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The Advantages of Machine Learning

17.1.2017

Machine learning is a form of data analysis that automates analytical model building. Using algorithms that continuously assess and learn from data, machine learning enables computers to access hidden insights. However, discoveries occur without programming systems to explicitly look for these digital treasures. 

This technology is now a crucial aspect for several burgeoning and established industries. If you want to read up on the basics of machine learning, find last week's blog post "Machine learning, explained!" here.

 

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Data Input from Unlimited Resources

Machine learning can easily consume unlimited amounts of data with timely analysis and assessment. This method helps review and adjusts your message based on recent customer interactions and behaviors. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. This prevents complicated integrations, while focusing only on precise and concise data feeds.

Fast Processing and Real-Time Predictions

Machine learning algorithms tend to operate at expedited levels. In fact, the speed at which machine learning consumes data allows it to tap into burgeoning trends and produce real-time data and predictions. For example, machine learning can optimize and create new offers for grocery and department store customers. This means that what customers might see at 1 p.m. may be different than what they see at 2 p.m.

In a nutshell, machine learning has the ability to identify, process and create data based on the following predictive analytics:

  • Churn analysis is what one uses to find which customers most probably will leave.
  • Customer leads, conversion and revenue rates, buying and spending patterns.
  • Customer defections to other brands - using recent data to identify your brand fallacies and product or service susceptibilities.

Practical Scenarios

Applying machine learning to practical applications and scenarios is simply vital. While predictive analytics are instrumental in saving costs and building revenue - it is equally as important to understand their impacts on real-life situations pertaining to customer acquisitions or loss. No matter your business niche or market, the following tips will help you deal with these scenarios in a practical and engaging manner:

1. Churn analysis - it is imperative to detect which customers will soon abandon your brand or business. Not only should you know them in depth - but you must have the answers for questions like "Who are they? How do they behave? Why are They Leaving and What Can I do to keep them with us?"
2. Customer leads and conversion - you must understand the potential loss or gain of any and all customers. In fact, redirect your priorities and distribute business efforts and resources to prevent losses and refortify gains. A great way to do this is by reiterating the value of customers in direct correspondence or via web and mail-based campaigns.
3. Customer defections - make sure to have personalized retention plans in place to reduce or avoid customer migration. This helps increase reaction times, along with anticipating any non-related defections or leaves.

 

Already up to date with Machine Learning? Watch this webinar about Machine Learning with Microsoft Azure

 

Machine Learning in the Medical Industry

Many hospitals use this data analysis technique to predict admissions rates. Physicians are also able to predict how long patients with fatal diseases can live. Similarly, medical systems are incorporating these technologies for cost-cutting measures, along with streamlining and centralizing expense reports and testing protocols. Experts even believe that radiologists will one day be replaced by computer algorithms that continuously churn and process data.

Machine Learning in the Insurance Industry

Insurance agencies across the world are also able to do the following:

  • Predict the types of insurance and coverage plans new customers will purchase.
  • Predict existing policy updates, coverage changes and the forms of insurance (such as health, life, property, flooding) that will most likely be dominant.
  • Predict fraudulent insurance claim volumes while establishing new solutions based on actual and artificial intelligence.

Other Advantages of Machine Learning

Machine learning is proactive and specifically designed for "action and reaction" industries. In fact, systems are able to quickly act upon the outputs of machine learning - making your marketing message more effective across the board. For example, newly obtained data may propel businesses to present new offers for specific or geo-based customers. However, data can also signify cutting back on unnecessary offers if these customers do not require them for conversion purposes.

The latter may even be a form of learning from past behaviors. Machine learning models are able to learn from past predictions, outcomes and even mistakes. This enables them to continuously improve predictions based on new incoming and different data.

The Microsoft Azure Experience

As an industry leader in data analytics, Microsoft Azure is the ultimate solution for companies wishing to tap into machine learning algorithms. This innovative, cutting-edge technology is now the driving force behind countless industries using cloud computing platforms for building, deploying and managing practical applications.

If you would like to learn more about the advantages of machine learning in Microsoft Azure, and the opportunities it gives, see our webinar!


Sources:

1. https://www.webtrends.com/blog/2016/03/four-benefits-of-machine-learning-for-marketing/
2. http://blogs.lexisnexis.com/insurance-insights/2016/06/machine-learning-artificial-intelligence-insurance/
3. https://www.statnews.com/2016/10/03/machine-learning-medicine-health/

Topics: Azure, machine learning, Advanced Analytics & BI



Emir Lejlic

Data Analyst

Emir is Innofactor's Data Analyst working mainly with machine learning and analytics using Microsoft Cloud technology. He is proficient with the programming languages R and Python and works with big data frameworks such as Hadoop, Spark and R Server. Prior to joining Innofactor he worked within the offshore industry with statistical computing of environmental loads and analyzing large datasets from advanced structural models.