How consumer review data makes the "Jobs-To-Be-Done" Framework more accessible.
If you work in Consumer Insight, R&D or Innovation, you have likely run into the "jobs-to-be-done" framework.
At the risk of oversimplifying, the theory is based on the idea that people buy products and services to get a “job” done. By understanding in detail what that “job” is, your are far more likely to create a solution that will differentiate your product in the marketplace and, thus, drive business growth.
Or, as Asi Maura eloquently said in a recent article, a job-to-be-done is the instantiation of an unmet need or want (in response to a trigger).
Traditionally, to get too meaningful "jobs", researchers would conduct interviews - many, many, many, interviews. It cost time, money and manpower, making "jobs" research a luxury. But, as it is with the so many traditional methodologies that are being replaced by technology-enabled research, a faster, more powerful means of identifying "jobs" has emerged.
Instead talking to a handful of consumers in the real world, the new normal involves leveraging observational ethnographic research online. By analyzing millions and millions of consumer reviews, we can identify consumer painpoints across almost any product category.
Or, to borrow from Maura's language, we can identify an unmet need, with a clear definition of both the instant it pertains too and the trigger that has made it relevant to the consumer.
Even better, because we are studying millions of consumers, we can better calculate the number of consumers this relevant too, as well as look at the types of products that they have purchased in the past, in order to calculate how much "fixing the problem" is worth to them, meaning we can identify the most valuable "jobs".
Let's look at one example of how this can be accomplished.
Leveraging the MotiveBase Needs platform, we have created a dataset of product reviews for Dairy Products. Our goal? Identify an unmet need and build a Jobs Spec for "Shredded Cheese".
This was the search term we used to begin our search. After less than 15 seconds, the system has analyzed over 226 products where the consumer has discussed the usage of shredded cheese.
The AI anthropology engine, groups the most dominant painpoints and plots them so we can immediately focus on the upper right hand corner where the machine has grouped painpoints that are relevant to more consumers, and that the consumer is willing to pay more to eliminate or solve for.
We can see the painpoint of "texture" and by clicking on that painpoint, the system identifies that this is relevant to almost 21% of shredded cheese consumers.
We can consumers see that this is relevant to consumers that over-index on young, 25-24 year-old single parents, in the middle class.
Most importantly, but looking at the reviews the consumer has shared, we are able to identify that this is largely a painpoint that emerges during dinner, when the consumer is rushing to make pasta, pizza and nachos.
As they rush to make the meal, the texture of the cheese can turn rubbery when it melts, or gritty when mixed in a pasta sauce.
Finally, we can click on "needs based segmentation" to identify the key motivation that is driving the needs of the consumer.
All of this can then be used to fill in a "Jobs" spec.
Everyday, consumers are revealing millions of painpoints in the product reviews they leave online. By marrying decades of social science theory with the latest in NLP and AI technology, solutions like the MotivBase Needs platform will make the "Jobs-to-be-done" framework more accessible and affordable.
And, in turn, it will make products more relevant and more desirable to the people that use your products.