Deep Learning Canvas and Viral Product Development
In the breakneck Deep Learning research field, the obsession is to dish out innovative research as quickly as possible so as to avoid being scooped by other researchers. Like similar games in academe, it is a game of enhancing one’s academic credentials through the accumulation of citations. We would of course like to see more real world problems addressed, however this isn’t a top of academics. Companies however have different priorities and therefore need to deploy products that are competitively superior. There is of course a massive knowledge gap of how we can take Deep Learning and build valuable products out of it. Here we discuss the nature of the products we should build.
Writing truly insightful and useful articles is very different from creating articles that become viral. I do notice this in my blogging. Many of the posts that I’m proud of writing, don’t receive the number of shares of my other simpler and less informative posts.
Jonah Berger is famous for introducing the elements that make up a viral post. Here are his elements:
Social Currency: We share things that make us look good (even if that means pictures of our cat).
Triggers: Easily memorable information means it’s top of mind and tip of the tongue.
Emotion: When we care, we share.
Public: Built to show, built to grow.
Practical Value: News people can use.
Stories: People are inherent storytellers, and all great brands also learn to tell stories. Information travels under the guise of idle chatter.
Why Ideas And Products Become Contagious: The Jonah Berger Formula
Ed note: For more on Jonah Berger's mission to map virality, pick up the April issue of Fast Company.
Coincidentally, these match like a glove the same elements that make up a good product. Clay Christensen who was first to coin the “Theory of Disruption” has this new “Theory of Jobs to Be Done”.
The “Jobs to Be Done” framework is a way at identifying the needs of customers. In conventional approaches we segment customers through attributes such as income, age, race and other categories and create products based on these. So the focus is on what companies want to sell rather than focusing on what customers need. Christensen explains this best in his this story about milkshakes:
Instead of focusing on attributes of the milkshake (thickness, amount of syrup, etc.) the researchers sought to understand the Job to Be Done for milkshake customers. They learned that customers were “hiring” the milkshake to help them stave off hunger and avoid a mess during their morning commute. The competitors for this job weren’t other milkshakes, but breakfast foods that are easy to consume while driving, such as bagels and bananas. With this insight, the fast-food chain began marketing the milkshake as a breakfast item, and sales soared.
Christensen’s approach is to focus on what a customer needs during their daily activities. Customers purchase products because they find themselves needing a product to solve their specific problem. Therefore, we need to understand the context in which a customer finds himself in and then identifying solutions that enable that customer to solve their problem. Understanding the “Job to be Done” leads towards the creation of products that are more likely to be “hired”. That is, we create products that are tailored to addressing what customers need to get done.
The needs to get done are multi-dimensional and they address some of elements as Jonah Berger’s virality elements. That is, beyond just the pragmatic functionality, we need to address dimensions such as the social and emotional needs. So when we build products, we don’t address just functionality. Steve Jobs was a master at this craft where every minute detail of a product was focused on. So Jobs demanded the rendering of fonts on the early Lisa computer despite the objects of engineering staff that focus more on utility.
The reason I bring this up in the context of a blog that focuses on Deep Learning AI is that we need to better understand how to apply this disruptive technology to the creation of innovative new products. Innovation however is a difficult problem because stuff that’s surprising and novel can also be considered as innovative. Unfortunately, many of the things we consider as “cool” has no effect on our bottom line.
The Deep Learning canvas focuses on the development of customer products “to get the Job done”. Because it goes beyond plain utility and addresses the social and emotional needs of the “human in the loop”. The likelihood of the success of the resulting product is therefore much higher. Many products have failed, not just in the marketplace but also inside corporations, despite addressing all the required functionality. It should be plainly obvious, products that people hate, do not become successful. It doesn’t matter if its in the free market or within an organization.
Does a product then have to be also have viral elements to succeed? Well, why not? We should strive to cover the extra mile of addressing beyond just “the job that needs to done”. Let’s look at the three other elements: Trigger, Public and Stories. The commonality of these three elements is that they pertain to availability and accessibility.
More on this in this book:
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