ROLE: You are a Web Analyst or E-commerce Data Analyst.
TASK: Analyse the customer journey funnel data from {DATA_SOURCE e.g., Google Analytics 4} for the period {DATE_RANGE}. Identify the conversion rates between key stages and pinpoint the biggest drop-off points.
CONTEXT:
Key Funnel Stages to Analyse:
{STAGE_1 e.g., Homepage Visits} ->
{STAGE_2 e.g., Category Page Views} ->
{STAGE_3 e.g., Product Page Views} ->
{STAGE_4 e.g., Add to Cart} ->
{STAGE_5 e.g., Checkout Start} ->
{STAGE_6 e.g., Purchase Completion}.
Data Source: @{ANALYTICS_REPORT_FILE} or describe data.
Goal: Understand where users are abandoning the purchase path to inform optimisation efforts.
FORMAT: 1. List the conversion rate (%) between each defined stage. 2. Identify the top 2 stages with the highest drop-off rates (%). 3. Provide a brief interpretation or potential reason for the major drop-offs.
You are a Web Analyst or Ecommerce Data Analyst. Analyse the customer journey funnel data from {DATA_SOURCE} (@{ANALYTICS_REPORT_FILE}) for the period {DATE_RANGE}. Identify the conversion rates between key stages: {STAGE_1} -> {STAGE_2} -> {STAGE_3} -> {STAGE_4} -> {STAGE_5} -> {STAGE_6}. Pinpoint the biggest drop-off points. FORMAT: 1. List the conversion rate (%) between each defined stage. 2. Identify the top 2 stages with the highest drop-off rates (%). 3. Provide a brief interpretation or potential reason for the major drop-offs.Why it works: Provides crucial insights into the customer purchase path, highlighting the most critical areas for conversion rate optimisation efforts based on user behaviour data.
