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21st Century Innovation Inputs and Validation

By Tamara Barber of the Affinnova Innovation Evolved blog

Ideation and insight-gathering techniques are evolving to meet the current realities of an increasingly connected customer. Specifically:

  • Qualitative insight gathering – Online communities, self-ethnography, and social media data now supplement and sometimes replace focus groups, in-depth interviews and in-person ethnography.
  • Quantitative insights gathering – Online surveys have become a top research expenditure at many companies, and mobile surveys via phones and tablets are gaining momentum. Traditional phone surveys are becoming more expensive, and at times less representative, as consumers cut out landline phones in favor of mobile.
  • Online ideation and brainstorming techniques – The in-person brainstorming session and focus groups are giving way to innovation jams, idea-voting sites like Ideastorm and MyStarbucks, and other online tools offered by research firms, technology platforms and innovation consultancies.

These are great new sources of inputs to fuel the bottom of the innovation rocket (our revamp of the old innovation funnel). But, with all of these new ways of eliciting ideas and gathering data, techniques for vetting this into viable, validated concepts at the top of the rocket have not evolved as quickly:

  • Qualitative techniques – Qualitative techniques are great for those jewels of insight that you would never get from a survey, and they add an invaluable personal dimension to otherwise broad markets. They are poor indicators, though, of the specific needs your customers have on a broad basis. Yet, companies may still rely on qualitative work out of habit or because other quantitative methods aren’t perceived to be any more effective. Are quantitative techniques any better for concept testing?
  • Quantitative techniques– Many techniques that are commonplace in the innovation process are inefficient at exploring and testing the full range of concepts. For example, sequential monadic testing – showing one concept at a time – does not account for the fact that people choose from an assortment of options in the market place. Discrete choice methods account for this but,  require testing in waves or batches to be cost effective. And even then, results are not easily comparable to other metrics.Lastly, conjoint analysis – which narrows-in on the appeal of specific features and messages – is limited in the number of features that can be tested at once and cannot test for interaction effects between different features.

So, what is an innovator to do? If companies rely on directional information or – worse yet – gut, they make decisions in an ivory tower, disconnected from the full picture of their consumers’ needs and desires. On the other hand, quantitative testing and retesting may feel like a closer collaboration with consumers, but companies still lose out because they can’t possibly be exposing the full range of concepts that a customer may potentially want.

Current methods leave a lot of risk on the table, and we find that many companies are stuck in analysis paralysis – a prolonged, expensive cycle of ideate-test-retest. The act of commercialization – knowing which ideas will perform best in market and then making them a reality through testing and validation – is the hard, gritty peak of the innovation process before going to market.

No one technique is a silver bullet for the entire process; researchers, marketers and product developers need to know when to use not only qual versus quant, but also the variety of methods within those two camps. What combinations of techniques do you use to distill ideas and concepts from the river of information into market-ready products?

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