Experimentation Matters - Unlocking the potential of new technologies for innovation

Bank of America

Bank of America (BOA) is the second largest bank in the US operating around 4500 banking centers in 21 states and servicing approximately 27 million households with 2 million business customers. The bank has learned number of lessons from a series of experimentations in spite of being a service-based organization. BOA wanted to successfully combine its workforce and technology in order to give its customers complete satisfaction.

• There was problem on deciding the experimentation capacity. The team was not able to fix the number of times they have to rollover the experiment again as it depended upon few external factors.

• The team had used two technologies named repetition of trials and experimentation controls to minimize the effect of noise.

• The team has learnt more through radical experiments - the experiments, which allows team to explore new possibilities rather than running experiments that allowed improving the banking process.

Tapping the power of Experimentation

The information based experimentation leads to more experimentation. Today, when technology has made experiments more fast and accurate, organizations ends up doing more experimentation. Let us go through a set of principles which guides organizations to help them in successfully tapping the power of experimentation:

• It is always better to solve the problems at the early stage of the product development process. Most of the drug companies face extra costs on drug discovery due to this reason. The average cost of developing a new drug was about $231 million in 1987 that increased to $802 million in 2000. The major reason behind this additional cost was not solving the problems at an early stage of drug development. The use of new technologies at the early stage of product development assists organizations in providing more interaction, communication and problem solving among individual groups. The concept of putting the front loaded development process also helps organizations to find out bugs at the early stages. For example, putting the testing group at the early stage of Microsoft Project helped Microsoft to cut down costs as well as finish the development faster. It is always beneficial for the management to consider the downstream testing groups to upstream decision making and planning.

• Companies should not always look for new technology during experimentation. The best way to obtain a right combination of traditional and new technologies is to identify the place where the traditional and the new ones fit perfectly and then accordingly go for it. This step will also help companies to save money in terms of leasing or buying the new technology.

• As learning is one of the most important parts of the experimentation. Organizations should go for the rapid experimentation. It provides an effective learning through reinforcing the learning from past experiments and then quickly modifying the information for the next series of experiments. A rapid experiment makes the learning very fast and well structured in comparison to experiments at more intervals. The other main benefit of rapid experimentation is quick feedback, which works as a fuel for the developers to keep working on the new ideas. For example, the lack of simulation technologies in early stages at BMW made the experimentation process very slow. Due to the lack of technologies, it took months for the engineers to get their feedback on the developed physical prototype. Further, the data from crash testing used to arrive so late that it acted as a barrier to the innovation process because by the time it reached to the engineers, they all were de-motivated.
   

• How do organizations categorize their experimentation process? Is it a part of their product development approach or a part of their R&D labs or it is a part of their innovation process? It depends upon organization to organization but in most of the cases experimentations are the part of innovation project where thousands of small experiments attempt to resolve technical solution, product possibilities, customer needs and markets dynamics. But sometimes organizations move to the new strategies where they consider projects as an experiment to make innovation process as a part of project in a time bound environment. For example, BMW decided for the development of its 7-Seater platform car project as an experiment project and asked its engineer to develop it in specified period. BMW took a big risk since its engineers did not know which process they were going to adopt and the time limitations.

The above examples show that there are number of factors that are important to any experiment whether it is technical or non-technical. A right combination of technology and strategy can bring the best from the experimentation. Learning is another important factor that makes the experimentation step every time more accurate and allows the developers in dropping the high rate of failures. New practices at the experimentation level also help companies to bring more innovative solutions. Companies can always look beyond the traditional and existing ways of experiment and go for new ones as what has done by BMW.

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