Overview

Resource Allocation is a tool used for optimizing resource size (e.g., advertising budgets) and the allocation of resources across segments, products, channels, etc.

Hard data rarely are available to support such decisions, because resource allocation decisions influence future (unknowable) outcomes. Consequently, in the first phase, resource allocation analysis builds on managerial experience and insights to create an effort/impact response curve consensus; that is, users answer the question, “Given our experience and knowledge about the market, products, customers, and competition, what would happen if we increased [a resource such as advertising] by x%?” A response curve may then be calibrated on the basis of these “what-if” assessments to determine how the market might react to changes in the resources allocated.

Then, in the second phase, these calibrated response curves can be used to derive an optimal solution to the resource allocation problem at hand by taking into account both stated objectives and constraints (e.g., budget limitations).

Resource allocation analysis helps firms answer such questions as: 

• How much should we spend in total during a given planning horizon?
• How should that spending get allocated to each marketing mix element? How much of our budget should be spent on advertising and other forms of impersonal marketing communications? On sales promotion? On the sales force?
• How should individual budgets be allocated? To customers? To geographies? To sub-elements of the marketing communications mix? Over time?

These types of questions are closely interrelated. It is nearly impossible to address the question of how much to spend (budget) without determining how to spend the budget properly (i.e., allocated across competing uses). Thus, these questions provide the perspective used to explore each question individually.

Getting Started

The resource allocation model allows you to use your own data directly or to use a preformatted template.

Because the resource allocation model requires a specific data format, users with their own data should review the preformatted template to become familiar with the appropriate structure.

Creating a template

The screen capture below shows the dialog box that results from using Enginius Templates (Resource allocation). 

The options are as follows:

• General options:
  • Number of categories: A category is the base unit of analysis in resource allocation; you might allocate resources across channels, customers, or products, such that each channel, customer, or product is defined by the generic term “categories” for the purposes of this analysis.
  • Number of effort levels:The calibration of the response curve requires the consensus estimates of how outcomes might be affected by changes in the input variables for each segment. For instance, how much additional sales would be generated by an increase of 50% in the sales force allocated to a specific customer segment or product? Obtaining accurate and consistent consensus estimates from a group of managers and experts is as much an art as it is a science. It requires free information exchange, constructive feedback, and a constructive discussion of the “what-if?” scenarios.
  • Include category-specific level constraints: If you check this option, Enginius will include a table in the template wherein you can specify the maximum and minimum effort levels that could be deployed for any category.  

Note: the check box at the bottom of the dialog box will cause the template to populate with sample (random) data that will allow you to run resource allocation immediately so you can preview the output produced. 

After selecting the desired model options, click Run to generate the data collection template. 

Data blocks

The template will consist of the following data blocks depending on the template options selected.

Base scenario

Data consists of current efforts and outcomes, cost, and margin for each category. 

Effort data

Data consists of the levels of efforts that will be used to calibrate the response functions. “Inf” can be used to represent an infinite level of effort.

Impact data

Data consists of the consensus estimates of how outcomes might be affected by changes in the effort variables for each category. For instance, how much additional sales would be generated by an increase of 50% in the sales force allocated to a specific customer segment or product category?

Obtaining accurate and consistent consensus estimates from a group of managers and experts is as much an art as it is a science. It requires free information exchange, constructive feedback, and a constructive discussion of the “what-if?” scenarios.[1].

Category specific constraints (not required)

Data consists of the minimum and maximum level of effort for each category. 

Running Analysis

Selecting the “Run Resource Allocation” option under the Quick Links or clicking the Resource Allocation icon on the left will bring up the following window where you can set up your allocation scenario:

Resource allocation requires:

• Base scenario:

Select the data block that contains the base scenario (current levels of effort and outcome).

• Response functions:

Select the data blocks that contain the effort data and impact data for setting up your response functions.

• Constraints: 
  • Set global minimum effort constraint: The minimum level of effort constraint. Often this will be set to zero to prevent the software from a solution with negative levels of effort.  
  • Set global maximum effort constraint: The maximum level of effort constraint across all categories.
  • Set category-specific effort constraints: Select the data block that contains your category specific constraints.
• Run sensitivity analysis:

Sensitivity analysis performs optimizations at various levels of global effort. It is time consuming to compute. You may expect significant waiting time. 

• Advanced

The advanced option enables an additional dropdown under the Response functions section in which you may choose the response function model to be used for calibrating the response curves. 

• AdBudg and Logit response functions are S-shaped functions that allow outcomes to increase very slowly initially as you increase effort from low levels, and then accelerate rapidly, before eventually reaching a plateau, or saturation level.
• An Exponential response function increases rapidly at the beginning, and each additional effort unit has a marginally lower impact than the previous effort unit.
• Automatic will select the most appropriate response function for each Category. The response function used for each category is indicated in the report produced.

Make the desired selections for the above data blocks and click the Run button. 

Interpreting the Results

Response function calibration

The report will display the response functions calculated for each category.

The results are also plotted on the next chart. 

Unconstrained optimization

The report will provide a table and chart of the optimization process using unconstrained data compared to the base data. (This chart will be produced even if you have placed constraints on your data.)

Constrained optimization

The result of the analysis with your constraints enforced is displayed.

Comparison of scenarios

A chart for each category will be produced comparing the current situation to both constrained and unconstrained options.

Sensitivity analysis

If you checked the Run sensitivity analysis option when running the model, Enginius will produce the following chart that summarizes the optimal net marginsat various levels of total effort (global effort).

We have highlighted the potential net margins that could be realized by reallocating current effort optimally (i.e., without a change in current global effort), and the potential net margins that could be realized by changing the total global effort optimally.