Data and product development
Traditionally data’s aim before it was called Data Science, was to inform people what to build. For instance, things like where to open the next Java restaurant, what bottle sizes would work best, should it have 700ml of alcohol vs. 20 ml? which one sells better? It was mostly used to make those kinds of decisions.
But when it comes to technology it goes beyond just figuring out how you launch. If you are familiar with the product life cycle, a very good example is twitter. Think about the first people to adopt Twitter? Were they tech people or humanitarian response or people with a very small niche that saw the initial need? This is normally called the product adoption life cycle. In product development, you also have the innovators who are very important and the first people who accept your product.
One of the most exciting things is that for you to successfully launch a product, you have to identify with your early adopters. They determine how your product will perform. The value has to be really good if not, it will collapse before it gets to the mature or late adopters. Part of the reason you want to include data is to figure out your very first best early adopter to spread the word. Carry out data analysis, looking at thousands of data points, what’s working, the responses you get and how much information resonates with them, retweets if any, etc and take all that into consideration.
The very second part is after you’ve launched the product, and while it’s getting some traction, do a B testing, to improve as well as discover more things e.g create the same product with different interfaces. Look at which one people respond to or create a button on your website that does nothing and see if people will be clicking on that button. And then if more people click on that part of the bottom then you know you can move some functionality in that area. Amazon and Google do this every now and then to figure out what works and what doesn’t.
The very last part that is almost normally forgotten about is when a product completely fails. Of course, people get fired or other things happen but you will be told to explain the root cause or reason as to why your product failed.i.e was it because of the price, usage, functionality, etc? Data scientists come in handy, they fit into the start of the product inception touch of the product to the phases where you need to make improvements to the point where it actually fails. Involving them helps you keep tabs with what’s working and what’s not, enabling you to improve areas of concern.
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