Using KPIs.do for A/B Test Analysis: Measure Impact as Code

A/B testing is the lifeblood of data-driven product development. You launch an experiment, eagerly anticipating the results that will validate your hypothesis and improve user experience. But what comes next is often a tedious process of manual data pulling, spreadsheet gymnastics, and dashboard-checking that slows you down.

What if you could treat experiment analysis not as a manual chore, but as an automated, programmable service? What if your key A/B test metrics were defined, version-controlled, and tracked right alongside your application code?

This is the power of "KPIs as Code." With a platform like KPIs.do, you can transform your A/B test analysis from a reactive, manual process into a proactive, automated workflow that delivers insights directly where you work.

The Manual Grind of A/B Test Monitoring

For most teams, the cycle of A/B test analysis is filled with friction:

• Delayed Insights: Product managers and engineers have to wait for a data analyst to run SQL queries or for a daily dashboard to refresh. By the time you get the data, you might have lost valuable hours or even days.
• Constant Context-Switching: To check on an experiment, you have to leave your IDE, Git, or project board to log into a separate BI tool. This constant switching disrupts deep work and slows down the development cycle.
• Inconsistent Logic: How is conversion rate really being calculated for this test? Is it unique users or total sessions? When metrics aren't defined as code, the logic can be opaque and lead to inconsistent interpretations across the team.
• Alerting is an Afterthought: You only find out if a variant is tanking your conversion rate when you remember to check. There's no automated system to alert you the moment a negative trend emerges or when a variant reaches statistical significance.

This manual approach is a bottleneck. It keeps valuable data locked in silos and prevents your team from making decisions at the speed of development.

From Manual Queries to Automated Services: A/B Testing with KPIs.do

KPIs.do enables you to manage your experimentation metrics just like you manage your infrastructure: with code. By defining your A/B test KPIs as a service, you create a single source of truth that automates calculation, tracks performance, and integrates seamlessly into your existing tools.

Step 1: Define Your Experiment KPIs as Code

Before you even ship your experiment, you can define the metrics that will determine its success. Using a simple, version-controlled configuration file, you establish the "what" and "how" of your analysis. This creates a transparent and repeatable contract for your experiment's performance.

Imagine you're testing a new signup flow. You can define a KPI that tracks the conversion rate for both the control and challenger variants.

Example kpi-config.yml:

kpi: signup-conversion-rate
description: "Tracks conversion rate for the new signup page A/B test."
metric: conversionRate

# Define the logic for calculating the metric
calculation:
  numerator: unique(userId where event='signup_complete')
  denominator: unique(userId where event='view_signup_page')
  window: 7d # Calculate over a 7-day rolling window

# Define the dimensions we can slice this KPI by
dimensions:
  - experimentId
  - variantName # 'control' or 'challenger'

# Set up automated alerts
triggers:
  - name: "Significant Winner Alert"
    condition: |
      kpi(variantName='challenger').value > kpi(variantName='control').value * 1.05 &&
      statisticalSignificance > 0.95
    action:
      type: slack
      channel: '#growth-experiments'
      message: "🚀 A/B Test `{{ kpi.dimensions.experimentId }}` has a winner! Challenger is up by {{ (kpi(variantName='challenger').value / kpi(variantName='control').value - 1) | percentage }}."

  - name: "Negative Impact Alert"
    condition: |
      kpi(variantName='challenger').value < kpi(variantName='control').value * 0.9
    action:
      type: pagerduty
      service: 'product-on-call'
      summary: "🚨 Critical Drop: A/B test challenger variant is significantly underperforming!"

This configuration file is now the heart of your experiment's analysis. It's readable, portable, and can be checked into your Git repository right alongside the feature code.

Step 2: Stream Data with a Simple API Call

With your KPI defined, your application simply needs to send the raw event data to the KPIs.do platform. Instead of complex ETL pipelines, your engineers can use a straightforward SDK to track user actions as they happen.

The kpis.do agentic platform listens for these events and handles all the complex calculations defined in your config.

import { kpis } from 'kpis.do';

// When a user sees a page in the experiment
// This contributes to the 'denominator' in our calculation
await kpis.track({
  name: 'signup-conversion-rate', // The KPI we are tracking
  data: {
    event: 'view_signup_page',
    userId: 'user-xyz-123'
  },
  dimensions: {
    experimentId: 'q3-new-signup-flow',
    variantName: 'challenger'
  }
});

// When that same user successfully signs up
// This contributes to the 'numerator'
await kpis.track({
  name: 'signup-conversion-rate',
  data: {
    event: 'signup_complete',
    userId: 'user-xyz-123'
  },
  dimensions: {
    experimentId: 'q3-new-signup-flow',
    variantName: 'challenger'
  }
});

Your code's responsibility is simple: report events. The kpis.do service takes care of the rest.

Step 3: Enjoy Automated Analysis and Alerting

Once your data is flowing, KPIs.do becomes your automated analysis engine. It continuously:

1. Ingests data from your API calls.
2. Calculates your conversion rates for each variant in real-time, according to your version-controlled logic.
3. Monitors for the conditions you defined in your triggers.

The moment a variant reaches statistical significance or a key metric drops unexpectedly, your configured actions are fired. Your team gets an immediate Slack message. A PagerDuty alert is triggered. A winning feature flag could even be rolled out automatically via a webhook. The manual loop of checking dashboards is broken forever.

The Real-World Impact: Faster Decisions, Better Products

Adopting a "KPIs as Code" approach for A/B testing fundamentally changes how you operate:

• Accelerated Velocity: Conclude experiments with confidence the moment they produce a clear result. Ship winning features faster and iterate more quickly.
• Radical Transparency: Engineers, product managers, and data scientists all share a single source of truth for metric definitions. No more "how was this calculated?" debates.
• Proactive Performance Management: Catch underperforming experiments early and cut your losses. Celebrate wins with the entire team the moment they happen.
• Seamless Integration: Pipe A/B test results directly into the tools your team already uses, from Slack and Jira to your own internal CI/CD pipelines.

Stop Analyzing, Start Automating

Your A/B tests are a critical part of your development process; their analysis should be too. By moving your experiment metrics out of brittle dashboards and into version-controlled code, you unlock a new level of speed, collaboration, and automation.

Ready to turn your A/B test analysis into an automated, measurable service? Explore KPIs.do and start treating your business performance as code.