| Cost Shocks
Be it the cost of labor, oil, food, financing, or other inputs to the hospitality industry, it is clear that we have entered uncertain times. The increasing demand for oil and food have created cost shocks or dramatic increases in cost structures that stand to dramatically affect competition in Asian hospitality markets. But what are the implications for CPM? To examine such effects, consider a scenario where we assume costs shocks (equivalent to 33% of the initial unit cost in the model) that occur in periods 34 and 67 of the 100 period time line. The results are depicted in Figure 5.
Figure 5: Cost Shocks
The foremost observation is that cost shocks make it very difficult to compete with a customer portfolio that is dominated by acquaintances. Because the margins on such customers are small, cost shocks (like eroding margins due to competition) will result in a large population of profitable acquaintances quickly becoming a large population of unprofitable acquaintances. The results further illustrate how intermediate level relationships, the friends in this case, offer a balance of risk and return. Building a portfolio that focuses on friendships will optimize the trade-off between the time and cost required to build closer relationships, and the risk involved with low-margin customers.
A High Touch Growth Market with Eroding Margins and Rapid Diffusion
For illustrative purposes, one last scenario illustrates the complexity of portfolio management in an Asian hospitality context. The results in Figure 6 combine most of the factors previously considered into a single scenario. This includes a growth market, a high touch service (with lower economies of scale), eroding margins over time (due to competition), and a rapid, technology-enabled diffusion process. The only factor not included is the cost shocks just considered, as their implication is clear. The results in Figure 6 focus on periods 1-50 of the simulation.
Figure 6: High Touch Growth Market with Eroding Margins and Rapid Diffusion
What these results reveal is the complexity and difficulty involved in timing relationship investments. This scenario is the closest yet to approximating the complexity of the real world. Consider specifically the importance of friendships to the overall portfolio. As noted earlier, profitable friends and partners evolve from acquaintances. Thus, it is critically important to build a population of weaker relationships. However, acquaintances themselves are never directly profitable. They are only indirectly profitable in that the sheer number of customers in this relationship segment contributes to the overall economies of scale for the entire portfolio, and is the basis for building friends and partners. Moreover, given the periodic drop and recovery in the contributions from acquaintances, it is very difficult to predict exactly when to shift relationship investments away from building acquaintances over to building friends and partners. In this case, the shift should occur around period 32. By illustrating the complexity of portfolio management, this scenario underscores the need for companies to invest in the metrics and expertise required to explicitly model how portfolios are likely to perform over time.
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