Five Reasons Your Business Should Adopt Machine Learning
When you misspell a word in search engine, Google immediately shows up the relevant search results on your screen, along with alternative spelling suggestion. Have you ever thought how this is happening? This case of effective web search is one of the most common applications of machine learning (ML) in our daily lives. From recommending movies in Netflix to paying someone using Amazon Pay, machine ML is influencing our personal chores. It’s no exception coming to business.
Machine learning optimizes user experience, which is at the heart of any business. As organizations try to reinvent their strategies to offer personalized customer services and generate more revenue, it’s important to understand how your business can benefit from machine learning.
How machine learning takes center-stage
Machine learning is defined as a sub-field of artificial intelligence (AI) that employs large data sets and training algorithms to give computers the ability to learn and act without being explicitly programmed. ML powered systems have the ability to automatically learn and improve from experience. McKinsey Global Institute reports that around 45% of work place activities can be automated using current technologies, out of which 80% is attributable to ML capabilities.
Here are a few factors that encourage organizations to adopt machine learning:
Rise of affordable cloud-based data storage services like Amazon Web Services, which makes it easy for business-critical applications to generate and store vast amounts of data
Availability of open source machine learning libraries like Google Tensor Flow, which enables data scientists and engineers to easily access cutting edge algorithms
Development of custom hardware and cloud-based platforms that offer the ability to run and manage machine learning applications at greater speed and lower cost
Ability of machine learning to seamlessly integrate into a wide range of applications such as Natural Language Processing, Image Recognition, business intelligence applications, learning management systems etc.
Currently, machine learning is applied across a wide range of industries such as customer service, logistics, healthcare, travel, retail, financial services, manufacturing etc. Each of these sectors apply ML technology to fulfil their unique business requirements, albeit everyone’s objective remains the same: to build data models that help organizations make better decisions without human intervention.
Five ways your business benefits from machine learning
Support real-time decision making:
Big data’s potential is growing fast, meaning that you need to match both users and providers (buyers and sellers) in real-time. Businesses simply cannot proceed on gut instinct, instead they should use data and analytics to make faster, context-based decisions. Personalization of services will please your customers and deliver greater returns. Machine learning enables companies to deliver real-time personalization by inferring valuable insights from data. For example, Amazon Personalize is a ML service that improves customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions.
Intelligent automation replaces manual efforts:
If the 20th century industrial automation centered around using machines to minimize predictable and repetitive human tasks, ML technology is going a step ahead to replace manual operations that involve unpredictable components such as variable parameters, external factors and internal system changes. By developing predictive data models, ML enables organizations to take decisions in real-time and automate tasks accordingly. An example is Auto Target, a powerful AI feature within Adobe Target that uses machine learning to deliver automated personalized experiences. Adobe Target’s personalization is based on a set of experiences defined by the marketer.
Reduce operating expenses of your business:
Operating costs, particularly in the case of customer support has witnessed a remarkable reduction with machine learning. Employing a large number of customer support staff and paying excessive telephone bills have given way to machine learning which optimizes speed and effectiveness of customer support. Automated customer response systems, scheduling of email responses and social media posts, introduction of chat bots etc. can guide the customers to the right information automatically, at a much lower cost.
Effectively address security and network challenges:
Very often, cyber-attacks, network intrusions, and other security glitches come unwarned in real-time, leaving little time to act. The 2018 series of DDoS attacks that hit GitHub and Arbor Networks in the US was one of the largest cyber-attacks in history. It’s important for organizations to proactively identify and prevent any kind of network intrusion before it escalates into service outages and data leaks. Machine learning algorithms are capable of monitoring network behavior to detect anomalies in real-time so that preemptive measures are automatically executed. Moreover, the state of cyber-security improves continuously as ML algorithms self-learn and adapt to change by replacing manual research and analysis.
Redesign your business model and services:
It’s a known fact that when top brands compete to dominate the market share, small and medium businesses concentrate on specific domains to remain on the competitive edge and sustain their profitability. What differentiates SMBs are usually innovative products, services or business models. By leveraging machine learning, many small and medium businesses have realized pioneering business models with striking features such as high level personalization, collaborative ecosystem, agility, adaptability, cost and asset sharing etc. Healx, a UK based company formed with a mission to accelerate treatment for rare diseases using existing drugs has developed a machine-learning algorithm that uses a patient’s biological information to predict which drug is more effective for a particular patient. This kind of differentiation made possible by ML empowers organizations to add more personalization and agility into their business.
It’s all about data efficacy
The performance of a machine learning system depends on the volume, depth and quality of data on which it is trained. Organizations should focus their questions to generate and collect well designed data. This can be achieved by understanding your core business challenges and matching them against the key capabilities of machine learning. With right data and right technology, you can build better business models that drive personalization.