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Posts from the ‘Machine learning’ Category

Machine Learning Is The New Proving Ground For Competitive Advantage

  • 50% of organizations are planning to use machine learning to better understand customers in 2017.
  • 48% are planning to use machine learning to gain greater competitive advantage.
  • Top future applications of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).

These and many other insights are from a recent survey completed by MIT Technology Review Custom and Google Cloud, Machine Learning: The New Proving Ground for Competitive Advantage (PDF, no opt-in, 10 pp.). Three hundred and seventy-five qualified respondents participated in the study, representing a variety of industries, with the majority being from technology-related organizations (43%). Business services (13%) and financial services (10%) respondents are also included in the study.  Please see page 2 of the study for additional details on the methodology.

Key insights include the following:

  • 50% of those adopting machine learning are seeking more extensive data analysis and insights into how they can improve their core businesses. 46% are seeking greater competitive advantage, and 45% are looking for faster data analysis and speed of insight. 44% are looking at how they can use machine learning to gain enhanced R&D capabilities leading to next-generation products.
If your organization is currently using ML, what are you seeking to gain?*

If your organization is currently using ML, what are you seeking to gain?

  • In organizations now using machine learning, 45% have gained more extensive data analysis and insights. Just over a third (35%) have attained faster data analysis and increased the speed of insight, in addition to enhancing R&D capabilities for next-generation products. The following graphic compares the benefits organizations who have adopted machine learning have gained. One of the primary factors enabling machine learning’s full potential is service oriented frameworks that are synchronous by design, consuming data in real-time without having to move data. enosiX is quickly emerging as a leader in this area, specializing in synchronous real-time Salesforce and SAP integration that enables companies to gain greater insights, intelligence, and deliver measurable results.
your organization is currently using machine learning, what have you actually gained?

If your organization is currently using machine learning, what have you actually gained?

  • 26% of organizations adopting machine learning are committing more than 15% of their budgets to initiatives in this area. 79% of all organizations interviewed are investing in machine learning initiatives today. The following graphic shows the distribution of IT budgets allocated to machine learning during the study’s timeframe of late 2016 and 2017 planning.
What part of your IT budget for 2017 is earmarked for machine learning?

What part of your IT budget for 2017 is earmarked for machine learning? 

  • Half of the organizations (50%) planning to use machine learning to better understand customers in 2017. 48% are adopting machine learning to gain a greater competitive advantage, and 45% are looking to gain more extensive data analysis and data insights. The following graphic compares the benefits organizations adopting machine learning are seeking now.
If your organization is planning to use machine learning, what benefits are you seeking?

If your organization is planning to use machine learning, what benefits are you seeking?

  • Natural language processing (NLP) (49%), text classification and mining(47%), emotion/behavior analysis (47%) and image recognition, classification, and tagging (43%) are the top four projects where machine learning is in use today.  Additional projects now underway include recommendations (42%), personalization (41%), data security (40%), risk analysis (41%), online search (41%) and localization and mapping (39%). Top future uses of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).
  • 60% of respondents have already implemented a machine learning strategy and committed to ongoing investment in initiatives. 18% have planned to implement a machine learning strategy in the next 12 to 24 months. Of the 60% of respondent companies who have implemented machine learning initiatives, 33% are in the early stages of their strategies, testing use cases. 28% consider their machine learning strategies as mature with between one and five use cases or initiatives ongoing today.

5 Ways Integration Is Enabling The Factory Of The Future

  • factory-of-the-future-report93% of global product leaders say that predictive maintenance combined with real-time equipment monitoring enabled by integration is a must-have for factory planning today.
  • 75% of global product leaders plan to implement factory of the future initiatives and programs in the next five years or less, starting with Industry 4.0
  • 67% of automotive executives expect that new technologies enabled by real-time integration will enable their teams to reach and exceed lean management and continuous improvement goals starting this year and accelerating through 2030.

Boston Consulting Group’s recent article, The Factory of the Future provides insights into a recent global survey the consulting firm conducted of more than 750 manufacturing product leaders from leading companies in three industrial sectors: automotive (which includes suppliers and original equipment manufacturers, or OEMs), engineered products, and process industries. The survey’s objective is to define the vision for the factory of the future in 2030.  Determining long-term benefits and the roadmap to implementation are also goals of the study Boston Consulting Group (BCG) and its research partner, the Laboratory for Machine Tools and Production Engineering at RWTH Aachen University, achieved. The Factory of the Future is a vision for how manufacturers should enhance production by making improvements in three dimensions: plant structure, plant digitization, and plant processes.

5 Ways Integration Fuels The Factory Of The Future’s Growth

Real-time integration based on intelligent objects that connect diverse enterprise systems including SAP, Salesforce and others is the foundation that manufacturing companies must adopt to excel in their Factory of the Future efforts. These real-time objects illustrate the future of Application Programmer Interfaces (API).  APIs that will fuel and drive the Factory of the Future will enrich each real-time integration points across manufacturing networks. Intelligent Objects pervasively used today are the precursors to the most valuable APIs that will enable Factories of the Future tomorrow. With APIs continually improving and gaining the capability to provide insight and intelligence, the essential role of real-time integration in all factories of the future becomes clear.

The following are the five ways integration is enabling the Factory of the Future today:

  1. Real-time integration enables the value chains supporting the Factories of the Future to continually accelerate, excel and improve with additional insight that drives future growth strategies. Bringing greater intelligence into each integration point across the value chains supporting the Factories of the Future leads to new technologies delivering greater lean management benefits. Real-time integration will deliver strong benefits in the areas of lean management, predictive maintenance, modular line setups, and the orchestration and collaboration of smart robots.


  1. The Implementation Roadmap for the Factory of the Future shows how critical real-time integration is to the Factory of the Future’s vision being attained. Multidirectional layouts, modular line setups, sustainable production, the orchestration of smart and collaborative robotics and attainment of big data and analytics plans all are dependent on real-time integration. The following graphic from the study illustrates just how central integration is to the optimizing of plant structure and plant digitization.


  1. By integrating large-scale enterprise systems including those from SAP, Salesforce and others with legacy, 3rd party and homegrown systems, every area of production quality will improve. The most urgent need global manufacturers have is finding new ways to improve product, process and service quality without raising costs. Improving the quality of these three dimensions makes any manufacturer more trusted and successful in selling next-generation products.  By aggregating data using real-time integration so that Big Data and advanced analytics can be used to find new patterns, some of the world’s most well-known manufacturers are excelling on product quality. To produce cylinder heads at its plant in Untertürkheim, Germany, Mercedes-Benz uses predictive analytics to examine more than 600 parameters that influence quality. Mercedes-Benz is an early adopter of using Big Data and advanced analytics to improve quality management and bring high precision to engineering. Bosch has implemented software that analyzes data about its production of fuel injectors in real time. The software monitors process adherence and recognizes trends. It automatically transmits information about deviations to operators, allowing them to improve the process accordingly.
  1. Real-time integration across and within manufacturing systems enables multi-directional layouts of production workflows. The Audi R8 manufacturing facility in Heilbronn, Germany, does not have a fixed conveyor so the teams there has greater multidirectional flexibility in building customized vehicles.  Real-time integration across the Audi factory floor is essential to provide R8 production teams with the specifics of how they can best collaborate and deliver the highest quality vehicles in the shortest amount of time. Real-time integration is enabling driverless transport systems, guided by a laser scanner and radio frequency identification technology in the floor, which moves the car bodies through the assembly process. These systems enable assembly layout changes quickly with no impact on existing production. Enabling real-time integration often involves extensive field mapping between different systems, which is a lengthy and error-prone process. Integration technology provider enosiX has developed a unique, real-time integration technology that obsoletes the need for field mapping and supports bi-directional data updates.
  1. Enabling the Factory of the Future’s production operations to flex in response to rapidly changing customer requirements is entirely dependent on real-time, reliable integration of production and customer-facing systems. The implications of the study on the future of manufacturing underscore just how critical it is for manufacturers to be agile enough to create entirely new business models while gaining insight and intelligence into how they can continually improve lean manufacturing. When real-time integration unifies a value chain for any manufacturer, their speed, scale and ability to simplify the complex processes required to serve customers turns into a formidable competitive advantage.


McKinsey’s 2016 Analytics Study Defines The Future Of Machine Learning

  • U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years.
  • Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.
  • 40% of all the potential value associated with the Internet of Things requires interoperability between IoT systems.

These and many other insights are from the McKinsey Global Institute’s study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month. You can get a copy of the Executive Summary here (28 pp., free, no opt-in, PDF) and the full report (136 pp., free, no opt-in, PDF) here. Five years ago the McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity (156 pp., free no opt-in, PDF), and in the years since McKinsey sees data science adoption and value accelerate, specifically in the areas of machine learning and deep learning. The study underscores how critical integration is for gaining greater value from data and analytics.

Key takeaways from the study include the following:McKinsey Analytics

  • Location-based services and U.S. retail are showing the greatest progress capturing value from data and analytics. Location-based services are capturing up to 60% of data and analytics value today predicted by McKinsey in their 2011 report. McKinsey predicts there are growing opportunities for businesses to use geospatial data to track assets, teams, and customers across dispersed locations to generate new insights and improve efficiency. U.S. Retail is capturing up to 40%, and Manufacturing, 30%.  The following graphic compares the potential impact as predicted in McKinsey’s 2011 study with the value captured by segment today, including a definition of major barriers to adoption.


  • Machine learning’s greatest potential across industries includes improving forecasting and predictive analytics. McKinsey analyzed the 120 use cases their research found as most significant in machine learning and then weighted them based on respondents’ mention of each. The result is a heat map of machine learning’s greatest potential impact across industries and use case types.  Please see the report for detailed scorecards of each industry’s use case ranked by impact and data richness.


  • Machine learning’s potential to deliver real-time optimization across industries is just starting to evolve and will quickly accelerate in the next three years. McKinsey analyzed the data richness associated with each of the 300 machine learning use cases, defining this attribute as a combination of data volume and variety. Please see page 105 of the study for a thorough explanation of McKinsey’s definition of data volume and variety used in the context of this study The result of evaluating machine learning’s data richness by industry is shown in the following heat map:


  • Enabling autonomous vehicles and personalizing advertising are two of the highest opportunity use cases for machine learning today. Additional use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics; predicting personalized health outcomes, and optimizing merchandising strategy in retail. McKinsey identified 120 potential use cases of machine learning in 12 industries and surveyed more than 600 industry experts on their potential impact. They found an extraordinary breadth of potential applications for machine learning.  Each of the use cases was identified as being one of the top three in an industry by at least one expert in that industry. McKinsey plotted the top 120 use cases below, with the y-axis shows the volume of available data (encompassing its breadth and frequency), while the x-axis shows the potential impact, based on surveys of more than 600 industry experts. The size of the bubble reflects the diversity of the available data sources.


  • Designing an appropriate organizational structure to support data and analytics activities (45%), Ensuring senior management involvement (42%), and designing effective data architecture and technology infrastructure (36%) are the three most significant challenges to attaining data and analytics objectives. McKinsey found that the barriers break into the three categories: strategy, leadership, and talent; organizational structure and processes; and technology infrastructure. Approximately half of executives across geographies and industries reported greater difficulty recruiting analytical talent than any other kind of talent. 40% say retention is also an issue.


  • U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising including pricing, assortment, and placement optimization is leading to an additional 16% in operating margin improvement. The following table illustrates data and analytics’ contribution to U.S. retail operations by area.


  • Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing. McKinsey estimates that analytics have increased manufacturer’s gross margins by as much as 40% when used in design-to-value workflows and projects. Up to 15% of after-sales costs have been reduced through the use of analytics that includes product sensor data analysis for after-sales service. There are several interesting companies to watch in this area, with two of the most innovative being Sight Machine and enosiX, with the latter enabling real-time integration between SAP and Salesforce systems. The following graphic illustrates the estimated impact of analytics on manufacturing financial performance by area.


10 Ways Machine Learning Is Revolutionizing Manufacturing

machine learningBottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.

Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

The ten ways machine learning is revolutionizing manufacturing include the following:

  • Increasing production capacity up to 20% while lowering material consumption rates by 4%. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric and cited in a National Institute of Standards (NIST) provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.

typical production improvemensSource: Focus Group: Big Data Analytics for Smart Manufacturing Systems

  • Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints. In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible.

factory and demand analytics

Source:  GE Global Research Stifel 2015 Industrials Conference

  • Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google, and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.

Azure IOT Services

Source: Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft

  • Enabling condition monitoring processes that provide manufacturers with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%. An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent.  By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown

OEE Graphic

Source: Using Big Data for Machine Learning Analytics in Manufacturing

  • Machine learning is revolutionizing relationship intelligence and Salesforce is quickly emerging as the leader. The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post,     Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.

Salesforce AI Acquisitions

Salesforce revenue sources

  • Revolutionizing product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide. Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
  • Increasing production yields by the optimizing of team, machine, supplier and customer requirements are already happening with machine learning. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customized products to use more of their production capacity, and machine learning help to optimize the best possible selection of machines, trained staffs, and suppliers.
  • The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
  • Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale.  For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
  • Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning.   Machine learning is extending what enterprise-level price optimization apps provide today.  One of the most significant differences is going to be just how optimizing pricing along with suggested strategies to close deals accelerate sales cycles.

Additional reading:

Cisco Blog: Deus Ex Machina: Machine Learning Acts to Create New Business Outcomes

Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft 

Focus Group: Big Data Analytics for Smart Manufacturing Systems

GE Predix: The Industrial Internet Platform

IDC Manufacturing Insights reprint courtesy of Cisco: Designing and Implementing the Factory of the Future at Mahindra Vehicle Manufacturers

Machine Learning: What It Is And Why It Matters

McKinsey & Company, An Executive’s Guide to Machine Learning

MIT Sloan Management Review, Sales Gets a Machine-Learning Makeover

Stanford University CS 229 Machine Learning Course Materials
The Economist Feature On Machine Learning

UC Berkeley CS 194-10, Fall 2011: Introduction to Machine Learning
Lecture slides, notes

University of Washington CSE 446 – Machine Learning – Winter 2014


Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.

Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.

Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).

Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.

Internet of Things, Machine Learning & Robotics Are High Priorities For Developers In 2016

  • 200213603-00156.4% of developers are building robotics apps today.
  • 45% of developers say that Internet of Things (IoT) development is critical to their overall digital strategy.
  • 27.4% of all developers are building apps in the cloud today.
  • 24.7% are using machine learning for development projects.

These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. 1,441 developers completed the survey globally. Please see page 17 of the study for additional details on the methodology.

Key takeaways from the study include the following:

  • Big Data analytics developers are spending the majority of their time creating Internet of Things (IoT).  The second-most popular Big Data analytics applications are in professional, scientific and technical services (10%), telecommunications (10%), and manufacturing (non-computer related) (9.6%). The following graphic provides an overview of where Big Data analytics developers are investing their time building new applications.

Best Describes App

  • Robotics (56.4%), Arts, Entertainment and Recreation (56.3%), and Automotive (52.9%) are the three most popular industries data mining app developers are focusing on today. Additional high priority industries include telecommunications (48.3%), Internet of Things (47.1%) and manufacturing (46.7%). A graphic from the study is shown below for reference.

Data Mining adoption

  • Nearly one-third (27.4%) of all app developers globally are planning to build new apps on the cloud. 66.9% expect to have a new cloud app within 12 months. Overall, 81.3% of all developers surveyed are building cloud apps today. The following graphic compares developers’ predicted timeframes for cloud app development over the next two years.

Plans for Apps In the Clouds

  • Better security (51.9%), more reliability (42%) and better user experience (41%) are the top three areas that motivate developers to move to new cloud platforms. Additional considerations include a better breadth of services (39.4%), networking and data center speed (37.8%), better pricing options (37.5%), better licensing structures (34.6%) and completeness of vision (30.9%). The following graphic compares the key factors that most motivate developers to switch cloud platforms.

key factors

  • 45% of developers say that Internet of Things (IoT) development is very important to their overall digital strategy. 7% say that IoT is somewhat important to their digital strategy. The study also found that 29.5% of all developers are creating Internet of Things (IoT) apps today. The following graphic illustrates the relative level of importance of IoT to developers’ digital strategies.

importance of IoT strategy

  • 41% say that cognitive computing and artificial intelligence (AI) are very important to their digital strategies. In speaking with senior executives at services firms, the opportunity to provide artificial intelligence-based services using a subscription model is gaining momentum, with many beginning to fund development projects to accomplish this on a global scale.

AI Importance

  • Most frequently created machine learning apps include those for the Internet of Things (11.4%), Professional, Scientific and Technical Services (10%), and Manufacturing (9.4%) industries.  Additional industries include telecommunications (8.3%), utilities/energy (8.1%), robotics (7.2%) and finance or insurance (6.8%). The following graphic breaks out the industries where machine learning app development is happening today.

Machine learning industries final

  • The majority of developers (84.2%) say that analytics is important for enabling their organizations to operate today. Of that group, 45.7% say that analytics are very important for their organizations to attain their goals.

Machine Learning Is Redefining The Enterprise In 2016

machine learning imageBottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before.

Industries Where Machine Learning Is Making An Impact  

The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry.

machine learning industries

Source: Tata Consultancy Services, Using Big Data for Machine Learning Analytics in Manufacturing – TCS

Machine Learning Is Revolutionizing Sales and Marketing  

Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets. And increasingly data sets are comprised of structured and unstructured data, with the global proliferation of social networks fueling the growth of the latter type of data.  Machine learning is proving to be efficient at handling predictive tasks including defining which behaviors have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first.  In the MIT Sloan Management Review article, Sales Gets a Machine-Learning Makeover the Accenture Institute for High Performance shared the results of a recent survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning. Key takeaways from their study results include the following:

  • 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.
  • At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
  • 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
  • Several European banks are increasing new product sales by 10% while reducing churn 20%. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.

Why Machine Learning Adoption Is Accelerating

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes.  Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.
The economics of cloud computing, cloud storage, the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the several factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complicated problems companies face in optimizing operations while predicting most likely outcomes, and the perfect conditions exist for machine learning to proliferate.
The following are the key factors enabling machine learning growth today:

  • Exponential data growth with unstructured data being over 80% of the data an enterprise relies on to make decisions daily. Demand forecasts, CRM and ERP transaction data, transportation costs, barcode and inventory management data, historical pricing, service and support costs and accounting standard costing are just a few of the many sources of structured data enterprises make decisions with today.   The exponential growth of unstructured data that includes social media, e-mail records, call logs, customer service and support records, Internet of Things sensing data, competitor and partner pricing and supply chain tracking data frequently has predictive patterns enterprises are completely missing out on today. Enterprises looking to become competitive leaders are going after the insights in these unstructured data sources and turning them into a competitive advantage with machine learning.
  • The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimizing supply chain networks and increasing demand forecast predictive As IoT platforms, systems, applications and sensors permeate value chains of businesses globally, there is an exponential growth of data generated. The availability and intrinsic value of these large-scale datasets are an impetus further driving machine learning adoption.
  • Generating massive data sets through synthetic means including extrapolation and projection of existing historical data to create realistic simulated data. From weather forecasting to optimizing a supply chain network using advanced simulation techniques that generate terabytes of data, the ability to fine-tune forecasts and attain greater optimizing is also driving machine learning adoption. Simulated data sets of product launch and selling strategies is a nascent application today and one that shows promise in developing propensity models that predict purchase levels.
  • The economics of digital storage and cloud computing are combining to put infrastructure costs into freefall, making machine learning more affordable for all businesses. Online storage and public cloud instances can be purchased literally in minutes online with a credit card. Migrating legacy data off of databases where their accessibility is limited compared to cloud platforms is becoming more commonplace as greatest trust in secure cloud storage increases. For many small businesses who lack IT departments, the Cloud provides a scalable, secure platform for managing their data across diverse geographic locations.

Further reading

Companies Are Reimagining Business Processes with Algorithms. Harvard Business Review. February 8, 2016.  H. James Wilson, Allan Alter, Prashant Shukla. Source:

Domingos, P. (2012). A Few Useful Things to Know About Machine Learning. Communications Of The ACM, 55(10), 78-87.

Pyle, D., & San José, C. (2015). An executive’s guide to machine learning. Mckinsey Quarterly, (3), 44-53. Link:

Sales Gets A Machine-Learning Makeover.  MIT Sloan Management Review, May 17, 2016. H. James Wilson, Narendra Mulani, Allan Alter. Source:, M. (2014).

The Next Wave Of Enterprise Software Powered By Machine Learning.  TechCrunch, July 27, 2015.

What Every Manager Should Know About Machine Learning, Harvard Business Review,  July 7, 2015.  Link:

What Is Machine Learning? Making The Complex Simple.  Mike Ferguson.  IBM Big Data & Analytics Hub. Link:

World Economic Forum White Paper Digital Transformation of Industries: In collaboration with Accenture Digital Enterprise, January 2016. Link:

Yan, J., Zhang, C., Zha, H., Gong, M., Sun, C., Huang, J., & Yang, X. (2015, February). On machine learning towards predictive sales pipeline analytics. In Twenty-Ninth AAAI Conference on Artificial Intelligence.  Link:

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