Let’s look at early data-science adopters : Companies like Google, Facebook, Amazon, and Pandora with its genome project fifteen years ago. Most were tech companies, but there were one or two long standing incumbent corporations that were also early adopters
Looking closely, you can see these have long benefitted from using state-of-the-art machine intelligence to leverage data and gain insights into their businesses and customers. They have learned how to better engage with and serve those customers, how to create new products for them, and how to tailor messages for them. As a result, these early adopters have become leaders in their industries, radically increased their ROI, and now control large markets.
Machine Intelligence, fully applied, will exponentially increase the benefits your business derives from its data. When you dig through all your data. create successful machine-intelligence-modelling techniques, and operationalize them throughout your organization, you will have profoundly leveraged your data assets. Transformation will occur everywhere in the enterprise, and many, if not all department, will be able to utilize the customer insights you have obtained.
How successful companies are using Machine Learning to increase revenue
Today, the most innovative companies rely on data-science and machine-intelligence techniques to drive and add value to business processes and user experience. When I ask executive what type of company Amazon is, they usually say, “Amazon is everything.” This is somewhat true, but if you really dig in, you’ll see that Amazon is essentially an enormous supply chain company. They them took their long supply chain and created modelling techniques to optimize data in every link of this chain.
Eventually, this huge, interconnected chain became extremely efficient and better able to serve Amazon’ s customers and users. Amazon perfected machine Intelligence in every part of its large supply-chain structure.
Consider , for Instance, how Amazon optimizes one part of its supply chain , its warehouses. In a medium-size Amazon warehouse or distribution center million of package go in and out every day. Can you imagine one person or even an army of 500 people trying to figure out how to organize millions of packages going in and out of a warehouse efficiently? It’s impossible. By leveraging data with modelling techniques, Amazon warehouses can optimally store and enable those million packages to route in and out to their proper destination.
Alibaba, the Chinese Amazon, does the same thing: employing robots to quickly organize their their large warehouse facilities. Learning Algorithms tell the robots how to organize millions of packages efficiently so that when a truck comes, they can load iy=t quickly and start shipping seamlessly.
Another area, among many others, where Amazon uses modelling techniques is delivering time. Amazon leverages parsed data to tell us exactly when we can expect to see our packages on our doorsteps. They have a long history of shipping items across the world, which provides an enormous amount of data. They also have access to weather data and can predict when their vehicles need maintenance. All this data taken together allows them to predict, with great accuracy, when a package will be at our door.
Netflix has excelled for years at leveraging data to provide movie recommendations. Every time you go to Netflix, you see your movie recommendation on the first screen, and the company has done a great job of making selections you will resonate with. Facebook has also perfected this, finding the right content and advertisement for right users, based on their preferences, by applying machine intelligence techniques to the curated data in its news feed. Google searches do much the same thing. Every time we google something, we get resonant results.
Futurepoof your business with machine learning
Although machine intelligence is already being used today, the potential it holds for business innovation in the future is even more interesting. The future of business innovation has machine intelligence at its core, driving product development and user experience.
A prime example is Nest, the latest generation of thermostat. Products like this are designed from the inside out, not the outside in. Nest is a machine Intelligence device that connects to your phone. It knows when you are close to home because the phone automatically connects to your router, while simultaneously receiving external data, such as the weather. You play with Nest for two weeks, giving it feedback on whether the room temperature is too warm or cold. The machine -intelligence device learns from this input and other data it collects, and after two weeks, you have a device that serves you without your needing to to touch it. Nets must be doing something right since it is already selling millions of units. It’s a product built entirely around machine intelligence at it’s core.
Other examples are Spotify and Amazon Echo. Both rely heavily on machine intelligence techniques to create, sell and make recommendation that serve users better which is why they are popular.
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