In the past decade or so of Digital Transformation, no one discipline has had as dramatic an impact on the business world as data and analytics. Data can now be collected, analyzed, and optimized over every step of the business journey, and the effect, not only on business productivity but on the bottom line, has been enormous. But involving people in analyzing and acting on that data is SO analog—what if there was a better, smarter, faster way?
Enter machine learning—a buzzwordy new application, sure, but one that can drive significant results for your business. But how do you separate the hype from the practical application? And how can you put machine learning to work for you today and tomorrow? Read on to find out.
Rise of the Machines
What IS machine learning, exactly? Machine learning is often confused or conflated with artificial intelligence—and while it is a function of AI, machine learning refers specifically to the ability to give computers access to large and complex data sets to iteratively learn and make adjustments to extract impactful business insights across the organization.
The application of machine learning in a business environment is as such: first, an algorithm is applied to a data set or data store. The algorithm is then able to parse the data, ‘learn’ from it and then make a determination or prediction about something in the business.
This is already having a transformative effect on businesses. In an MIT Sloan Management Review survey of executives at 168 large companies with at least $500 million in annual revenue, 76% of companies responded that they were already using machine learning to increase their sales growth. Moreover, more than two out of five companies have already implemented machine learning in sales and marketing.
So, how is this being put to use in today’s enterprise businesses?
Big Data, Bigger Business
Machine learning algorithms are currently finding practical application in many business environments and processes. Here, we cover a few:
Predictive Modeling and Forecasting: Retail giant Walmart caters to millions of customers and, as a result, has access to one of the world’s largest stores of customer data. But data from 149 million Americans would be incredibly unwieldy for a data scientist, or even entire data scientist team, to sift through—it’s estimated that Walmart captures 2.5 petabytes from customers every hour. With machine learning, though? Anything is possible.
Walmart has applied algorithms to data stores of customer purchase info, location data, and website activity. With predictive modeling, they’re now able to predict what consumers will buy, and use that information to serve up personalized offers, shopping list, and item recommendations.
Fraud Prevention: You’ve likely encountered at least one form of machine learning in the wild—many banks and payment processors now use machine learning algorithms to recognize transactions that are different from consumers’ usual purchasing patterns and flagging them for fraud. Machine learning algorithms can quickly parse purchase data and quickly apply rules based on location, amount, and thousands of other variables—and as your purchasing history evolves, the algorithm will adjust and ‘learn.’ This is why your account doesn’t get flagged for fraud every time you travel or buy a big-ticket item—which are both situations that would happen were humans managing the data.
Customer Lifetime Value Modeling: What’s the value of a customer’s total interaction with your business? A simplistic view would look solely at one input: the revenue generated by a customer on a single purchase multiplied by the estimated number of purchases throughout their life. But in the real world, customers can impact revenue in different ways—referrals, word-of-mouth, social media shares, and more.
To accurately determine a customer’s lifetime value, a machine learning algorithm must take into consideration both the ever-shifting value of these various revenue drivers, as well as estimating a customer’s direct revenue contribution based on the frequency and amount of their purchases, which can take place on a very random interval over time. The end result is always knowing exactly what a customer is worth to your business—and being able to use this data to influence customer acquisition cost and sales and marketing spend.
Inventory Management: Finding the sweet spot for on-hand inventory continues to be a hurdle to overcome for many retail chains or online storefronts, and carrying too much inventory on hand or failing to meet consumer demand can be a make-or-break (particularly as companies gear up for the busiest retail time of the year). Machine learning algorithms can take a highly sophisticated approach to inventory management, factoring in sales projections, real-time sales statistics, data on regional economies, and even the weather—then optimize inventory amounts for each individual retail location based on forecasts.
Applying machine learning to inventory management can help multi-location chains eliminate the need to carry costly inventory that might not sell, reduce inventory shortfalls, and even reduce transportation and logistics costs as stores will now have the precision amount of inventory on hand, every time. In fact, it’s estimated that businesses can reduce supply chain forecasting errors by 50%, and lost sales by 65%, all thanks to our robot friends.
What’s Next for the Machines?
As algorithms become more sophisticated and datasets grow larger and more unified, computers become smarter and the opportunity for machine learning to fundamentally transform your business will increase. Revenue from the Big Data industry is estimated to grow from $35 billion in 2017 to $42 billion in 2018, and to reach $103 billion by the year 2027. That’s no small investment—and as the data industry scales, machine learning uses will increase in Business Application.
Machine learning algorithms will soon become part and parcel of the software solutions you use to manage your business, IoT-connected devices, and more. Imagine self-optimizing manufacturing equipment that schedules its own downtime based on shifting production needs. Machine learning will also have a profound impact on human-computer interactions. We’re already seeing a proliferation of chatbots assisting with customer support and marketing functions, but the next generation will be more capable of reacting to the nuances of human speech—things like tone, sarcasm, mood, and body language.
Machine learning is already having a transformative impact on modern business, and as the technology evolves and global data soars, your business is at risk of being left behind by not leveraging this incredible opportunity. Contact us today to discover how you can make machine learning work for you.
