Using Machine Learning to Create New KPIs

By Abigail Collins

 
(Pixabay.com)

(Pixabay.com)

Machine learning is a very pressing global topic right now.

Arthur Samuel described the technology as a way of programming that gives computers "the ability to learn without being explicitly programmed." It detects meaningful patterns and then makes decisions based on those patterns.

It is expected by 2020 that AI and machine learning will transition from mere automation to augmentation, meaning the technology will be able to relieve humans of repetitive work and help them make decisions faster. From chatbots, robotic process automation, and customer segmentation, machine learning will soon assist humans with tasks like optimization, new pricing dynamics, and the ultimate one, AI-assisted creativity. Maryville University also cites significant growth in software development, with 1.1 million computing-related job openings to be expected by 2024. That means it is increasingly vital for machine learning tools to be developed to improve software and keep it up to date.

New Performance Metrics

At present, machine learning can already be leveraged to make more informed business decisions because it can create new Key Performance Indicators (KPIs). However, new technology doesn’t necessarily mean more insights. Ad Age’s recent study with Advertiser Perceptions found that fewer than 45% of marketers considered themselves advanced in the use of data science.

Traditionally, marketers would rely on KPIs like clicks, conversions, and time spent on-page because they are easy to measure. However, to drive real business outcomes, you should be able to connect the data they have to your target goals. For instance, a marketer’s objective isn’t merely to get more visitors to a website, but rather to reach the right customers that will suit the company’s long-term goals.

The key is to find hidden value in the data that’s mined, and that is where machine learning can help. Machine learning can identify patterns that are not visible to humans, and these patterns then help companies arrive KPIs that measure specific types of behavior. Netflix, for example, uses binge-watching as a KPI for business success. The same data it collects from viewers is used to inform program creation and recommendation, which encourages more binge-watching behavior.

Defining the Right KPIs

When it comes to KPIs, everyone within a company should be on the same page. “Active Users” shouldn't mean different things to different stakeholders. To ensure consistency, everyone must agree on one definition of what an “active user” is, such as a person who has opened an app, for instance.

Once KPIs are clearly established, a business can leverage its communication channels (meetings, emails, intranet, etc.) to remind employees of the definitions and reinforce consistency. This is a way of operationalizing the KPIs so that everyone in the company knows what they need to be working on.

Searching for the right metrics to suit your business goals is not as easy at it sounds. However, you can expand and improve the available choices by supplementing your company’s data with data from external sources. This allows for additional analyses that can help produce more insights, similar to how a travel agency can tap into weather data to find out the impact it has on its bookings.

Importance of Machine Learning

AI and its machine learning capabilities are becoming increasingly important in today’s market. The KPIs that have worked before can quickly become obsolete and may be as applicable in the current business environment as they once were. In fact, 3 in 4 organizations implementing AI and machine learning increase their sales by more than 10%, according to a Capgemini report on AI. Humans will need the help of machines to not only make better business strategies but also to stay ahead of the competition.


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