- Introduction
- The Resource-Allocation Framework
- An Illustration of the Resource-Allocation Framework
- Measuring ROI: Did the Resource Allocation Work?
- Working with Econometrics: IBM and Others
- Conclusion
This chapter is from the book
This chapter is from the book
This chapter is from the book
An Illustration of the Resource-Allocation Framework
Consider a pharmaceutical company in which the marketing department wants to determine the effects of sales calls on the profits it makes per customer (in this example, physicians are customers). In Figure 1-2, profits are broken down into number of new prescriptions and probability of new prescriptions. Both can be represented using a linear or logistic regression as a function of sales calls.
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Figure 1-2 An example of the system of metrics in the pharmaceutical industry
Source: Created by case writer.
Because sales calls also represent a marketing cost, the goal is to balance their effect on the top and bottom lines to maximize profits. The marketing manager can express the relationship between sales calls and profits mathematically and perform both linear and logistic regressions2 as follows (Equation 1):
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Performing the regression analyses will determine the value of a, b1, c, and d1, giving the marketing manager a mathematical way to value sales calls with respect to their ability to increase the number of prescriptions written by physicians and the probability of a new prescription. And because sales calls are a cost center, the pharmaceutical company can maximize total profits by weighting its number of sales calls subject to optimal spending under its budget limit (see Figure 1-3).
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Table 1-1 provides hypothetical data describing the effects of sales calls on profits per physician. Say the values for a, b1, c, and d1 turn out to be 0.05, 1.5, 0.006, and 1.2 based on the regression analysis.
Table 1-1 Numeric Example of Optimal Allocation of Marketing Spend
|
a |
b 1 |
c |
d 1 |
Price |
Cost of Sales Calls |
|
0.05 |
1.5 |
0.006 |
1.2 |
300 |
50 |
|
Sales Calls |
Sales |
u |
p (Sales) |
Profit |
|
|
1 |
1.09 |
0.84 |
0.70 |
109.73 |
|
|
2 |
1.70 |
1.32 |
0.79 |
181.65 |
Current |
|
3 |
2.13 |
1.67 |
0.84 |
226.31 |
|
|
4 |
2.46 |
1.94 |
0.87 |
252.30 |
|
|
5 |
2.74 |
2.16 |
0.90 |
265.25 |
|
|
6 |
2.97 |
2.34 |
0.91 |
268.74 |
Optimal |
|
7 |
3.17 |
2.50 |
0.92 |
265.10 |
|
|
8 |
3.35 |
2.64 |
0.93 |
255.94 |
|
|
9 |
3.50 |
2.77 |
0.94 |
242.39 |
|
|
10 |
3.65 |
2.88 |
0.95 |
225.27 |
Source: Created by case writer.
The price of a unit (a prescription drug) is $300, and the cost of a single sales call is $50. The drug company currently calls its physicians an average of twice per month (which means that, in this example, the number of sales calls is two). Based on the estimated weights for each unknown in the described relationships, this strategy yields a profit of $181.65. If the company were to increase sales calls to six per month, the expected profits would be $268.74. Increasing sales calls beyond six per month, however, makes the cost of the sales calls higher than their incremental benefits, meaning profits start declining for sales calls of seven per month and above. In this example, six is the optimal level of sales calls because it maximizes the expected profit ($268.74) from each physician. As the example illustrates, the optimal number of sales calls that maximizes profits is critically dependent on the unknown weights of the empirical relationship.
Figure 1-4 shows a decomposition commonly used by consumer-goods companies to forecast the performance of new products. Using this model, a company can study how advertising leads to awareness and how the sales force leads to availability, among other things. Once the company understands the empirical relationships mathematically, it can calculate expected sales using simple arithmetic.
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Figure 1-4 System of metrics to forecast new product sales
Source: Created by case writer adapted from Farris, Pfeifer, Bendle, and Reibstein.
Marketing analytics relies on three pillars: econometrics, experimentation, and decision calculus (Figure 1-5).
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Managers can use econometrics when they need to make hypotheses about their business and test them by using experiments. Where the decision calculus comes down to individual companies introducing their own intuition into the equation, marketing analytics as a whole allows firms to identify best estimates for how to weight the effects of marketing activities. Intuitively, these weights should provide the best relationship between marketing inputs and consumer response. Looking at past cases wherein a firm has tried different levels of marketing inputs and observed consumer response reveals this relationship.
In the case of Dunia, if a customer purchased a service, such as a loan or credit card, the bank would track the channel through which he or she was reached, as well as behaviors such as delinquencies, and incorporate those results into its cross-selling criteria. The results would then be used to develop new models to indicate how it should introduce future offers. According to Ali Hurbas, head of Dunia’s Strategic Analytics Unit, “It is not just about quantitative techniques but also business sense.”3