Why companies should apply machine learning to their overall digitization efforts

Darren Person is the Chief Information Officer at NPD group. He manages the company’s operations and technology departments.

In my last article, I explained how businesses should differentiate between digital strategy, digitization and digitization. The piece focused on how everyone uses the terms differently and ultimately how digitization was really about how companies can better automate processes and practices in their organization.

Example: Digitization turns a fully or partially manual process into a fully digital one. This could include automating workflows or processes. Digitization, on the other hand, converts analog content in digital form. Simply put, the goal of digitization is to create efficiency and extract value.

A big part of digitization is figuring out how to improve processes, not only to reduce costs and save time, but also to improve an organization’s productivity and overall customer experience. Machine learning (ML) and artificial intelligence (AI) are an important part of the formula, as both help companies scale.

ML tools can help automate the process of extracting insights from large volumes of data, then sorting and indexing them to create value for the customer. For example, at a consumer packaged goods company, they can analyze their point-of-sale data to create a better product—and more compelling marketing—for their customers. Let’s assume a product like toothpaste, and then expand that SKU to different attributes related to the toothpaste: does it contain fluoride, its color, whether it’s whitening, flavor, whether it’s a multipack or just one, etc. When you take a look at the sales volume of these products over time and analyze the data, useful insights can be gleaned. Maybe a customer is willing to pay 5 cents more for whitening, or the combo pack always works better at a particular store.

This type of ML and AI technology isn’t just for CPG brands. It can be applied to almost any industry. For example, if you are in the healthcare field, the application is similar. Instead of using point-of-sale data, it’s patient profile data. Of course, all patient data should be anonymous, but if you analyze diseases in thousands of patients—and the drugs they’ve used to treat those diseases—over time, you’ll see patterns in their outcomes. This is a highly informative and useful insight that can be leveraged by ML and AI to provide doctors and scientists with the information they need to improve patient care.

The same goes for media companies and content businesses. As the saying goes, content is king. But the reality is that all knowledge and insights are locked in content—no matter what form it takes—and are invaluable to an organization. For example, take a company that creates, distributes and broadcasts video content. ML technology can help convert audio to text by speaking to text ML engines, which generate even more data that over time can provide insights, such as a sense of content about a particular product, character, or scene.

Search engine giants are already capitalizing on this technology. Video results don’t always take you to the beginning of a particular video. Instead, they link directly to the part of the video that provides the most search relevance, which by extension provides the most value.

Integrating ML into your processes

To move forward, take a step back and create time to think about where technology can be applied to your business, and then focus on some of those areas where you can achieve quick wins. Businesses should choose two or three areas in a given year where this technology could solve some of the business challenges they face in their organization.

Look at the low hanging fruit. Identify where time is being spent less efficiently. For sales, is it time spent scheduling calls? For distribution centers, do you schedule deliveries? How can you apply technology to improve it? ML can help optimize the schedule, look at the location nearby, and then make sure the right people are in the right places at the right time. Programming and statistical algorithms will help you automate finding and implementing efficiencies at scale to improve your business results.


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