Response Curve Analysis

  • Published on Sep 16, 2025
  • 1 minute(s) read
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Overview

The Response Curve Analysis widget helps marketers analyze the relationship between a marketing input (such as ad spend or campaign engagement) and a response metric (such as pipeline value or bookings). This visualization provides insights into how changes in marketing investments impact business outcomes.

Key Features

  • Marketing Input Selection: Choose a marketing variable (e.g., Google Spend, Facebook Spend, Email Marketing) to analyze its impact.

  • Response Metric Selection: Select a business outcome metric (e.g., Pipeline Value, Booking Value) to see its response to the chosen marketing input.

  • ROI Curve Toggle: If the selected marketing input is a spend-related variable (e.g., Google Spend, LinkedIn Spend), you can enable the ROI Curve to see how returns scale with investment.

  • Best Fit Line Toggle: Enable this option to overlay a best-fit curve for trend analysis.

  • Log Scale for Y-Axis: Use this option to better visualize exponential growth patterns.

Graph showing the relationship between Google spend and generated pipeline metrics.

How to Use the Widget

  1. Select a Marketing Input:

    • Click the Marketing Input dropdown.

    • Choose a variable such as "Quarterly Google Spend" or "Quarterly LinkedIn Spend."

  2. Select a Response Metric:

    • Click the Response Metric dropdown.

    • Choose an outcome metric such as "Quarterly Generated Pipeline Value" or "Quarterly Generated Booking Volume."

  3. Enable ROI Curve (if applicable):

    • If the selected marketing input is a spend variable, the ROI Curve toggle will be available. Switch it on to view return trends.

  4. Adjust Visualization Options:

    • Toggle Best Fit Line to add a regression curve. Represents what curve best explains the relationship. For e.g., the below is an exponential decay curve which says the increase in Google spending is resulting in exponentially lesser increase in Pipeline volume.

    • Use a Log Scale for the Y-axis for a better representation of high-range data.

  5. Interpret the Graph:

    • The dots represent observed data points.

    • The shape of the curve helps identify diminishing returns or optimal spending levels for marketing investments.

Graph showing exponential decay of pipeline number per $1000 spent on Google.

Use Cases

  • Budget Optimization: Understand how increasing or decreasing ad spend affects pipeline growth.

  • Marketing Channel Performance: Compare different marketing inputs (e.g., Paid Social vs. Organic Social) to determine the most efficient channels.

  • Forecasting & Planning: Use historical trends to predict the impact of future marketing investments.