Partnering with Product Development via Predictive Modeling

By Dr. Mildred Hastbacka

Product development is a risky business. Consider the following statistics:

  • Of every seven ideas, only one yields a successful product.
  • Of every seven product ideas, only four enter the product development stage.
  • New products have a failure rate of 25 percent to 45 percent.1

The odds for product developers don’t look good, do they? Given the less than encouraging statistics, why do companies continue to pour resources into it? Because new products and their commercialization are vital to business success, top and bottom lines. For example, 3M allocates 6 percent of sales to R&D, of which 85 percent goes to development.2

Given the large amounts at stake, even modest improvements in product development “show up” in the bottom line. It’s not surprising, then, that “reducing the risk of product development” generates 2.5 million hits in Google searches!

Where do these risks come from? Most new product development programs are technical successes. That is, once a development program reaches the point of product launch, the product works as intended. More often than not, the risks are non-technical and include the following:

  • Competing products from outside the firm,
  • competing products within the firm,
  • external drivers such as industry standards and government regulations,
  • existing technologies already in commercial use,
  • market inertia,
  • absence of enabling technologies, and
  • supply chain inadequacies.

For a product development program to increase its chances of success, the associated non-technical risks must be identified and assessed with respect to program impact, both qualitatively and quantitatively. Furthermore, a predictive model needs to be developed to address these risks, individually or collectively. Actuaries have the skills required for risk assessment and modeling. The role of the actuary in a product development team is to identify and quantify sources of risk and build models that accommodate input variations over time.

This article presents some examples of product development programs that failed due to lack of adequate risk assessment and faulty predictive models.

A $300 Million “Debacle” At Google: Getting Out Over Your Skis

Google Wallet was meant to be a central hub for linked debit and credit cards, serving businesses and consumers, and increasing Google’s targeted advertising. That didn’t happen due to a cascade of supply chain problems, all of them related to risks that should have been anticipated:

  • Credit card companies charged fees for digitizing credit cards. The fees were big enough to generate losses for Google on each transaction.
  • Telephone companies saw Wallet as a competitor in the mobile payments business and, therefore, blocked the Wallet service.
  • Telephone handset manufacturers and retail merchants delayed installation of necessary technology.

At $300+ million later, with many of its original “drawing board” features stripped out, Google Wallet is focused solely on mobile payments between individuals.3, 4

We Have Met the Enemy and He Is Us: The Risk Of Competing Products

In the mid 1990s, Philips Corporate Research5 was engaged in a project that involved a new technology: optical recording tape. The tape was considered a major opportunity because sales were projected in the billions. However, there were substantial uncertainties about the technology and its intended market (consumer products).

The “go ahead” decision was based on an option model according to which the value of the option to develop products was greater than the associated cost.

Fast forward to the end of the 1990s: Philips stopped its optical tape product development program. Competing recording technologies, namely the recordable CD and the recordable DVD, took the wind out of the sails of optical tape recording in both the PC and consumer electronics market segments.

What was lacking in the predictive model? The model didn’t factor in competing technologies and products. To add insult to injury, these competing technologies had been in simultaneous development within Philips itself!

Sometimes You Can’t Even Lead a Horse To Water: The Risk Of Market Inertia

Another development program within Philips in the 1990s involved analog and digital video tape recording. Philips had the choice of which standard to adopt for its 8mm tape products, either analog or digital. Each technology required approximately the same investment but the expected cash flows were different. The investment risk was linked to which standard would eventually prevail.

Again, the “go ahead” decision was based on an options model. It was decided to develop both standards simultaneously. And again, Philips reversed its decision.

What was missing in the model this time? A number of external market factors that ultimately led to Philips to rethink its original decision such as:

  • Incumbency and strength of existing commercial product technology 
  • The VHS market was well established and Philips was not a dominant player here.

  • Market inertia                       
  • Although Philips introduced a Digital Compact Cassette (DCC), its rate of market penetration was lower than expected. The product advantages did not excite customers. This experience increased the risk of introducing improved analog technology or the new digital technology.

“Predicting the future is easy … getting it right is the hard part.”
—Unknown

With respect to product development there is plenty of opportunity to improve the decision-making process. Technical expertise is necessary but not sufficient for commercial success. The relevant non-technical success factors must to be identified as well as their inherent risks. Once this is done, predictive modeling can improve launch success rates and, consequently, the top and bottom lines.

Mildred Hastbacka, Ph.D.,is founder and managing member of Prakteka LLC, a business-focused technology consulting company. Learn more at  http://www.prakteka.com or email Mildred at  mah@prakteka.com.

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November 2017