Machine Learning Market Research: How Leading Industries Are Adopting AI

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Meet The Authors

Anna Räder                  

Anna, Senior Consultant at M-Brain Toronto, is responsible
for new business development in California as well as managing and
executing strategic analysis and advisory projects.

Email:        anna.rader@m-brain.com
Twitter:     https://twitter.com/AniaRader
LinkedIn:  linkedin.com/in/annarader/
Google+:   https://plus.google.com/+AnnaRaderProfile

 

Irida Jano

Irida, Consultant at M-Brain Toronto, manages and
executes strategic analysis and advisory projects.

Email:       irida.jano@m-brain.com
LinkedIn: linkedin.com/in/irida-jano-1a355b48

 

 

 

Across industries companies are applying machine learning primarily for process optimization, M-Brain research says.

Research by M-Brain suggests that process optimization is the most popular application of machine learning among companies across verticals including financial services, high tech, retail, manufacturing, and healthcare.

M-Brain identified and analyzed 60 machine learning application cases published by vendors of machine learning tools to understand the use and application of machine learning by industry. The analysis focused on five market verticals: Financial service, high tech, retail, manufacturing, and healthcare. This article presents a summary of key findings from M-Brain’s research.

Exhibit 1. Most Common Machine Learning Use Cases by Vertical

Exhibit 2. Top Vendors Providing Machine Learning Solutions by Vertical

 

FINANCIAL SERVICE

Finance Header With Icon

 

Financial service industry players see key benefits of machine learning in increased accuracy of fraud detection.

Financial service companies, including large global and US financial institutions, insurance companies, payment providers, and global financial data vendors, benefit from machine learning projects by being able to:

• Increase the detection of fraud and reduce the number of false alarms;
• Decrease costs and increase savings (e.g. time).

In addition to fraud prevention and process optimization, key priority areas for implementing machine learning programs include risk calculation and mitigation, analysis of financial data, such as credit card information and other data that may aid in making financial decisions, such as credit decisions, as well as improving customer experience.

A typical customer journey for financial services players planning to implement machine learning starts with realizing that this technology can improve the efficiency of business operations by advancing data analysis capabilities and driving automated decisions.

There is a combination of vendors providing machine learning solutions to this vertical. Identified vendors include AWS, IBM Watson, and Microsoft Azure, and smaller providers like Logical Glue and Work Fusion.

Examples of financial services companies analyzed in this study include: PayPal, Atom Bank, Callcredit, BuildFax, and Quarterspot.

Exhibit 3. Financial Services: Most Common Machine Learning Use Cases

Finance Machine Learning Uses

Exhibit 4. Financial Services: User Journey

Step 1

Need Realization

Companies seek out ML technologies, realizing that predictive analytics could improve the efficiency of business operations by advancing data analysis capabilities and driving automated decisions. Applying the technology would allow companies to progressively learn the most efficient way to deliver information and solutions to customers.

Step 2

Machine Learning Technology Exploration

Many companies will explore and test multiple vendors’ machine learning solutions and capabilities before committing to a final decision. Integration with existing user capabilities or systems is considered.

Step 3

Decision & Implementation

Company makes final decision and implements ML solution with vendor’s assistance. Depending on the use case, ML may be implemented in one business area and then expanded to others.

Note: While there are similarities, the user journey is not identical in each case study and may differ depending on each customer’s use case of machine learning

 

HIGH TECH

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Most high tech companies already have technological capabilities; they aim to advance their existing systems with machine learning.

Software and technology companies that use machine learning include providers of marketing analytics, content marketing, e-commerce, and data science companies. Examples include: Kristalytics, VMware, Upserve, Visual.ly. By implementing machine learning, these companies experienced:

• Immediate impact on operational efficiency, employee satisfaction,
productivity, and customer efficiency;
• Improved knowledge transfer.

High tech companies typically use machine learning for market research and analytics to provide relevant intelligence to inform strategic decisions, to streamline processes (e.g. sales), to improve customer experiences and operational efficiencies, as well as to improve employee engagement and satisfaction.

A typical customer journey includes exploring different types of technologies and considering other options such as hiring a data scientist and building in-house models.

While there are a few small vendors like Wise.io and Alpine Data providing machine learning solutions to the high tech companies, SAP HANA, AWS, and Microsoft Azure dominate the industry.

Exhibit 5. High Tech: Most Common Machine Learning Use Cases

Exhibit 6. High Tech: User Journey

Step 1

Need Realization

Most users in this vertical already have technological capabilities, but want to advance their existing systems and implement ML to reap a variety of benefits including: Enhancing customer experience and internal productivity, streamlining processes and improving operational efficiencies, ensuring accuracy, accessibility, interpretation of data, serving workforce and customers better, etc.

Step 2

Machine Learning Technology Exploration

To find the most scalable solution, most users explored different types of technologies and considered other options such as hiring a data scientist, building in-house models, etc.

Step 3

Decision & Implementation

A cloud-based solution was deemed the best option by many. Some users chose companies they already had established relationships (trust) with (i.e. SAP and AWS). Implementation times vary; i.e. 8 weeks for GENBAND/SAP HANA, 2 weeks for Upserve/AWS.

Note: While there are similarities, the user journey is not identical in each case study and may differ depending on each customer’s use case of machine learning

 

RETAIL

RetailHeaderWithIcon

 

Machine learning helps retail companies make use of customer data that has been collected for many years.

There is a wide variety of retail companies that have implemented machine learning, from general retailers like Walmart, to online fashion, beauty, or subscription-based retailers like Asos and Youbook, to furniture retailers like Pier 1 Imports. Retail market players discovered that machine learning helps them to:

• Ensure growth and repeat business by offering a strategic solution;
• Improve customer service and experience (i.e. making informed purchase
decisions based on information and recommendations generated through machine
learning);
• Improve retail capabilities (automation, predictive);
• Improve automation and efficiency, save time and labor.

When evaluating different machine learning solution vendors, retail companies often look for technologies that could easily integrate with, complement, or make use of existing internal data and systems.

While there are a few smaller companies like Celaton and WorkFusion providing solutions to companies in this vertical, Microsoft Azure is the most common provider of machine learning solutions in retail.

Exhibit 7. Retail: Most Common Machine Learning Use Cases

Exhibit 8. Retail: User Journey

Step 1

Need Realization

Companies may have been collecting user data for years, but had not made use of it. They started to seek out ML technologies, realizing that it could have a significant impact on various business areas, including marketing, sales, inventory planning, procurement and finance, and customer service/experience.

Step 2

Machine Learning Technology Exploration

One retail company, Walmart, developed its own internal ML system, Polaris, in 10 months, improving online shopper base by 10-15%. Other companies sought out technologies which could easily integrate with, complement, or make use of existing internal data and systems.

Step 3

Decision & Implementation

Implementing ML technologies varies in each case study (i.e. 3 months for JJ Food Service/Microsoft, 12 months for The North Face/IBM Watson).

Note: While there are similarities, the user journey is not identical in each case study and may differ depending on each customer’s use case of machine learning

 

MANUFACTURING

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Manufacturing companies look for predictive analytics to improve the efficiency of their assembly lines and design better products.

Manufacturing firms that are using machine learning identified in this research include players active in appliances and tools (Bosch, Ridgid), electronics and IT hardware (Jabil, Fujitsu), aircrafts, and elevators (ThyssenKrupp). M-Brain research shows that the key benefits of implementing machine learning in the manufacturing sector include:

• Improved inventory planning and supply chain decision making;
• Faster processing of manufacturing data;
• Reduced production costs, high output, product quality improvements.

Some of the key priority areas when implementing machine learning in this vertical include applying predictive capabilities to improve assembly line efficiency, improving sustainability, enhancing product quality, preventative maintenance (by identifying problems within manufacturing process), sales and marketing including upsell and cross-sell opportunities, as well as inventory management.

It is typical for companies in manufacturing to conduct trials during the exploration phase of their customer journey when evaluating machine learning solution vendors.

As with retail, Microsoft Azure is also the dominant ML provider in manufacturing. Other vendors include GE Predix, Infosys AI, Alpine Data, Siemens, and Saffron Technology.

Exhibit 9. Manufacturing: Most Common Machine Learning Use Cases

Exhibit 10. Manufacturing: User Journey

Step 1

Need Realization

Manufacturing companies were looking for predictive analytics to improve the efficiency of their assembly lines, design better products, maximize ROI, etc. Some companies had already developed predictive maintenance models in-house, but needed a way to manage, complement, or improve these models.

Step 2

Machine Learning Technology Exploration

Some companies conducted trials during the exploration phase before committing to a specific vendor.

Step 3

Decision & Implementation

Implementation times vary depending on case study (e.g. 9 months for Rigid/Microsoft Azure).

Note: While there are similarities, the user journey is not identical in each case study and may differ depending on each customer’s use case of machine learning

 

HEALTHCARE


Machine learning aids healthcare providers in improving diagnostics and providing better treatment options.

Types of healthcare institutions that have implemented machine learning include cancer centers, occupational & health services, elderly and disabled care, medical transport, and medical benefits management solutions providers. Healthcare companies highlight the following benefits of machine learning projects:

• Improved benefits administration;
• Evidence-based diagnosis and treatment suggestions for patients;
• Real-time healthcare monitoring;
• Process improvement.

For some of the analyzed healthcare companies, the need for implementing machine learning projects arose from a growing client base. For others, it was about potential cost savings or the interest of making better use of data.

IBM Watson is the dominant provider of machine learning solutions in healthcare. Other providers include SAP HANA and Alpine Data.

Exhibit 11. Healthcare: Most Common Machine Learning Use Cases

Healthcare Machine Learning Uses

Exhibit 12. Healthcare: User Journey

Step 1

Need Realization

Organizations within healthcare realized the potential benefits of implementing machine learning technologies. For some users, the need arose from a growing client base, and for others, it was about potential cost savings or the interest of making better use of data.

Step 2

Machine Learning Technology Exploration

The key criteria for most users within healthcare when choosing a solution is the ability to process large volumes of available data with minimal IT involvement.

Step 3

Decision & Implementation

There is no indication of users covered in this study having tested multiple machine learning technologies; Implementation times are unknown based on the analysed cases.

Note: While there are similarities, the user journey is not identical in each case study and may differ depending on each customer’s use case of machine learning

 

 

Are you looking for answers to specific research questions that will help you become more competitive?

Call us at M-Brain. We can help.

 



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