5 ways to leverage a Machine Learning strategy

Machine Learning, a branch of Artificial Intelligence, uses special algorithms to allow computers to learn from data without ever being specifically programmed to do so. While the term was coined in 1959, the concept didn’t take flight until recent years. However, today machine learning plays an increasingly important role in optimizing an organization’s technological capabilities and automating processes from RPA to IPA.

If your organization can build a strong machine learning strategy, you’ll experience several short- and long-term benefits. It can serve as a critical component of your roadmap to move beyond manual, unreliable, and time-consuming processes to true Intelligent Automation of processes. Here’s a closer look at five ways your company can leverage machine learning and deep learning and actionable tips to get your strategy started.

What is Machine Learning?

Machine Learning (ML) is a subfield of Artificial Intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.

In basic form, machine learning can be divided into three types:

• Supervised learning
• Reinforcement learning
• Unsupervised learning

The type to be used will always depend on the type of expected result, the data to be trained and the techniques to be used.

With organizations creating more data than ever before, programs powered by machine learning provide companies with the flexibility to process, analyze, and develop feedback loops for data-driven process improvements and decision-making. That might sound a bit abstract - but in practice, it means your software applications will learn how to complete certain tasks (like predicting an outcome) without a human needing to tell them how to do it.

Machine Learning has been used for amazing purposes, such as building the neural networks that support Sophia, a social humanoid robot. However, for the typical business, machine learning is used in the development of recommendation engines, customer experience workflows, and other behind-the-scenes functions within businesses, such as natural segmentation, natural language understanding (NLP), fraud detection, credit scoring, and modeling.

What is Deep Learning?

Deep Learning is a Machine Learning technique that uses a similar analytics life cycle like ML but uses neural nets to solve problems equivalent to the human brain by means of functions.

Deep Learning is a key technology behind driver-less cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas Machine Learning is an application or subset of AI that allows machines to learn from data without being explicitly programmed.

Five ways you can leverage machine learning

Machine Learning can add valuable, instantaneous data capabilities to your organization. Here are some of the most common use cases that business leaders leverage with the technology.

1. Intelligently recommending products

Machine Learning and automation are some of the secret weapons that have built online companies like Amazon into the behemoths they are today. As consumers visit websites, search for products, spend time viewing product pages, and ultimately purchase items, each of these interactions becomes a single data point in a complex consumer profile.

With machine learning capabilities, it is possible to evaluate billions of data points (data frames) and begin to understand wider patterns of consumer behavior. Based on new insights, companies can develop product recommendation engines or more customized consumer experiences that provide the personalized and resonant digital interactions that today's consumers crave.

2. Predicting demand

In a scenario similar to the one outlined above, machine learning can use a variety of factors to identify product demand. Changes in search volume, a sudden rise in pricing, changes in competitor pricing, and other factors in the broader landscape can help provide early insight on changing demands. This has important implications across the value chain.

Consider the rapidly changing demand for certain types of consumer goods at the beginning of the coronavirus pandemic. With machine learning algorithms that predict demand, retailers were able to identify a surge in certain areas such as toilet paper or cleaning products and proactively restock those items, place orders from suppliers, and even manage buying restrictions to keep supply even. Machine learning can also help you identify declining demands, which can help you determine specific product segments to put on sale and to adjust your inventory plan. It can also help optimize the planning and delivery dates for big customers, and signal when it's time to consider what products to market and promote.

3. Dynamic pricing

In a brick-and-mortar setting, items are typically tagged with static prices. However, online retailers and venues have greater flexibility to price according to demand. With Machine Learning, it’s possible to watch a variety of factors such as changes in demand, the broader competitive landscape, and even search activity to identify which products and services are in demand.

A spike in demand may signal an opportunity to increase prices, while a lack of activity on a specific category or product may suggest that a price drop could help reignite interest. With Machine Learning, ongoing monitoring and adjustments can happen automatically in a timely way that’s driven by changes in the market.

4. Fraud detection

Across industries, detecting and preventing fraud is a top concern. However, relying simply on human audits and manual processes leaves a lot of potential room for error. By incorporating Machine Learning, it’s possible to use data anomalies to signal situations that require a closer look.

For example, a surge in spending on a credit card that’s out of line with a buyer’s typical behavior might simply mean they’re making a large purchase. Or it might mean that a credit card has been lost or stolen and the stakeholder hasn’t yet identified the situation. Fraud detection can happen at every level, from tracking employee behaviors to customer transactions.

5. Image and video recognition

Certain types of data, such as image and video, are becoming more ubiquitous across the business landscape. Whether the data is posted on social media or it’s data that field service technicians collect while working with customers, the ability to recognize and categorize what's occurring in an image or video is critical.

Machine learning powers advanced recognition that can automate this process and help quickly categorize unstructured images or video to incorporate into your company's larger data set. It can also be used to identify anomalies. For example, some companies utilize Machine Learning to monitor images or video for security violations. Others leverage ML to scan photographs of store layouts to ensure that retailers are complying with CPG manufacturers marketing agreements.

Implementing Machine Learning

Today's leading organizations use Machine Learning-based tools to automate decision-making processes and are beginning to experiment with more advanced uses of Artificial Intelligence (AI) for Digital Transformation. However, there are several challenges that occur when implementing Machine Learning. These include factors such as limited familiarity with technology and process flows which can prevent the algorithm from being used, or the business may not have appropriate access to the predictions long enough to be functional for the relevant areas or leaders who require it to make decisions. Also, it is difficult to allocate the correct amount of time and money to ensure that it works as expected.

Thankfully, there are experienced partners that you can lean on in these difficult situations. SoftwareOne regularly works with customers that are implementing Machine Learning programs. With decades of combined experience with implementing Machine Learning solutions, our experts assess all aspects of the program, from use case identification, model design and deployment of the solution. 

Whether you need help defining business problems, determining the data sources required for a Machine Learning model, determining the most relevant scientific data definition stages to train the models, or provide the skills for continuous refinement, our team is experienced in all aspects of Machine Learning, from conceptualization to implementation. In addition to the theoretical aspects of ML programs, our team can provide data wrangling, testing, programming, hardware implementation, scaling, and training. By combining these factors, your team will have what it needs to get the most out of Machine Learning best practices.