Coming up with the most innovative idea to solve their customer problems is one of those things almost every organisation desires. Sticking to the norm means a highly competitive space, where-by you can only win by producing products and features that are just a little bit better than the next one. If you can create a game changing product or feature, this can separate you from your competitors, allowing you to dominate your market and bringing long term success.
The problem with all personas is that they are still fictitious.
Sturgeon's law states that 90% of everything is crap, which basically means your first idea is rarely the best. With that adage, innovators should step away from long, drawn out requirements and waterfall types processes.
In order to innovate you need to develop lots and lots of ideas. Then you can test your ideas with customers and with enough volume you'll hit gold sooner or later. Whilst this ideate, test and iterate philosophy seems on the face of it a good strategy, it actually has some serious flaws.
For a start this kind of approach often means your innovation team are shooting in the dark. They don't know what a good idea looks like as there are no pre-agreed criteria for assessment or prior insight. The probability of finding the right idea is greatly reduced.
Secondly since you don't know which of your customers needs are unmet, chances are a lot of your ideas already have pretty decent pre-existing solutions out in the market.
With these constraints in mind: how long will it take to strike gold using this ideate-test-iterate approach?
One solution, which we nearly always turn to at Webcredible is upfront customer research. Through a bunch of interviews and ethnographic research you can build up a pretty decent picture of your customers goals and needs. This can form the basis for innovation.
Often these user insights can be consolidated into personas, which are representations of customer segments goals, needs, demographics, attitudes and so on.
Some personas in the UX industry are well produced and others terrible. But the problem with all personas is they are still fictitious. They are based on researched user data but they aren't a real person.
Any ideas you come up with to try and solve each of these personas problems are a construct of your own assumptions about what certain features will match specific goals and attitudes. There is a chain of causality you pre-dispose on them as an artefact.
Furthermore you have no data on which of these needs are already met, so you'll often end up wasting time arriving at solutions that are already catered for.
Finally, it's fairly rare that your personas are bolstered with quantitative data, so they are rarely of statistical significance. You often don't know how many people you can influence with any idea you come up with, because you don't have a % of population share of each need.
With this in mind, personas can be a useful artefact for justifying design decisions to clients, and no doubt better than innovating without customer research. But there is another solution.
Anthony Ulwick from Strategyn presents a new model for innovation in his white paper: outcome driven innovation. Whilst he makes some very strong (questionable) claims about his rate of success (86% success of innovation), he opens up a world of possibilities in how we approach innovation. For more detail, I point you to the paper itself, but with some adaption here are the key learnings.
The central concept to this framework is 'the job'. Whenever a customer interacts with your product or service they are hiring you (paying) to get a job done. A job is the fundamental goal customers are trying to accomplish or problem they are trying to solve in a given situation.
A job-to-be-done can be expressed as a job story.
Note that unlike a traditional user story, which uses format: 'As a [persona type] I want to [action] so I can [goal]' - the job story focuses on context rather than persona. By uncovering the time, environment, situation, etc, we get a lot more useful information about what kind of solution would be useful built into the story.
Also note that it is solution-agnostic. Because of this the possibilities for solving the problem can be much broader. We can think outside the box.
According to Ulwick all jobs-to-be-done in a given problem space (i.e. when someone is hiring your product) can be mapped to the universal jobs map. Jobs can be broken down into process steps, which will un-cover all the related jobs-to-be-done. This should at least help to understand the types of functional jobs that could be considered.
A job also represents a series of customer needs. We can discover jobs-to-be-done by doing similar research as we would for personas: a series of 1-2-1 interviews where we probe on people's experience in a given problem space.
We are much less interested in the demographics and general attitudes to current products. What we want to know, is what jobs were the users trying to get done. Therefore the main aspect of these interviews is to interrogate the causality and series of events that led to a particular purchase.
This is a complex interview technique that requires building a lot of empathy to trigger memory recall. The process will let us know what job that product was hired to solve.
By starting our questioning with the point where the user decided to buy the product we can work back interrogating the series of events that led to that point. Afterwards, we can also assess how effective that product was and whether this led into another cycle of finding a solution for the job.
After analysis we should be able to develop a number of job stories, which represent our customers needs with plenty of context for each. We can consolidate very similar jobs, being careful not to over-simplify. We are just getting started...
Next we need to see how significant these jobs-to-be-done are in the overall marketplace. We care about 2 things. Firstly, how important these needs are to people, and secondly, how un-met these needs are by current solutions.
A questionnaire survey can be produced with all the job stories and responses for importance 1-5, and satisfied with current solution 1-5. Since you don't want to end up with un-reliable data, which will lead you to innovate on false assumptions, you should pilot the survey with a few users face-to-face first to ensure all the job stories make sense.
Using best practices survey sampling, we can then circulate this across our target demographic and collate the responses. You may want to consider paying a small fee (e.g. £10 per participant) to ensure motivation and response accuracy. £5000 of incentives for 500 responses may seem like a lot, but the amount of time and money the insight can save you should more than pay off.
After analysing the data we can then plot the responses on a chart, representing Ulwick's opportunity algorithm.
What we are looking for are the jobs-to-be-done that are very important to our audience but customers are not satisfied with current solutions. This is the sweet spot for innovation.
Now we finally have a solid point to start innovating. Our confidence can be statistically significant with the right numbers, and we have segmented our audience in a useful way: by un-met needs. We now won't focus on those needs we know are either unimportant, or well met by current solutions.
With this data in mind we can conduct brainstorm activities with our innovation teams to solve these needs. And because there is no solution in the job story we can think outside the box. We don't need to be constrained by our current product or feature set. The ideas we go onto develop will have a much better chance of being truly innovative.
So there you go. A better way to (start) innovating.
Keen to learn more? Check out our Innovation and Product Development course!