The future of machine learning in business looks bright. Machines that learn have long been a bugaboo in sci fi books and movies. Once the robots surpass humans in intelligence, what’s to stop them from taking over?
In truth, we’re a long way from that dystopian outcome—it definitely won’t happen within our lifetimes. But machines that “learn” are very real. The medical industry has used the type of Artificial Intelligence (AI) known as “Machine Learning” (ML) to extract patterns from vast datasets, learning to spot diseases and other pathologies earlier than ever before.
The business world is waking up to what the medtech already knows—the devil is in the data. But with ML, those devils can actually be angels.
So what is “machine learning,” if not the first step down the road to Skynet? It is a generalized term for the ability of a computer to intake vast quantities of data and make observations, detaked patterns, or extract judgments from that data. A key component of ML is that the computer gets better and more precise, the more information it receives.
On a macro level, the best example is the Google Search algorithm. The more searches it records, the more clicks one link gets over the other, the more its web crawlers map the internet … the better it is able to return the most relevant data for any given search, even as its site index balloons past all human reason. That’s a hallmark of AI—not the ability to perform tasks like a human brain, but the ability to perform highly complex tasks better than any one human brain.
Not every company is Google, but almost every vertical is capable of harnessing machine learning in business, in more pedestrian forms or even in dramatic forms. Here are five machine learning applications that can enhance your business if and when you adopt them …
1. Building Better Tech And Digital Products
Machine learning applications in business eliminate the guesswork from discovering consumer pain points. Examining vast quantities of consumer data can reveal gaps in the market that new products can fulfill.
Once products are in prototype or MVP stages, vast quantities of user-generated data can be quickly parsed by ML to improve the product or service and devise a marketing plan to get the product or service in front of its most appropriate market.
2. Churn Modeling
“Churn” is the bane of every company’s existence. It basically refers to the losing of a customer, necessitating the need to acquire a customer of equal value to maintain the same revenue base.
ML churn modeling can examine all the data pertinent to customer churn to build a comprehensible “model” of what customer churn “looks like” for that particular company.
Do departing customers have any particular characteristics? Does the company lose them at a particular touch point? Are there any actions that seem to avert or reduce churn before it happens?
This data would be nonsense to human eyes, but an ML algorithm designed to model customer churn could help a business:
- Identify customers at risk of churning and targeting them for intervention.
- Identify reasons for churn and attempt to ameliorate them.
- Find touchpoints responsible for churn, try to improve them, and measure the results.
It is five times more costly to acquire a new customer than to keep an existing customer. ML churn modeling could be the key to keeping your business on the happy side of that equation.
3. Dynamic Pricing
Before dynamic pricing, deciding what things cost was a data-driven exercise, but not nearly so sophisticated. Vendors might pick a price based on a rudimentary analysis of what competitors were charging for similar products. If the product flew off the shelves, maybe you guessed too low and your customers are getting a deal. If units fail to move, maybe you priced it too high and have to adjust.
ML dynamic pricing absorbs datasets to attempt to set the best price possible—that is, the highest price consumers will happily pay, maximizing profit while maintaining steady inventory. This type of machine learning application performs complex equations to try and determine factors like supply and demand, comparing competitor rates and differentiating qualities to arrive at an exact price.
Using dynamic pricing, the price of a good or service could change on a daily basis, based on market conditions. Examples include dynamic pricing of products in an eComm marketplace like Amazon, or dynamic pricing of apartments for rent based on internal and external inventory.
4. Image Classification And Image Recognition
Machine learning has moved beyond the point of just being able to read ones and zeros. It can actually read images. Google Reverse Image search is the obvious example of this—an algorithm that can analyze an image and find that same image online. The ability to “see” images and analyze visual information is a big leap forward for machine learning in business.
Doctors use image recognition and classification to detect subtle cancers and other abnormalities on X-ray and MRI images.
Businesses, on the other hand, could use image recognition and classification to identify successful design elements of physical products or digital experiences across large data sets. It offers the heretofore unheard-of opportunity to quantify a visual interaction, data previously locked in qualitative analysis.
5. Customer Recommendation Engines
Consumers see customer recommendation engines at work when they intentionally (or unintentionally) click through to an Amazon product page … and for the next week and a half, ads for that same product appear on their Facebook wall first thing every morning.
ML customer recommendation engines learn about a customer’s buying habits and interests, then make upsell or secondary sales recommendations to the customer to try and get them to buy again and again (remember, it’s five times easier to keep an existing customer than to rope a new one!) As the ML algorithm becomes “smarter” with more information, the more relevant the recommendations become, and the more likely they are to prompt an impulse purchase.
Customer recommendation engines can also use machine learning in business to make “customer personas” that it can apply to customers about whom they have gathered very little data, or to make “lookalike lists” to market to on social media and search engine channels.
With a robot apocalypse unlikely in the foreseeable future, the future of machine learning in business looks bright. Machine learning applications can eliminate the guesswork from a variety of business processes, allowing entrepreneurs to process vast data sets and make data-driven decisions about product design, customer retention, pricing, and touchpoint optimization throughout the customer journey.
Dr. Daniel Werner –
Co-Founder Venture Leap
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