At the recent Front End of Innovation conference, Wells Fargo’s Michael Duke presented in a session devoted to innovation metrics. He opened with a slide that asked attendees:
Which would you rather have?
- 1,000 ideas; or
- 20 working prototypes
With a setup like that, what do you think the general response was? Of course getting to 20 working prototypes was the overwhelming answer. But it’s a false dichotomy, as the right answer is that from 1,000 ideas you can find 20 that are worth turning into prototypes.
But it does point out an issue with traditional ways of simply collecting ideas (i.e. the suggestion box):
It’s not idea overload. It’s filter failure.
I’m stealing the point made by Clay Shirky last year, where he gave a talk titled, “It’s not information overload. It’s filter failure.” The point Shirky made is that we’ve had an abundance of information for centuries. No one ever complained about the volume of books at the local library. The digital age has ushered in a need for new ways to filter information.
At FEI, I had the chance to speak with a number of companies interested in pushing innovation further inside their companies. One topic I discussed with them was the ways in which companies, with thousands of employees, can filter through high volumes of ideas to find the best ones. This was not a philosophical discussion for them. I heard examples of internal initiatives that resulted in hundreds of ideas. The challenge was having people review them.
This issue – filtering – is an important one. Missing out on the valuable ideas of employees has been the status quo for…oh, a millennium at least. Advanced companies aren’t satisfied with that anymore.
Presented below is a model for filtering through a large volume of ideas:
It relies on three elements:
- Individuals provide feedback, in aggregate
- Follow individuals, categories, keywords, tags
- Experts find overlooked gems
Let’s break ’em down.
Individuals’ Feedback, In Aggregate
“Crowds can be very good at evaluating innovations”
– James Surowiecki, FEI Boston 2010
“Wisdom of Crowds” author James Surowiecki made that observation. The basis of his statement is that crowds, with cognitive and heuristic diversity, offer multiple perspectives. In aggregate, these perspectives are stronger than the raw intelligence of any single person.
So use the crowds to provide the first filter for ideas. Distribute the workload!
With a small team evaluating all ideas, the workload can be large. Change that equation. With a large number of participants evaluating a small number of ideas, the work of identifying the most promising ideas is significantly easier, and better.
The great thing here is that people will self-select to provide feedback. They naturally weigh in on what interests them.
As for the types of feedback, basic votes are a start. They give a quick view on the idea overall. From there, deeper levels of community analytics can be applied to surface the best ideas. These include the reputations of individuals, the level of engagement and ranking of ideas based on multivariate scores.
Follow Categories, Keywords, Tags, Individuals
Employees are busy, and have a variety of tasks they need to tackle daily. This will put them in a variety of systems for work: office productivity apps, ERP systems, project management systems, etc., as well as the innovation management platform.
To stay on top of new ideas as they are posted, and new activity on ideas of interest, provide people with a variety of bases for tracking. Each person is unique in her interests, and give them the choice of what they want to track:
- Categories => ideas sharing some common characteristics
- Keywords => ideas containing words representing a subject of interest
- Tags => similar to keywords, but using the tag metadata
- Individuals => people who work in an area of interest
The tracking mechanism is email, which is probably the most used application inside organizations. The value is here is that even when an employee isn’t logged in to see the ideas as they are posted, they get email notification of them.
These notifications are important for distributing the review workload for large volumes of ideas. And they address much of Shirky’s notions about filter failure.
Experts Find Overlooked Gems
The crowdsourced feedback is powerful for surfacing the top ideas. But there will be occasions where potentially valuable ideas are overlooked. This can happen if the idea is incomplete, but does have a kernel of an important concept. Or the idea is so different, it may not resonate immediately with people. How to handle these cases? Organizations don’t want to miss them.
Inside companies, or even externally, there will be experts who provide deeper domain knowledge on a given subject. It is these people who become part of the idea filtering process.
Experts are assigned to categories, or keywords. They don’t have responsibility to review every idea that falls into their domain. Rather, they focus on potentially good ideas that receive less attention. The reason for the lack of attention may simply be that the idea isn’t that valuable to the company.
However, if there is value in the idea, the expert is the one who helps find and surface it. This distributes the work of reviewing ideas, but only for a subset, not the hundreds submitted by the community.
These three elements are solid, practical methods for evaluating the potential of a large volume of ideas. With the advantage of replacing the heavy workload that can fall solely on a small group of people.
Hutch Carpenter is the Vice President of Product at Spigit. Spigit integrates social collaboration tools into a SaaS enterprise idea management platform used by global Fortune 2000 firms to drive innovation.