67. S-CURVE LIFE CYCLE ANALYSIS

OBJECTIVE

Identify the maturity of a product or service and forecast the demand for the next periods.

DESCRIPTION

The assumption behind this model is that usually a product has a life cycle that follows an S-shaped curve with three main phases (see 12. PRODUCT LIFE CYCLE):

• -          Emergent phase: this is characterized by a low number of firms, low revenues, and usually zero or negative margins;
• -          Growth phase: the margins are increasing rapidly (for a while, but less in the last part of the growth phase), as well as the number of firms;
• -          Mature phase: the global revenues are increasing at a far lower rate; both the margins and the number of firms are decreasing. At this point the product can enter into a decline phase, for example if a newer substitute product is introduced or if the demand is decreasing.

As mentioned in chapter 12. PRODUCT LIFE CYCLE, it is important to identify the phase of the product or service (this analysis also applies to different product levels – brand, product line, product category, etc.). For this purpose we should identify the trend in the number of firms, revenues, and margins. However, we can complement this analysis with a statistical approach that will help us to forecast the future growth.

If our product is in the emergent phase or just in the development phase, we do not have enough data to estimate the S-curve, so we should use the data of products with similar characteristics and analyze their product life cycle curves. It is important to make good assumptions about the market saturation level and the differences between our product and the similar product that can affect the growth rates in the three phases. The more data we obtain after the launch of our product, the more precisely we can compare it with the S-curve of similar products and, thus, the more we can adjust our assumptions.

If we estimate that our product is already in the growth phase, besides comparing it with similar products, we can build our S-curve using for example a logarithmic linear estimate (see the template) and forecast future sales. As shown in the figure below, the sales data for each period (years[1]) are inserted into the tthe able then transformed to estimate a linear trend. In the last row of the table, the logarithmic transformation is reversed and the sales are projected (see the graph on the right in the graphs).

Sales Data and Forecast Using Log Transformation

If we think that the S-curve does not fit the forecast properly, we can change our assumptions about the market saturation level (which will change the log-transformed data and thus our forecast). In the template the R² is calculated and we can use it as a measure of how well the forecast curve fits the actual sales data. However, remember that our assumptions about market saturation levels are more important than reaching a “perfect” fit of the forecast curve. For example, we can reach a higher R² but with an improbably high market saturation level. In fact, there are different factors that can affect the shape of the curve: the economic situation, competitors’ strategies, the entry of new substitute products, new fashions, and so on.

Log Transformed Data and Linear Trend Line (on the Left); Real Sales Data and Forecasted Sales (on the Right)

Finally, in the case that our product is already in the mature phase, this model will just project a slightly increasing or stable level of sales. However, it cannot predict or estimate whether or when our product will pass into the decline phase. This can be realized only when the product has already started to decline. Anyway, when it becomes apparent that our product is in the mature phase, our strategic actions will focus on optimization and “life extension.” On one side we have to optimize what we can obtain with this product and its market by reaching its full potential (pricing optimization, loyalty programs, product bundling, advertising, promotions, etc.). However, sometimes this is not enough either to continue to increase sales or to avoid declining. In this case we should consider actions that are able to “push upward” the S-curve, extending the life cycle of the product: product evolution, innovation, new markets, new use of the product, and so on.

Finally, it is important to remember that this is not a precise forecasting method, since this is not its main goal. With this method we can project the medium- and long-term product growth potential, but, for a more accurate forecast, this method has to be complemented with more precise methods, especially for the short-term forecast.

TEMPLATE

Download the S-Curve Life Cycle Analysis Template

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[1] Usually the life cycle of a product takes years to reach maturity, but, if we have a shorter life cycle, we can use deseasonalized monthly data (for example using a moving average).