The first framework we’ll look at as part of our Four Frameworks of Portfolio Management series is historical performance metrics as an indicator of future performance.
Evaluating Historical Revenue Growth and Profitability
When evaluating a portfolio of investments, it helps to review historical performance and leading indicators. In this section, we will discuss how to illustrate an effective historical view of a company’s portfolio.
When assessing past performance, it is important to evaluate both revenue and profitability. Revenue growth should take a multiyear view so that measuring compound annual growth (CAGR) will indicate whether revenue is growing or contracting. The contribution margin should be a percentage of revenue for the current year. An effective representation of this data is to plot the investments using revenue growth on the x-axis and contribution margin (profitability) on the y-axis.
Next, break the graph into quadrants by adding lines (represented as dashed lines in the figure above).
The location of the lines depends on corporate targets for each investment. In the hypothetical model above, the company is targeting a 30% contribution margin.
A good way to understand your target contribution margin is to take the corporate operating margin percentage and add in sales and general and administrative (G&A) costs. For example, if the company’s operating margin is 14%, and the company spends 16% of revenue on sales and G&A costs, the target contribution margin will be the sum of the two, or 30%.
By adding a horizontal dashed line at this point on the graph, it will become clear which investments are helping achieve the goal (above the horizontal dashed line) and which investments are detracting from the goal (below the horizontal dashed line).
The target revenue CAGR should also align with corporate goals. In the example, the corporation’s objective is to grow revenue 10% annually. When you add a dashed line at this point on the graph, it will be clear which investments are helping achieve the goal (to the right of the vertical dashed line) and which investments are detracting from the goal (to the left of the vertical dashed line).
Finally, to convey the relative contribution to corporate revenue, the products plotted on the graph should be sized proportionally to the revenue contribution. For example, a product generating $100 million in revenue will be twice as big as a product generating $50 million in revenue.
To illustrate how the historical model is used, we will create a sample portfolio of product offerings for a company that generates $500 million in annual revenue. Each product in the portfolio is at a different point in the product life cycle and each possesses a different financial profile.
Product A is the product upon which the business was built. For years, this software-as-a-service (SaaS) product has been the leading contributor to company revenue and profits. In the past year, the product’s revenue continues to be strong at $250 million (50% of the total revenue). The contribution margin is also strong at 45%. However, revenue is in decline. In the past three years, revenue has grown at only 2% compounded annually. The product is important in sustaining the business, but it is clear the company must find other sources of revenue and profits.
Product B is a mature and shifting business. While the product still contributes meaningfully to revenue at $125 million (25% of total revenue), profitability has declined as the business has shifted from an on-premise software license and maintenance business to a predominantly service business with high customization. This shift has caused the contribution margin to drop to 25%, below company targets.
Product C is a smaller, but growing, SaaS business. The product revenue is growing at 15% compounded annually and now makes up $105 million (20% of the company revenue). The product also enjoys healthy contribution margins of 40%.
Product D is a new product offering. The company has invested heavily in this new SaaS business. While the total revenue is low at $25 million (5% of company revenue), it has been growing quickly at 25% compounded annually. Due to the up-front investments in development, the contribution margin is below target levels. The expectation for this product is that revenue will continue to grow at the same rate and profitability will improve.
By plotting the products on our matrix, it is easy to determine the profitability and growth of each product.
In the example above, it is clear that Products A and C are contributing to overall profitability. Despite slow growth, Product A still contributes meaningful revenue and profitability, so attempts should be made to preserve the business. Product C is showing strong profitability and growth, so further investment is warranted.
Conversely, Products B and D are not contributing to the bottom line. Product D has strong growth but very low profitability. Discussions should focus on how and when Product D can reach profitability. Product B represents meaningful revenue, but since it is below the target operating margin and target revenue growth, discussions should focus on improving profitability or freeing up capital for more profitable or higher-growth products.
Historical performance analysis allows decision makers to quickly understand current and future product-revenue contributions. In the example above
- Product A is the cash cow and is important in sustaining the business, but it is clear that the company must find other sources of revenue and profits as growth has slowed.
- Product B is a mature and shifting business. While the product still contributes meaningfully to revenue, profitability is declining below company targets.
- Product C is a smaller, but growing, business with good profitability, so it is a candidate for increased investment.
- Product D is a new product offering. The expectation for this product is that revenue will continue to grow while profitability improves.
Next, we’ll look at the second portfolio management method proposed in the 2020 white paper, Four Frameworks for Portfolio Management, applying complexity science to decision making.