Metrics are fundamental to the Delta model; they chart the course for implementing the desired strategic position and are at the heart of adaptation. Unfortunately, most businesses are limited in their ability to identify and track effective performance metrics, for two key reasons.
First, metrics have heavily depended on financial and accounting data, which explain how the business has performed but provide little insight on future performance. To anticipate the future, it is necessary to track performance against the adaptive processes, which are the initiatives enabling the strategy. Most importantly, the metrics need to clearly align with the strategic position.
Figure 8.8 shows distinctly different metrics for each strategic position, according to the adaptive process. Operational effectiveness goes well beyond the conventional role of ensuring a low-cost infrastructure for the delivery of products and services. In the case of customer solutions, it also allows inquiry into the best way to add value to the customer by quantifying the economics of the value chain and how alternative products affect it. Moreover, in a system lock-in strategy, it also examines the total potential of the product's system and how the system can contribute to product enhancement and profitability. Likewise, customer targeting goes beyond the stereotype of customer identification and prioritization to get to the roots of customer profitability and the ability to appropriate system profits. Finally, innovation is not simply a process of new product development but also a way to secure customer bonding and competitive lock-out.
Operational Effectiveness (Cost Drivers)
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Customer Targeting (Profit Drivers)
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Innovation (Renewal Drivers)
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Aggregation is the second reason that conventional metrics are inadequate. Most top executives have information based on broad aggregates and averages. However, our research shows that the inherent variability beneath the averages points to the root cause or fundamental drivers of cost, revenue, or profit. Managing by averages leads to below-average performance.
An example in the telecommunications industry illustrates the nature and value of granular metrics. The overall activities and cost chain for providing a local data circuit are shown in figure 8.9. Dissection of the cost into finer elements reveals wide variability. The highest-cost order was more than ten times the lowest-cost order. Also, these high costs were concentrated in a few orders; 20 percent of the orders generated 75 percent of the total costs in order fulfillment. It was not possible from the averages to know how well or poorly the company was fulfilling orders.
Figure 8.9: Cost Behavior in the Telecommunications Industry
Other dimensions of this cost variability, such as location, explain the cost behavior. Among this telephone company's five locations, the unit cost was more than twice as high at some sites than at others. These differences were driven by structural factors, such as the scale of the facilities or the density of the service area, and managerial factors, such as training, incentives, or practices.
At one location, we dissected the interconnected activities such as order entry, design, facilities configuration, switch testing, and so on. In 70 percent of the orders, each step proceeded flawlessly and resulted in on-time, low-cost delivery. In the other 30 percent, the order failed in one or more steps and required expensive, timeconsuming remedial attention. The high-cost order path was ten times the cost of the low-cost path. Some of the high costs were caused by the people involved and some by the particular facility. Some groups consistently operated at three to five times the cost or speed of others. By comparing the groups and their different work practices, training, experience, or incentives, we began to formulate specific, focused efforts to address the high pockets of cost.
This pattern of economic behavior is the rule, not the exception. In our research, the concentrations of cost became more pronounced and the solutions more focused. Granular segmentation allows a company to focus, to measure, to learn, and to innovate.
The same pattern was evident in profit performance. Figure 8.10 shows a huge variation in profit margin by individual credit-card customers. The customers were ranked from most to least profitable. The top 10 percent of the customers contributed 99 percent of the business profits, the next 10 percent accounted for 43 percent of additional profits, and the next 16 percent of the customers added 25 percent more. Only 36 percent of the customers contributed to profitability and collectively accounted for 167 percent of the business profits. Unfortunately, the remaining 64 percent of the customers produced losses equivalent to 67 percent of the total profits.
Figure 8.10: Credit Card Customers' Profit Margin Contribution. Source: Dean & Company analysis
While many companies tend to dwell on one measure of customer attractiveness, we found that no one factor adequately explains the variation in customer profitability. A high-usage customer can be unprofitable due to low outstanding balances. Given high acquisition costs, a long-time customer can make a lowbalance customer profitable. The combination of all these factors, which seem to grow with the complexity of the business, leads to greater profit concentrations.
Business has become a complex interaction of many employees, customers, suppliers, teams, procedures, and processes, with each unit operating according to straightforward rules. When combined into a system, however, certain accelerating or stagnating patterns emerge—in demand, revenue, or cost. Companies that can adaptively capture the unpredictable explosions in market growth, while arresting the eruptions in cost, will generate massive market value. A company needs to segment at granular levels, but retain a strategic perspective within a unified framework.