Chart Deep Dive

When to Use

• •A metric spiked or dropped unexpectedly
• •You need to understand what’s driving a trend
• •Preparing a detailed, evidence-backed analysis for stakeholders
• •Investigating differences between user or event segments

Instructions

Step 0: Identify the Chart

• •Accept a chart URL or chart ID
• •If the user provides a URL, use Amplitude:getting_data_from_url to extract the chart ID
• •If no chart identifier is provided, ask explicitly for the chart URL or ID and stop

Step 1: Retrieve and Validate Chart Data (Mandatory)

• •Use Reading chart data to retrieve the chart definition and data
• •If chart data cannot be retrieved or is empty, do not proceed
  • •Explain what’s missing (time range, event, filters, permissions)
  • •Ask the user to correct the chart or provide a valid chart

Capture and restate:

• •Metric being measured
• •Time range and granularity
• •Chart type (e.g. time series, funnel, retention)
• •Existing filters, segments, or breakdowns

Step 2: Identify the Pattern and Change Window

Use Analyzing chart to characterize what’s happening:

• •Spike / Drop: Sudden change on specific date(s)
• •Trend: Gradual increase or decrease over time
• •Seasonality: Recurring weekly or monthly patterns
• •Anomaly: Deviation from recent baseline or historical behavior

Explicitly identify:

• •The window of change (start/end)
• •Direction and magnitude of the change
• •Baseline period used for comparison (default: previous equal-length period)

Step 3: Investigate Likely Drivers (Bounded)

Instead of broad slicing, use guided segmentation:

1. •Use Finding the right event properties to identify the most relevant properties for explaining the change
2. •Select up to 9 high-signal properties (e.g. platform, country, plan, version)
3. •Re-run Analyzing chart with these properties in mind to determine:
   • •Which segments contribute most to the change
   • •Whether the pattern is localized or broad-based
   • •Only fetch up to 3 charts at a time when using Amplitude:query_charts

Avoid testing more than 9 properties in aggregate unless the user explicitly asks for deeper exploration.

Step 4: Correlate with Context (Required for Anomalies)

For spikes, drops, or unexpected shifts, gather contextual signals in the same timeframe:

• •Use Getting experiments to identify active experiments or flags
• •Use Getting deployments to identify releases or rollouts
• •Use Searching for content to surface annotations or relevant documentation
• •Use Amplitude:get_feedback_insights to search customer feedback trends that might explain the change
• •Use Amplitude:get_feedback_mentions to pull in specific customer mentions if there's a likely feedback trend tied to what's being explained.

Determine whether any contextual changes align temporally with the chart pattern.

Step 5: Synthesize Findings

Present a structured, decision-ready analysis:

1. •

   What Happened
   Clear description of the observed pattern and magnitude

2. •

   When
   Exact timeframe and comparison baseline

3. •

   Primary Hypothesis
   Most likely explanation based on chart data and contextual signals

4. •

   Supporting Evidence

   • •Key metrics
   • •Segment contributions
   • •Relevant experiments, deployments, or annotations
5. •

   Alternative Explanations
   1–3 plausible alternatives and why they are less likely

6. •

   Impact
   Quantify impact where possible (users, events, conversion, revenue proxy)

7. •

   Recommended Next Step
   One clear follow-up action (e.g. deeper segment, experiment review, instrumentation check)

Always include:

• •Chart name
• •Chart ID
• •Link back to the chart
• •Coverage (e.g. properties tested, segments analyzed)

Best Practices

• •Always compare against a clear baseline period
• •Distinguish observations from hypotheses
• •Prefer high-signal segmentation over exhaustive slicing
• •Note data quality issues (low volume, incomplete periods, heavy “(none)” values)
• •Do not create or edit charts unless the user explicitly asks