Wizard​

The wizard guides you through a structured process:

  1. Define your product's primary goal — What is the main business outcome you're driving toward?
  2. Identify key drivers — What factors most influence that goal?
  3. Select relevant metrics — Choose from suggested metrics or define your own
  4. Generate a KPI tree — The wizard creates a starting KPI tree based on your answers

After completing the wizard, you'll have:

  • A draft KPI tree connecting your goal to suggested metrics
  • Metric recommendations based on your product type and lifecycle stage
  • A starting point you can customize and refine over time

Types of Recommendations​

KPI Suggestions​

Value Discovery can recommend new KPIs based on what's missing from your trees. For example:

  • You have a Growth-stage product with revenue metrics but no engagement metrics — Suggestion: "Consider tracking DAU/MAU ratio and feature adoption rate"
  • You have a Maturity-stage product with no cost metrics — Suggestion: "Consider tracking cost per user and infrastructure spend"

Gap Analysis​

The AI identifies areas where your measurement approach has blind spots:

  • No leading indicators — You're only tracking outcomes, not inputs
  • Missing lifecycle metrics — Your stage suggests metrics you haven't added
  • Unbalanced trees — Some branches of your KPI tree have deep detail while others are bare

Metric Improvement​

For metrics you're already tracking, Value Discovery can suggest:

  • Better target setting — Based on typical ranges for your product type
  • Related metrics — Complementary measurements that give a fuller picture
  • Decomposition — Ways to break a high-level metric into more actionable sub-metrics

Using Recommendations​

When Value Discovery presents a suggestion, you can:

  1. Apply it — Click to add the suggested KPI or metric to your tree
  2. Dismiss it — Click to remove it from the list
  3. Save for later — Bookmark the suggestion for future consideration

Applied suggestions are added as new nodes in the relevant KPI tree. You can then edit them, set targets, and attach actual values like any other node.

Getting Better Recommendations​

The quality of AI recommendations improves with better context:

Provide Complete Product Details​

The more Valtrics knows about your product, the better the suggestions:

  • Set the correct lifecycle stage
  • Add a meaningful description
  • Assign an owner
  • Apply relevant tags

Build Initial KPI Trees​

Even a simple tree gives Value Discovery something to analyze. A product with no KPI tree gets generic recommendations. A product with a partial tree gets targeted suggestions for the gaps.

Update Metrics Regularly​

Value Discovery uses metric health information to tailor suggestions. If all your metrics are stale, the AI can't assess which areas need improvement.

Common Value Discovery Scenarios​

"I'm new to KPIs"​

Value Discovery can suggest an entire initial KPI tree structure for your product based on its type and lifecycle stage. This gives you a starting framework that you can customize.

"My metrics feel random"​

If your existing KPIs don't connect clearly to business outcomes, Value Discovery can suggest a restructured hierarchy that creates clearer cause-and-effect relationships.

"I need to report to leadership"​

Value Discovery can suggest portfolio-level KPIs that roll up from individual product metrics, making it easier to tell a coherent performance story.

"We're changing strategy"​

When a product moves to a new lifecycle stage or pivots its strategy, Value Discovery immediately adjusts its recommendations to match the new context.

Frequently Asked Questions​

Does Value Discovery access my data? Value Discovery analyzes your product attributes, KPI tree structure, and metric health. It does not share data across organizations.

Can I turn off Value Discovery? Value Discovery is opt-in. You choose when to request recommendations. It doesn't make changes to your data without your action.

How often should I use Value Discovery? We recommend checking when:

  • You create a new product
  • You build or modify a KPI tree
  • A product changes lifecycle stage
  • You're preparing for a strategy review

Are the recommendations industry-specific? Recommendations are based on general product management best practices and your specific product context. As Valtrics grows, recommendations will become more industry-specific.

Need help? Check our FAQ or contact support.