I’m running a little behind this week as I catch up on business demands, but we’re getting into the home stretch on this Book Discussion about Victor Cook’s Competing for Customers and Capital. Best of all, we’re reaching the point in the book where there’s a significant payoff for the hard work of wrapping your brain around new concepts that involve numbers.
I’m going to launch a discussion now of Chapter 6 (you can find a supplemental narrated PowerPoint here), which is a fantastic case study for applying the Rule of Maximum Earnings to an analysis of one of the world’s most infamous corporate corrections–IBM’s dramatic loss of market share during the 90s, and the rise of Dell. There are many ways to retrospectively read that market’s evolution and IBM’s fall, but in the context of a theoretical Maximum Earnings threshold that IBM substantially overreached, it has substantial value in highlighting the strategic importance of Enterprise Marketing. It’s interesting to me that at the time, Gerstner and his financial team figured out that they were dramatically overpaying for market share and a made many controversial moves to correct it, but the conventional wisdom still suggests that the real problem was manifest during the fall from the pinnacle of market share, rather than any problem, like over-investment, during the climb to the top.
One thing that seems to be a challenge in the application of the Enterprise Marketing framework relates back to chapter 3, and the definition of a strategic competitive group. IBM’s competitive group changed substantially during the 1990s, from a significant focus on systems and components to a focus on software and professional services. Microsoft and HP are two other 800-pound gorillas with such a diversified portfolio of products, it’s hard to shoe-horn them into any single competitive group, where the Enterprise Marketing expenses can be assumed to be evenly distributed over their various product categories.
Given the value of the Enterprise Marketing framework, I wonder if there are any potential strategies to unwind some of the financial data in ways that could shed more light on the various Enterprise Marketing expenses within a single companies product portfolios? I know it’s not a perfect solution by any means, but could enterprise-wide SG&A expenditures be allocated proportionately to various product groups based on group revenues, so that some comparison to other pure-play businesses in the strategic group could be made? I know each product group would probably have different levels of product marketing efficiency, but how different would you expect the corporate marketing efficiency to be across product groups?
UNWINDING FINANCIAL DATA
Recall from Chapter 4 on Enterprise Marketing (SG&A) Expenses that the three published components are HR, R&D, and A&P. This is a critical take on what generates intangible market value.
First, you carve out the three parts of a revenue dollar: COGS+SG&A+EBITDA equal revenues. Then you unwind SG&A into human resources, research and development, and earnings. These simple takes on your income statement give you a new perspective on the relative cost of the inputs that create intangible value. At this point you can ask: what should we be spending on each component to maximize earnings?
ALLOCATING SG&A EXPENSES
Yes, since SG&A expenses are so highly correlated with revenues, they can be allocated to groups so that comparisons with pure-play businesses can be made. This is a good idea.
MARKETING EFFICIENCY ACROSS GROUPS
In the seventy or so strategic groups I’ve analyzed, marketing efficiency ranges from very efficient (around 0.20) to very inefficient (1.9). Remember, an efficiency ratio of 0.20 means the company pays only $0.20 for resources that cost the average competitor $1.00, while the inefficient company paid $1.90 for those resources.
And there are significant changes over time. For example, look at the trends for Dell and IBM in the presentation on “The Battle for Your Desktop.” Dell’s enterprise marketing efficiency decreased from 0.39 in 1993 to 1.00 in 1991. While IBM’s ratio improved from 1.61 in 1996 to 1.37 in 2000. These are huge changes that have an enormous impact on earnings.