Product Life Cycle Curves in the 21st Century

Published by RoadMap Technologies on May 14, 2012May 14, 2012

Product Life Cycle Curves in the 21st Century

One phenomenon that is common to many recent hi-tech product launches is the compression of the innovation part of the product life cycle into the initial product launch phase. This is due to the fact that many consumers in the innovator segment are very savvy purchasers. They often preplan their purchase, and thus preorder. This type of phenomenon takes out essentially the first leg out of the product life cycle curve and combines it with the launch month. initial buyers in the modern product life cycle are now compressed into the product launch month, which becomes the peak sales month. Therefore, in the classic product life cycle, sales may take as much as 8 months to hit the
peak. Launch weeks and launch months are now increasingly important in marketing products with very short life cycles. Given the rise and pervasiveness of social media technologies such as Facebook, Twitter, and YouTube, products with a very short life cycle can “go viral” across multiple social networks or fizzle out very quickly. Social networks are really a force multiplier for the “word of mouth” effect. They can make or break new
products very quickly. Consequently, now there are much higher marketing expenditures
in the pre-launch marketing phase in order to build the initial pulse of demand. Marketers cannot wait for the product to be released to build momentum. They want to build sales momentum prior to shipment. This makes the classic product life cycle model irrelevant in many industries. However, sales forecasting still needs to be done as accurately as possible, given the dynamics of the market. Therefore, the best way to forecast
products with very short product life cycles is to follow these steps:

Step 1: Gather the historical sales data by product

To forecast products with very short life cycles, you first have to gather data on all of current and predecessor products. Historical sales data are not enough. You also have to collect data of the past three years on the product attributes of products that are currently offered, as well their predecessors. The exact list of relevant attributes varies by market, but these are the ones that matter most:

• Performance characteristics
• Multifunctional capabilities of
• Price
• Order of market entry
• Distribution partners
• Media spending

Step 2: Develop Product Roadmaps.

A product roadmap is a bar chart that describes how new technologies will be introduced into various aspects of the product over time. For example, in a mobile phone product roadmap, one bar would show exactly when the company’s mobile phones would support the 4th generation communications network (4g) instead of the existing 3rd generation network (3g). If your marketing team has developed product roadmaps in the past, they are valuable tools to help you identify the truest predecessors to your upcoming line of new products. The product roadmap of Intel Corporation’s Notebook processor line is available at http://download.intel.com/products/roadmap/roadmap.pdf. What matters most for forecasting is how consumers look at the market, not how the company looks at the market. For example, if you want to forecast sales for a mobile phone manufacturer, you have to know how large the market segment is for price driven phones (e.g., feature phones that consumers get for less than $99 with a two-year commitment) vs. web enabled Smartphones that currently cost between $99 and $199 with a two year commitment. Smartphones also require a separate subscription to a data plan to access the Internet, which increases the total cost of ownership (TCO) of the Smartphone. In addition, you have to realize that the words “small,” “large,” and “fast” are constantly being redefined in many consumer electronics industries.

Step 3: Forecast Sales Over the Entire Product Life Cycle.

Once you have a good idea of the size of the market opportunity for the next generation of your product based on the previous generations of that product, you need to come
up with a statistical forecast of sales over the entire term of the product life cycle.
At this point, it’s useful to compare the statistical forecast to the sales force’s initial order forecast. The initial order forecast is the sales force forecast for the initial month of sales.
If the initial orders forecast for such a model of phone is about 25% of the total sales . One general rule of thumb in weighing the statistical forecast against the sales force estimate is that the forecast version that is lower would most likely be more accurate. Once that sanity check is completed on the total forecast for the product life cycle, the forecast is ready to be split into weeks for sales planning. Leading companies in consumer
electronics have built comprehensive E-commerce and demand planning systems that integrate product life cycle management data and collaborative planning and competitive intelligence with statistical forecasting capabilities to create the most accurate forecasts
and, consequently, the most responsive supply chains. Samsung Telecommunications
America, one of the leading mobile phone manufacturers, uses a system known as the Global Samsung Business Network (GSBN) which delivers comprehensive new product information, statistical sales forecasting, and inventory optimization capabilities to their retailers and channel partners.