Several Newry clients have recently found success by adopting principles of “Lean Startup” – intensive customer engagement; agile development of minimally viable products to elicit feedback; rapid iteration and prototyping – to help align their innovation efforts with market needs (learn more). We are excited by the impact that Lean Startup has had at our clients; however, we have also observed that it can be challenging to subscribe to these principles in practice. Gathering customer feedback to understand market “pull” can be a time-consuming and cumbersome process, especially if it is a new product area where the target customers and technology needs are still undefined. Clients are often hesitant to engage until they have developed a working solution to their problem, which runs contrary to the intent of agile, iterative development.
In the B2B world, where customers are frequently more secretive, more demanding, and spread across a greater diversity of market segments, data science has the potential to play an even more valuable role.
One increasingly popular solution to the challenges associated with obtaining rapid and reliable customer feedback is the application of various data science techniques. Data science has already had an immense impact in the B2C world, where Netflix and Spotify have realized tremendous success using predictive algorithms that draw on patterns of user behavior to hone and personalize their services. Proctor & Gamble, for example, employs data modeling, simulation, and predictive analytics in their product development efforts, and recently made very effective use of these techniques in their design process for a new disposable diaper product. Designing a disposable diaper in the traditional way requires creating a prototype by hand, and then assembling a large customer focus group to gather feedback on the design. With modeling and simulation, P&G saved thousands of dollars, creating and prioritizing thousands of iterations in seconds.
In the B2B world, where customers are frequently more secretive, more demanding, and spread across a greater diversity of market segments, data science has the potential to play an even more valuable role. Materials- and technology-oriented companies have broad technology portfolios that can be applied to a multitude of products in a variety of industries, which means that they are not afforded a project focus as concrete as creating a new disposable diaper. Consequently, gathering market feedback via traditional methods is even more challenging, and alternative means of gathering customer insight are that much more critical (and may be much more effective).
For example, in a recent project focused on identifying new business development opportunities for a specialty lighting technology, Newry leveraged data science techniques such as data mining and natural language processing to uncover and select among hundreds of possible applications for our client’s product. Using these techniques, we were able to filter through millions of problems voiced by potential customers in industries ranging from military/defense to consumer electronics, identifying a key few specific opportunities that matched with the differentiated material solution provided by our client. Aircraft cabin lighting emerged as a top opportunity based on the shortcomings of incumbent technologies, and after interviewing a number of potential customers to confirm these needs, we were able to connect our client with Tier 1 suppliers who had specific, immediate requests for our client’s technology.
In this instance, the data science tools we had at our disposal enabled us to capture market feedback much more rapidly than traditional methods, which enriched the depth and breadth of our recommended strategy. In combination with other Lean Startup tactics such as prototyping and iteration, this approach has the potential to be even more impactful, and could be applied to any number of innovation-driven new business and product development efforts.