Step 1: Determine the Objective Metric
The first step of resource allocation is to determine the objective metric. The company needs to set a goalâsuch as maximizing profits or improving brand awarenessâand then choose a metric to measure its progress toward that goal. Metrics often used for this include: paid search click-through rates, conversion rates from website visits, net profits, customer lifetime value, near-term sales lift, new buyers, repeat sales, market share, or customer retention rates.
Step 2: Develop a Function that Connects Marketing Inputs to the Objective Metric
The second step is to connect the firmâs marketing inputs to its resource-allocation objective metric. Managerial intuition is of paramount importance in this stage, as it allows the marketer to correctly decompose a metric. If a company is examining gross profits, for example, what components of the business contribute to those profits? Unit sales is a function of price, advertising, salesforce, and trade promotions. Advertising, salesforce, and trade promotions also incur marketing costs. Gross profit is sales minus unit costs, and net profit is gross profits minus marketing costs. Thus manipulating marketing inputs can improve sales, but the different inputs are also cost centers.
Once the marketing inputs are mapped to the objective metric, the marketing manager must determine which relationships between components that contribute to the metric are computational and which are empirical.
Computational relationships, also called accounting identities, can be calculated directly using known quantities. An example of an accounting identity is net profit: if both gross profit and marketing costs are known, net profit is simply gross profit minus marketing costs.
Empirical relationships are more complex, and driven by unknowns. They are functions, or transformations, that reflect analysis of historical data. An example of an empirical relationship is the relationship between marketing costs and unit sales. Unit sales cannot be obtained by a direct sum of the investments in marketing (as mentioned, these include price, advertising, salesforce, and trade promotions). The relationship is termed empirical because it is based in observation: the manager must analyze historical data to develop a function that transforms the marketing inputs into unit sales. For example, the manager could analyze the historical data on price and unit sales and develop a function that describes their relationship. The transformation function ideally yields a weight that translates a productâs price into unit sales; in other words, the weight is the amount that unit sales would be expected to change with every unit change in price. These weights do not provide perfect transformations: they are best guesses based on historical data, wherein several factorsânot just priceâaffect unit sales.
The main difference between an empirical relationship and an identity relationship is that an empirical relationship implies a best guess or prediction, while an identity is certain.
Step 3: Estimate the Parameters of the Function Identified in Step 2
The third step in the resource-allocation process is to estimate the best values for the unknown weights of the empirical relationships identified in the second step. A common method for identifying these weights is to build an econometric, or regression, model. For example, consider a problem where unit sales is the objective metric and price is the marketing input. Letâs say in step 2, we specified the function connecting price and unit sales as: unit sales = a + b Ă price. We donât know a and b because unit sales and price have an empirical relationship. In step 3, we collect historical data and use marketing analytics to find the values of a and b in the context we are analyzing. This process of finding the values of a and b is called parameter estimation.
In this model, the independent variables are what we have been calling the marketing inputs: in the current example, this is price. The dependent variables are the potential metrics that a business can use to assess its resource-allocation efforts: in the example, this is unit sales. The aim of the model is to identify which marketing inputs of interest (price, advertising, sales calls, and so on) should be considered as having an effect on the dependent variable.
Once the unknown values in the empirical relationship between marketing inputs and objective metrics are estimated, the marketing manager can begin to predict the objective metric at different levels of the marketing input. This is the mathematical model that describes the relationship between the independent variables (price, advertising, sales calls, and so on) and the dependent variable (market share, profits, customer lifetime value, and s...