Article
Why it’s time to consider bringing machine learning to your business
Oct. 5, 2020 · Authored by Clayton Cafferata
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Article
Oct. 5, 2020 · Authored by Clayton Cafferata
For years, machine learning — along with big data, data analytics, visualization, and artificial intelligence (AI) — has been at the forefront of discussions about how managers can make informed decisions or improve and automate business processes.
While the technology for machine learning has existed since the 1980s, the breakout of machine learning, and its benefit for businesses, came about in 2006. Since then, the technology has matured to the point where incorporating machine learning into a business has never been easier or yielded greater returns.
Indeed, machine learning has become so ubiquitous in certain areas that choosing not to incorporate it into your operations could make it harder for your company to stay competitive.
Machine learning is often seen as a subset of AI, and it overlaps with statistical learning; both attempt to make predictions by finding and learning from patterns and trends within datasets.
There’s a common misconception that certain companies or use cases are too small, or not complex enough, to benefit from machine learning. However, the contrary — even for single-owner operations — is more often true.
However, machine learning uses mathematical models based on passive data sampling to make predictions or decisions automatically — that is, without being explicitly programmed to do so. Generally, it could be considered whenever it isn’t feasible to accomplish the same objective with discrete conventional rules or programming algorithms.
There’s a common misconception that certain companies or use cases are too small, or not complex enough, to benefit from machine learning. However, the contrary — even for single-owner operations — is more often true.
A large staff of PhDs and data scientists are no longer required to employ machine learning. Even though the underlying technology and math is complex, companies don’t have to start from scratch when implementing this strategy thanks to recent improvements in data storage, computing power, and software.
Open-source algorithms and tools are widely available, and companies like Microsoft, Amazon, and Domo offer these tools as part of their cloud-based services. The availability of these tools makes machine learning well-suited to analyses and the improvement of routine processes for smaller companies.
Hershey licorice machines had a tendency to run too hot. This caused the company to overproduce licorice at a rate of 100 grams per minute, all of which ended up being given away or considered waste.
By adding sensors — linked to Microsoft’s Azure Machine Learning — that could better respond to production changes and regulate the machine’s temperature, Hershey was able to reduce its wasteful licorice production from 100 to 25 grams per minute.
Without data scientists, Hershey was able to use Azure Machine Learning to predict machine operating temperatures and enable production changes; the result was a reduction in waste and an increase in cost savings as reported by PC Magazine on April 28, 2017.
Basic machine-learning algorithms can identify correlations and make recommendations by learning from transaction histories. They can also make experiences more contextual and personalized by testing and developing better social media ads and automating and improving customer service requests.
Machine learning has the potential to help you look beyond obvious issues and anticipate future changes your business will need.
Customer-support logs can also provide insight into customer issues including the amount of time it takes to repair a problem. Data may also be stored in text that indicates what customers are looking for in the future.
Financial companies use machine learning in a variety of other ways including:
Human resources departments leverage machine learning for recruiting and retaining top talent while smart assistants — like Google’s Mini and Amazon’s Alexa — use it to provide answers and suggestions.
Machine learning has the potential to help you look beyond obvious issues and anticipate future changes your business will need. Incorporating a machine learning algorithm in to operations is an iterative process; data changes, preferences evolve, and competitors will emerge. When implementing machine learning in to your operations, it’s critical to consider three key items:
With these questions in mind, you can begin the processes of strategically integrating machine learning capabilities into your organizational operations.
These activities are reasonably accessible to small businesses, which means there's an opportunity for machine learning to improve pretty much every aspect of how a company uses data.
A small improvement in a key performance indicator (KPI) can have a significant impact on a company’s bottom line. The challenge largely rests in identifying where these opportunities lie.
Machine learning helps provide actionable insights to improve marketing decisions, such as determining which ads are most likely to be relevant to specific users.
Email and paperwork automation — as well as chatbots — are commonly available machine-learning solutions. These examples help companies provide better customer-service and adaptive responses while saving employees’ time.
Machine learning tracks patterns in data which makes it well-suited to the following actions:
Fraudulent activities that are dispersed and involve low enough values don't always get flagged by standard fraud-prevention systems. By using a machine-learning test programmed for this task, merchants might be able to catch fraud patterns that wouldn't otherwise be visible.
Personalized suggestions provided in services such as Amazon, Siri, Netflix or Spotify employ the same machine-learning technology that can be used to personalize ad campaigns and suggest next purchases for customers. These often appear as “you might also like” suggestions on retail websites for example.