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Response Curve Model

Catalog sales, whether by traditional direct mail or the increasingly popular email variety, are an integral channel for many companies.  Accurately modeling customer response to campaigns allows managers in product development, finance, supply chain, purchasing, capacity planning, and other departments to plan and operate more efficiently.   Catalog campaigns are inherently multi-dimensional; with many Quantrix customers in retail and manufacturing having multiple mailings that start on different dates, target different market segments, and experience different response rates. This Modeler Note and sample model demonstrates why Quantrix Modeler is ideally suited for layering in multiple mailings and campaigns into one integrated Response Curve Model. 

This Response Curve sample model allows the user to:
  • Enter starting date, quantity mailed, forecasted total response, and average sales order for each catalog campaign.
  • Enter a projected daily response rate for each mailing type (direct mail or email).
  • Enter actual sales for each day in the marketing campaign.
  • View projected vs. actual sales numbers and chart the results for visual analysis.
  • Effortlessly scale the model by adding new mailing campaigns to the Mailings category.
  • Securely share the model with colleagues in other departments with User Roles and Permissions
  • Provide a high level interactive dashboard for management and executives
  • More effectively and profitably manage your company’s catalog sales efforts
Click to download the Response Curve Model.
Illustration 1:
In the sample model, all of the pertinent information for each mailing is entered in the Mailing Program Assumptions matrix.  This model projects customer response for four catalog mailings. To add an additional mailing to the model, simply add a new item to the Mailings category.

Illustration 2:
Daily response rates for the duration of the campaign (in this case 40 days) are located in the Response Curve Assumptions.  The information is based on historical data for each type of mailings.  This model has two mailing options - direct mail and email.

Illustration 3:
After adding a new mailing campaign in the Mailing Program Assumptions matrix, simply select the mailing type and the appropriate response rates will be applied.

Ilustration 4:
The response rates are then used in the Sales Forecast matrix to calculate and forecast orders and sales for each day following the mailing.  The Sales Forecast matrix tracks actual sales and compares to the forecast on a daily and a cumulative basis.

Illustration 5:
This information is summarized in the Monthly Summary matrix for quick reference.

Illustration 6:
You can track the costs of mailing campaigns and compare to revenue and profitability for determining the campaign return on investment.  In the Mailing Cost Assumptions matrix, fixed and variable costs associated with each mailing type are entered.  This data is then applied to each campaign in the Mailing Costs matrix.

Illustration 7:
This sample model provides a chart that was created to visually present the forecasted and actual sales in the Sales Forecast matrix.

To recreate the chart:

  1. Select the Sales Forecast matrix and press the Insert Chart button.
  2. Move the Day category to the row-tray and move the Item category to the column-tray.
  3. Collapse all Column items except for the Cumulative Forecast Sales and Cumulative Actual Sales.
  4. Filter the Cumulative Actual Sales to remove any days that contain no sales.

Illustration 8:
The Sales Forecast Canvas in this example acts as both a reporting mechanism and an interactive dashboard.  All of the highlights of the model are displayed in one place and can be simultaneously filtered according to the mailing campaign.  The Response Rate slider can be used to adjust the Forecast Total Response to create a visual representation of different scenarios or to make an adjustment to forecasts based on actual sales throughout a campaign.
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