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Innovation and Intelligence

Posted on February 25th, 2016 by in Chemical R&D


Brilliant innovation of products that miss the market commercially is a common mode of failure of new product launches in the chemical industry.  New materials or chemicals that appear novel in the laboratory can fail once put in the hands of paying customers and subjected to the harsh reality of market and competitive dynamics.

One key cause is incomplete commercial intelligence of the market and competitive landscape during product development and prior to product launch.  Often this lack of intelligence misses rather simple things including:

  • Poorly understood economics of incumbent products.
  • Underestimation of the impact of industry specifications and codes on commercial leadtime and customer uptake.
  • Ignoring non-product attributes of the offering such as the importance of distribution, bundling of co-products and technical services.

As I have pointed out in earlier postings, innovation and product development projects fall along a spectrum of incremental or “evolutionary” research at one end and step-change or “revolutionary” research at the other.  The intelligence needed to support research these projects also varies across this spectrum and should accommodate these extremes.  For instance, one-off customer-specific projects likely require developing a deep narrow range of intelligence around that one customer and/or application such as physical property and economic targets.  At the other extreme, major step-change revolutionary projects may require a much broader understanding of the entire market including how industry trends may play out over an extended period of time, in-depth competitive analysis, deep value chain analysis and more.  The development of intelligence starts with good data gathering followed by equally good analysis to convert the raw data in to intelligence that can be acted upon.  This intelligence, in turn, should guide the innovation and product development and be used to make the final “go” decision to launch

Gather Data

The market research to gather data should be approached with the same discipline as product research. Proper use of a product development project management methodology such as Stage-Gate™ can help assure the right intelligence is gathered and considered at the right time in the project.  Stage-Gate™ lays out clear milestones of commercial intelligence at each “gate” of the project and provides an excellent framework for the needs of the chemical industry.

Key things to consider in data gathering include:

  • Plan research with the end in mind – make sure the research you conduct is consistent with the intelligence needs – avoid wasting time learning about subjects that are unrelated and tangential to the product launch.
  • Use a good blend of primary sources (first hand interviews of people) along with secondary sources (published works and data in the public domain). Synthesize the data in to a complete picture of the market in which the new product will compete.
  • Prevent bias in the research. For instance, don’t rely solely on gathering input from existing customers who may sometimes lead you to conclusions that benefit them and potentially undermining to your product.  Customer input is certainly important, but consider it along with diverse input from 3rd parties (e.g. those outside your current company and customer base) such as trade groups and consultants.
  • Conduct both quantitative and qualitative research. Understand not only the “what” but also the “why” and “how”.  Weave together “hard” data (e.g. physical properties, costs/price) with more “soft” data (e.g. trends, buying behaviors) to get a complete view of the environment in which the new product will compete.

Turn the Data into Intelligence through Analysis

Data gathering is important but it is of no use unless it is properly analyzed and turned in to intelligence that can be used to guide the path to commercialization.

A simple example is the need to develop pricing strategy by developing an understanding of the incumbent economics.  Let’s assume a chemical company is developing a new paint that lasts longer but requires two extra coats(layers) compared to incumbent products.  Data can be easily gathered on the price of the incumbent paint and its estimated life.  But deeper analysis is needed to develop “lifetime costs” that more accurately reflect the value of the extended life.  Other costs such as labor to apply the extra layer may be important to understand as well.  This example requires analysis (mostly math) to normalize the cost in to a “$/year of ownership”.  From this analysis a theoretical selling price can be back-calculated for the new product.

When doing analysis consider several things:

  • “Pressure test” the value proposition of your innovation. Deeply understand what factors will motivate a customer to switch from the incumbent technology and whether there is enough “uniqueness” of these factors (features/benefits) for the customer (or their customers) to switch.
  • Analyze non-transactional buying behaviors such as industry specifications and codes, regulations, safety/health/environmental, etc.
  • Always do economic analysis on a basis that is relevant to and understandable by the customer
  • Don’t stop at side-by-side comparisons with incumbents. For materials of construction, such as plastics, this might include analyzing how a tensile strength advantage might allow thrifting of part weight resulting in a lower part cost to the final customer.

Market research has entered a new era with incredible tools for gathering data.  These tools have lowered the cost and increased the quality of data and there should be no excuse for not having the right commercial intelligence to support product development.

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