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The ModelThe GE/McKinsey approach provides an effective tool for evaluating segment attractiveness and selecting target segments. It does not address the resource allocation question.For planning purposes, how much should we spend in total across all these segments and how much of that total spending should we allocate to each? ReAllocator provides accurate answers to these questions using a Response Model-Analysis Based Approach: The figure below gives the idea behind a response function; that figure shows that we invest marketing resources to get some sort of return in a market segment. To be specific, assume our goal is to maximize short term profitability and that we are able to develop a response function for each of the market segments, relating sales in that segment to the level of investment we make in that segment. While there are several ways to generate the data for such a relationship (including analysis of historical data and market experimentation) we will focus on using managerial judgment to develop the response function. 
Each segment response function will require four judgments. We start with a reference value for sales (R). That reference value is our best guess about what sales would be in a specific time frame under a reference segment investment plan. Relative to that value, we then ask, how would segment sales be affected if, instead of that reference investment, our investment in that segment were 1) No segment investment = Y1 2) 50% of the base investment = Y2 3) 150% of the base investment = Y3 4) Unlimited investment = Y4 The figure below (a response curve plotted through the four managerial inputs, market response (Y1-Y4) ) shows the resulting curve for one segment, relating the marketing investment (input) on the X-axis to the segment response on the Y-axis. 
If we assume that unit profit margins in each of these segments are fairly constant over the range of segment investments we are interested in, we can develop a computer program to address the following problem: How much should we invest in total and how should that investment be allocated across segments in order to ReAllocator has been developed to perform this reallocation function. The model combines management science techniques with historical data and managerial judgment to calculate the incremental gains and losses in net contribution for unit increases in marketing effort. Getting startedBefore you start running our software, please take a look at the Compatibility List.Due to our intensive use of various Web technologies you may experience difficulties running a software under your browser. We apologize for any inconvenience this may cause and are working on full browser compatibility. You may have to enable Javascript, Cookies and/or accept Active X Controls. Please refer to the Compatibility List Read this help section and the Tutorial carefully before running the software. You can also see how the software actually works by running the Demo. Running the softwareIn Step 1 of the Resource Allocator you specify the study title, number of segments, Marketing Mix Element name, unit cost of the Marketing Mix Element and Response curve sensitivity. The Study title will be used as the default file name for saving a case. Response Curve Sensitivity will be used in step 2 to determine the response curve, it ranges from 1% to 99% (do NOT include the % sign in your input). Example input for step 1:
Step 2 specifies segment name, base sales effort, base sales level (in dollars), unit margin, and response curve estimates for each segment. You are free to use any segment name you prefer, however, long segment names may generate display problems at the report stage. Base Sales Level should be in dollars. Do NOT use any commas in the base sales level input. Keep in mind that Response Curve Estimates must be increasing. Example input for step 2:
Step 3 handles interaction between segments. If there is no interaction between segments, the user does not have to input anything. If there are important segment interactions, fill in the appropriate cells on the following table. Work row by row asking yourself: "An incremental dollar in segment 1 has what effect on sales in segment 2? (3?, 4?, ... etc) relative to its effect in segment 1?" The entry in each cell must be between +1.0 (maximum positive synergy) and -1.0 (maximum negative synergy). A value of 0.0 means no synergy (which is the default value). Most entries should range between +0.2 and -0.2. Once you have gone through step 1, 2 and 3, the resource allocator will give an optimized allocation strategy without applying any constraints. At this point, you may choose to apply different constraints. Each segment has a low constraint and high constraint. There is also an overall low constraint and high constraint. If you do not want to specify a constraint for an input, you can simply leave it blank. Example input for Constraints:
The Resource Allocator keeps two sets of response curve estimates: Base Response Curve Estimate and Current Response Curve Estimates. At the beginning, Current Response Estimates are set equal to Base Response Curve Estimates. You may modify Current Response Curve Estimates choosing "Change Estimates" under the "Options" menu of the resource alloctor. Instructions for printing: 1. Choose "Create Report" under the "Options" menu of the resource allocator, which will produce a PDF file. 2. Choose "Print" from the file menu of your browser. 3. Click OK to print. Alternatively you can download the PDF report on your computer, simply click the "Save" button. The report is designed to fit on A4 or letter size papers. To view a file in PDF format, you need Adobe Acrobat Reader, a free application distributed by Adobe Systems. If you do not have it installed on your computer, you may download it here. You can save a case on your computer by choosing "Save" under the "File" menu of the Resource Allocator, and you can upload it later by clicking the "upload" link at step 1 or choosing "open" under the "File" menu of the Resource Allocator. Understanding the resultsWe will run four different scenarios in order to illustrate the capabilities of the software:
 The recommended sales force = 747 and generates a net profit of $280,254 which represents an increase of 26% of the net profit (compare to the base case $222,248K). 
The sales force is constrained to the base level (430) and the net profit = $266,452K which represents an increase of 20% of the net profit. Therefore from case (1) and (2) we can calculate what is the gain from reallocation and from sizing. The total gain in net profit is 26% and the gain from reallocation is 20%, thus the gain from sizing is 6% (26%-20%). Assume that the drug Synalar has positive synergies with the sales of drug Nasalide and that an additional dollar of marketing resource spent on Nasalide has an additional, spillover effect of 0.5 dollar of marketing resource on Synalar. Therefore we input a coefficient of 0.5 in the segment interaction matrix. 
If we run the unconstrained problem, we notice that the overall sales force has decreased by 6 reps, and the profit has slightly increased. The important point is that the recommended sales force for Synalar has decreased of 6 reps and its recommended sales level is nearly the same. That means that Synalar has the same sales level with fewer reps because Nasalide has a positive effect (positive synergy) on Synalar: sales in Nasalide generate sales for Synalar. Assume that the drug Synalar is a substitute drug for Nasalide and that an additional dollar of marketing resource in Nasalide has an effect of -0.2 dollar of marketing resource on Synalar relative to its effect. Therefore we input a coefficient of -0.2 in the segment interaction matrix.  If we run the same unconstrained problem, we notice that the recommended sales force for Synalar has increased of nearly 3 reps and its recommended sales level is nearly the same. Synalar has the same sales level with more reps because Nasalide has a negative effect (negative synergy) on Synalar: sales in Nasalide take sales away from Synalar. |
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