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How To Increase Business Efficiency With Machine Learning

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Want to go global? Machine learning helps in improving business operations and enhances business scalability for global enterprises. Surfing the Internet...reading online weather reports…using speech recognition, and driving a car using GPS navigation...are some of the benefits of machine learning that have already become a part of our daily lives.  

Today, artificial intelligence tools and machine learning algorithms have become popular in the field of business analytics; industry experts believe it deserves such attention. 

The term machine learning refers to enabling computers with the ability to draw observations from the collected data; you do not need explicit programming for this process.

Machine learning helps in extracting meaningful information from raw data; it is an excellent technique of solving complex-data-rich business problems that are not resolved by the traditional approaches like human judgment or software engineering.  

Alexander Linden, research vice president at Gartner, states: “Ten years ago, we struggled to find 10 machine learning based business applications. Now we struggle to find 10 that don’t use it.”

Top 5 Industry Applications of Machine Learning

In their blog dated 4th Jun 2016, Forbes has given an in-depth analysis of how machine learning is redefining enterprises. The image below shows the impact of machine learning across different industries. 

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Industry expert Gartner identified the following ways in which enterprises are applying machine learning to optimize their business operationsSome of the major enterprise functions that have been optimized using machine learning include:

  • Sales and marketing – You can use algorithms and machine learning models for predicting which product a customer would like to buy. The inputs include customer profile such as customer activity, recent purchases, as well as personal details.
    Enterprises can map this information to predict the likelihood of customer response to a specific product/service.
     
  • Disaster management – Implement machine learning algorithms in alarm systems in a hazardous environment such as a chemical manufacturing industry. Such algorithms analyze sensor data that includes measurement of air quality, equipment performance, employee productivity, or unusual behavior; this information can help you avoid accidents.
     
  • Risk and fraud management – The scope of machine learning in fraud detection is  in identifying descriptions of transactions to their likelihood, and indicating whether an ongoing transaction can be fraudulent or not.

    This technique helps in identifying credit risk, where you can learn about the demographics and payment history details of a loan applicant; this information will assure the financial institution about the likelihood of the applicant defaulting on the given loan.
  • Efficient transportation – Optimizing traffic situations is one of the benefits of applying machine learning tools. You can understand the usual traffic patterns through comprehensive analysis of sensor data, accident history, and roadworks.
    Machine learning tools predict delays, traffic jams, or road blocks and in addition, suggest a faster alternative route to public buses, commercial and consumer vehicles.   
     
  • Improves Supply chain – Machine learning plays a vital role in asset performance management to monitor the operating conditions of assets and predict possible failures well in advance. The main objective is to reduce overall maintenance costs and minimize downtime.

Predictive Analytics - A Game Changer
Today, predictive analytics has become easier and more relevant for enterprises to maintain a competitive edge. Big data technologies and modern tools of computing have helped enterprises gain access to efficient and accurate predictive analytics models.

Why predictive analytics? Because it will help businesses to win, retain, and serve customers better than other industry players. 

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Enterprises should gain predictive powers in three key areas:

1. Providing direct insights about customers and processes. Digital dashboards and analytical reporting tools are commonly used by global enterprises. 

For causative trends and future projections, many traditional BI tools come with simple predictive models. These tools provide essential information to business leaders, but often are not implemented for improving business decisions, process optimization, customer experience, or any other activity based on these insights.

2. Intelligent, adaptable customer interactions. If you aren’t using predictive analytics, you will lag behind in the digital transformation race.

Today’s top predictive analytics tools can deploy their models or scoring engines into applications when you are looking for insights. Enterprises use predictive models to enhance business processes by detecting fraud during a PoS (point of sale) sale, and automatically adjust digital content based on user context, or initiating proactive customer service for at-risk revenue sources.

3. Redefining customer engagement. The potential of predictive analytics goes beyond the mainstream uses. Model building and deployment continues to accelerate, thus helping developers to utilize insights quickly and with increasing ubiquity in deployed applications.

Along with using app data, developers are now able to focus on features and bugs predicting greatest customer value; in addition, they can anticipate the impact of new app functionalities.

Machine Learning for B2B Enterprises

A recent survey by Gartner concluded that 75% companies are either investing now or planning to invest in analytics and big data solutions in two years. Due to this increased interest level, analysts speculate that in a short while big data initiatives will amount up to $242 billion.

For B2B enterprises, machine learning is recommended to knowledgeable and practical decision makers and not the ones who make emotional business decisions. Moreover, it takes time to build B2B relationships thus you must make efforts to maintain it as well.

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 Machine learning helps minimize financial risks; enterprises can use machine learning tools to increase revenue from existing clients. The combination of real-time data and automated business processes is the ideal solution for addressing complex and disparate data including huge data sets comprising several variables; machine learning helps enterprises in resolving complex data problems.

Tremendous opportunity lies ahead for enterprises to apply machine learning techniques in order to discover valuable insights that will improve their decision-making processes. Today, practical applications of big data and machine learning are abundant.

Examples

  • Financial institutions will be able to detect frauds quickly
  • Utilities will be able to predict systematic failures and take necessary steps for prescriptive maintenance
  • Retailers will be able to reduce customer turnover as they will become highly accurate in anticipating customer purchases

With the growing significance of machine learning in future, enterprises will have to understand how it can affect their business outcomes.

To improve business efficiency, you must incorporate feedback analysis as a core process of business operations. Regular feedback is crucial; whether it is from your sales team or from the customer. You cannot follow an annual or bi-annual approach in this case. Knowledge about major problems will keep your system in check.

To improve accuracy rate, you must find pain points of your customers, attend to their inquiries and always allow respondents to give their explanations first. This process will help provide desired outcomes.

How cloud enhances machine learning

Cloud computing holds much promise for developing and leveraging machine learning capabilities for enterprises. Developing a true cloud-based machine learning environment will require organizations to process huge amounts of data; analytics engines of that magnitude may be expensive to develop and maintain in-house.

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Google Cloud Machine Learning Platform performs photo and voice recognition along with email content matching; developers can access the platform now.  

Amazon Machine Learning has visual wizards, query generation tools that help users gain access through APIs to daily predictions.

Another big data analytics tool, IBM Watson, analyzes unstructured data to address customer queries, extract relevant information from all types of collected data, and reveals data insights and patterns to improve business efficiency of enterprises. 

However, any approach of machine learning is driven by its algorithms and analytics engine; enterprises must scrutinize these algorithms to avoid bias in the analysis process. The future holds good opportunities for cloud service providers; hence they must align their capabilities with enhanced security benefits. Educate your employees about latest trends of machine learning in the cloud and understand the vital role of secured data processing systems.