Five Steps to Approaching Machine Learning for Your Business

Machine learning is rapidly becoming a buzzword in the field of online marketing. This buzz is well-deserved as the efficacy of machine learning increases at a remarkable rate. Companies across industries are finding new ways to optimize this technology to drive consumer relationships and revenue.

What is Machine Learning?

Machine learning is a type of artificial intelligence that is beneficial to both consumers and businesses. While you may have seen artificial intelligence in science fiction movies like 2001: A Space Odyssey, now it’s become a reality. Machine learning allows you to gather data from consumers and use it to predict future behavior.

Machine learning is already driving some of the best experiences on the Internet. Every time Netflix recommends a show, it’s based on previous viewing habits and the ratings of both the individual who owns the account and those with similar viewing habits. That’s machine learning. And whenever you check Facebook and see a product, account, advertisement, or friend recommendation, that’s Facebook attempting to provide added value to their consumers. Because machine learning isn’t perfect, it doesn’t always hit the mark, but it provides valuable insights and on-target recommendations with surprising regularity.

Businesses benefit from effective machine learning because it makes it easier for them to offer their consumers exactly what they want.

The Balance Between Machine Learning and Humanity

Even with the obvious benefits for consumers and businesses, it is still necessary to carefully balance the predictive power of machine learning with the contextual view that a human being brings. Developers and businesses should approach it from a sense of service to their consumers rather than just manipulative profit engineering.

Consumers are remarkably adept at identifying when companies steer them toward an outcome with no regard for their best interests. If machine learning is used to urge consumers to make a purchase that is contrary to their base desires, it will ultimately be resisted and disdained. But, if machine learning is used to match a customer’s preferences only with those companies and services with which they’re in exact alignment, then consumers will regard the interaction as valuable, welcomed, and preferred.

The Five-Step Test & Learn Approach

Implementing machine learning into your business model doesn’t have to be overwhelming or overly complicated. By keeping the project simple and focused, it is possible to make advances more quickly and grow the project organically. Numerous third-party platforms make it easy to integrate machine learning into advertising. It is also feasible to have a unique application coded for your individual needs.

Regardless of the method you chose, the five steps for implementation are similar.

Step One: Decide what the business objective for the initial test. What are you trying to learn? For example, an objective could be to decrease the number of consumers who abandon their shopping cart.

Step Two: Choose which customer segment will be the focus of the test. Will it be based on the total cost of the shopping cart, the gender or age of the user, the total length of time spent shopping, or perhaps consumers who have left items in the cart for a set of time? It is possible to test any or these. But, the more variables you include at one time, the more difficult it will be to determine a causal relationship between action, offer, and subsequent behavior.

Step Three: Decide on the hypothesis of the test. What do you think the result of the trial will be? What would an optimal result look like as opposed to a complete failure? Here’s an example hypothesis: If you offer a coupon to consumers after they have abandoned their shopping cart, then consumers will be more likely to revisit and purchase.

Step Four: Create the algorithm based on a cohesive message. Once you know the objective, customer segment, and the hypothesis that will be behind the process, it’s possible to create the type of cohesive messaging necessary for success. As the algorithm learns to respond based on the actions of the consumer, the potential responses need to feel coherent and authentic, so the user feels understood rather than manipulated.

Step Five: Test within a controlled environment such as a website or email list — an atmosphere that allows for the control of variables. Using a third-party site to test native applications leaves a lot open to chance and may not result in satisfactory results or understanding of those results. Once you set your parameters, machine learning makes the process of testing new approaches infinitely easier than the previously required manual effort.

Using this five-step process, you can learn what works and discard whatever does not. Then, with a responsive and observant team, your company has the benefit of instant analyzation and human insight. Suddenly, machine learning is far more than just a buzzword; it’s valued data intelligence for both your business and your customers.