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10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018

  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations is achievable with machine learning.
  • Reducing supply chain forecasting errors by 50% and lost sales by 65% with better product availability is achievable with machine learning.
  • Automating quality testing using machine learning is increasing defect detection rates up to 90%.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level.

Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

The ten ways machine learning is revolutionizing manufacturing in 2018 include the following:

  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations are is achievable with machine learning. Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimization are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

  • Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimization. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.

  • Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC. Analytics and MI-driven process and quality optimization are predicted to grow 35% and process visualization and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

  • McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

  • Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

  • Automating inventory optimization using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%. AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimization across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance. Source: Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.

  • Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

  • Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more. Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity, and related variables gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios. Source: Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

  • A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning. The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process. Source: The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

  • Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE). OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today. Source: TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Additional reading:

Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT (26 pp., PDF, no opt-in) Hitachi

Artificial Intelligence and Robotics and Their Impact on the Workplace (120 pp., PDF, no opt-in) IBA Global Employment Institute

Artificial Intelligence: The Next Digital Frontier? (80 pp., PDF, no opt-in) McKinsey and Company

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services

Connected Factory and Digital Manufacturing: A Competitive Advantage, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)

Demystifying AI, Machine Learning, and Deep Learning, DZone, AI Zone

Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses (76 pp., PDF, no opt-in) University of Cambridge

Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Get started with the Connected factory preconfigured solution, Microsoft Azure

Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Impact of the Fourth Industrial Revolution on Supply Chains (22 pp., PDF, no opt-in) World Economic Forum

Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

Machine Learning & Artificial Intelligence Presentation (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group

Machine Learning Techniques in Manufacturing Applications & Caveats, (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell

Machine learning: the power and promise of computers that learn by example (128 pp., PDF, no opt-in) Royal Society UK

Predictive maintenance and the smart factory (8 pp., PDF, no opt-in) Deloitte

Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated reviewAi Edam28(1), 83-97.

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company

Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney

The Future of Manufacturing; Making things in a changing world (52 pp., PDF, no opt-in) Deloitte University Press

The transformative potential of AI in the manufacturing industry, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017

The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.

Turning AI into concrete value: the successful implementers’ toolkit (28 pp., PDF, no opt-in) Capgemini Consulting

Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applicationsProduction & Manufacturing Research4(1), 23-45.

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10 Charts That Will Change Your Perspective Of Amazon Prime’s Growth

    • 70% of Americans with incomes of $150,000 or more who shop online have Amazon Prime memberships.
    • Amazon Prime international customers will grow at a 56% compound annual growth rate (CAGR) between 2016 to 2018.
    • Amazon shipped more than 5 billion items in 2017 with Prime worldwide.
    • By 2022 there will be 56 million Amazon Prime Video subscribers in the U.S., and 122 million worldwide.

Net Sales at Amazon reached $177.9B in 2017, a 31% increase from $136B in 2016 and Net Income increased from $2.4B in 2016 to $3B in 2017. Their fourth quarter, 2017 financial results are available here. Their latest financial results also reflect how increasing operating expenses are squeezing margins as the company builds out their fulfillment network in international markets, technology, content, and marketing efforts.

Amazon Prime is an annual membership program that includes unlimited free shipping of over 100 million items, access to unlimited instant streaming of thousands of movies and TV episodes, Alexa voice shopping, unlimited free access to thousands of Kindle books and content. Amazon Prime also includes free same day delivery on selected products, in addition to planned services Amazon is fine-tuning for launch later this year.

Revenue for online subscriptions to services like its Amazon Prime membership, Audible, Prime Video, and Prime Music Unlimited was up 49% year over year, handily outpacing the 20% year-over-year revenue growth from its online store segment. In January 2018 Amazon raised the price for Prime membership $2 to $12.99 for customers making monthly payments, totaling $156 per year. Amazon chose to leave the Prime membership price at $99 for those customers choosing to make one annual payment. Investment firm Cowen & Company estimates the $2 price increase to Prime subscribers who pay monthly will generate an additional $300M in revenue.

The following ten charts provide insights into Amazon Primes’ explosive growth:

  • 51% of U.S. households will be Amazon Prime subscribers in 2018, up from 45% in 2017 with Prime subscribers spending up to 4.6X more than non-prime customers. Morgan Stanley estimates that the average Amazon Prime customer spent $2,486 over the last twelve months compared to $544 for non-Prime Amazon customers. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • There are an estimated 90 million paying Amazon Prime subscribers in the United States today according to Consumer Intelligence Research Partners and Statista. Amazon was able to grow Prime memberships from 63 million in June 2016 to 90 million in September of last year. From just 25 million members in December 2013 to 90 million in September of last year, Amazon has been able to attain a 29.2% CAGR of subscribers over the last five years. Statista found that Amazon Prime members spend an average of $1,300 per year compared to non-Prime members who spend $700 annually. Source: Statista.   

  • 70% of Americans with incomes of $150,000 or more who shop online have Amazon Prime memberships. Alexa, Echo, Dash, IoT, Smart Home and Prime Now delivery services are predicated on attracting and retaining Prime customers who have higher disposable incomes and are willing to pay for convenience. Amazon realizes the most profitable Prime customers they have are facing a continual time shortage due to demanding jobs and travel schedules. The Prime services roadmap continues to reflect convenience and speed to serve high-income families, many of which have two wage earners, where time is at a premium. Source: Statista.

  • 46% of Amazon Prime subscribers buy something online using the benefits of their subscription at least once a week. In contrast, only 13% of non-Prime Amazon shoppers make weekly purchases. Amazon’s proliferation of services helps to keep Prime customers coming back. Combining a broad services portfolio and real-time convenience on a trusted platform, Amazon has found a way to become indispensable to customers who have high disposable incomes and little extra time. Source: Nearly Half of US Households Are Now Amazon Prime Subscribers, eMarketer Retail. January 30, 2018.

  • Amazon Prime international customers will grow at a 56% compound annual growth rate (CAGR) between 2016 to 2018, growing over two times as fast as the S. Prime customer base while expectations of shorter delivery times increase. Morgan Stanley estimates there will be 62 million U.S.-based Amazon Prime customers by the end of 2018, growing from an estimated 54 million in 2017. International Prime subscribers are projected to grow from 18 million in 2018 to 45 million in 2018. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • By 2022 there will be 56 million Amazon Prime Video subscribers alone in the U.S., and 122 million worldwide. Within four years it’s projected that Amazon Prime Video will grow its customer base globally to 122 million subscribers, with 45.9% from the U.S. alone. Amazon’s Source: Statista.

  • Amazon Prime Video is the primary growth catalyst for Amazon to gain new subscribers in Japan, Germany, and the UK. Amazon Prime membership jumped 16% in Japan in just three months following the launch of Prime Instant Video. Prime subscriber rates increased in the UK and Germany with the introduction of Prime Instant Video. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • 63% of Amazon online shopping users are also subscribers to Amazon Prime today. Gaining new Prime subscribers from existing online users have started to slow down compared to other areas of Amazon Prime growing at double-digit growth rates. Amazon’s strategy of broadening the base of services and devices including Alexa to attract new subscribers shows signs of working according to their latest financial results. Source: Statista.

  • Amazon Prime has 3.4 times the number of customers acquired Whole Foods Market has and is changing the pricing and profitability of food retailing now. Amazon is actively re-ordering the food retailing landscape by capitalizing on the scale of their operations in the supply chain, logistics and fulfillment operations. Morgan Stanley found that the primary reason customers aren’t shopping at Whole Foods Markets is the perception of lower prices elsewhere. Amazon’s selective reduction of prices at Whole Foods Markets is margin-driven today. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • Amazon is combining Prime Now 1 to 2-hour deliveries and Whole Foods Market local inventory to fuel and scale a profitable grocery delivery business. One of the most attractive benefits of Prime membership is the flexibility of ordering products for 1 to 2-hour By increasing the variety of products deliverable by the Prime Now service, Amazon is scaling its home delivery business profitably. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

Data Sources on Amazon Prime and their latest reported financial results:

Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in)

Amazon has around 80 million reasons to be excited for Prime Day, Business Insider. July 10, 2017

Amazon hikes the price of Prime monthly memberships by 18%, CNN, January 19, 2018

Amazon nipping at Netflix’s heels, IHS Markit, January 16, 2018

Amazon Prime Had A Ridiculously Good 2017, Slash Gear January 2, 2018

Amazon Prime had its best year of sign-ups ever, Quartz, Alison Griswold.

Amazon Prime Hits 90 Million US Members, Consumer Intelligence Research Partners, October 18, 2017 (PDF, 22 pp., no opt-in)

Amazon Prime’s Monthly Price Hike Will Generate $300 Million a Year, Bloomberg & Company, January 22, 2018

Don’t Overlook These Metrics From Amazon.com, Inc.’s Fourth Quarter, NASDAQ. February 10, 2018

For the wealthiest Americans, Amazon Prime has become the norm, Recode, June 8, 2017

Here’s How Much Amazon Prime Customers Spend Per Year, Fortune, October 18, 2017

Nearly Half of US Households Are Now Amazon Prime Subscribers, eMarketer Retail, January 30, 2018

Number of Amazon Prime Video subscribers worldwide in selected countries in 2022 (in millions), Statista, 2018.

Pros and Cons of Amazon Prime, Consumer Reports, February 22, 2018

Sixty-Four Percent Of U.S. Households Have Amazon Prime, Forbes, June 17, 2017

Why Amazon Bought Whole Foods, The Atlantic, June 16, 2017

10 Ways Machine Learning Is Revolutionizing Marketing

 

  • 84% of marketing organizations are implementing or expanding AI and machine learning in 2018.
  • 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.
  • 3 in 4 organizations implementing AI and machine learning increase sales of new products and services by more than 10% according to Capgemini.

Measuring marketing’s many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing.

The best marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what’s most effective for each prospect and customer. Machine learning is taking contextual content,  marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed.

The strongest marketing departments rely on a robust set of analytics and Key Performance Indicators (KPIs) to measure their progress towards revenue and customer growth goals. With machine learning, marketing departments will be able to deliver even more significant contributions to revenue growth, strengthening customer relationships in the process.

The following are 10 ways machine learning is revolutionizing marketing today and in the future:

  1. 57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support. 44% believe that AI and machine learning will provide the ability to improve on existing products and services. Marketing departments and the Chief Marketing Officers (CMOs) running them are the leaders devising and launching new strategies to deliver excellent customer experiences and are one of the earliest adopters of machine learning. Orchestrating every aspect of attracting, selling and serving customers is being improved by marketers using machine learning apps to more accurately predict outcomes. Source: Artificial Intelligence: What’s Possible for Enterprises In 2017 (PDF, 16 pp., no opt-in), Forrester, by Mike Gualtieri, November 1, 2016. Courtesy of The Stack.

  1. 58% of enterprises are tackling the most challenging marketing problems with AI and machine learning first, prioritizing personalized customer care, new product development. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in).

  1. By 2020, real-time personalized advertising across digital platforms and optimized message targeting accuracy, context and precision will accelerate. The combined effect of these marketing technology improvements will increase sales effectiveness in retail and B2C-based channels. Sales Qualified Lead (SQL) lead generation will also increase, potentially reducing sales cycles and increasing win rates. Source: Can Machines be Creative? How Technology is Transforming Marketing Personalization and Relevance, IDC White Paper Sponsored by Gerry Brown, July 2017.

  1. Analyze and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models. Instead of relying on expensive and time-consuming approaches to minimize customer churn, telecommunications companies and those in high-churn industries are turning to machine learning. The following graphic illustrates how defining risk models help determine how actions aimed at averting churn affect churn impact probability and risk. An intervention model allows marketers to consider how the level of intervention could affect the probability of churn and the amount of customer lifetime value (CLV). Source: Analyzing Customer Churn by using Azure Machine Learning.

  1. Price optimization and price elasticity are growing beyond industries with limited inventories including airlines and hotels, proliferating into manufacturing and services. All marketers are increasingly relying on machine learning to define more competitive, contextually relevant pricing. Machine learning apps are scaling price optimization beyond airlines, hotels, and events to encompass product and services pricing scenarios. Machine learning is being used today to determine pricing elasticity by each product, factoring in channel segment, customer segment, sales period and the product’s position in an overall product line pricing strategy. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

  1. Improving demand forecasting, assortment efficiency and pricing in retail marketing have the potential to deliver a 2% improvement in Earnings Before Interest & Taxes (EBIT), 20% stock reduction and 2 million fewer product returns a year. In Consumer Packaged Goods (CPQ) and retail marketing organizations, there’s significant potential for AI and machine learning to improve the entire value chain’s performance. McKinsey found that using a concerted approach to applying AI and machine learning across a retailer’s value chains has the potential to deliver a 50% improvement of assortment efficiency and a 30% online sales increase using dynamic pricing. Source:  Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

  1. Creating and fine-tuning propensity models that guide cross-sell and up-sell strategies by product line, customer segment, and persona. It’s common to find data-driven marketers building and using propensity models to define the products and services with the highest probability of being purchased. Too often propensity models are based on imported data, built in Microsoft Excel, making their ongoing use time-consuming. Machine learning is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model.

  1. Lead scoring accuracy is improving, leading to increased sales that are traceable back to initial marketing campaigns and sales strategies. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies. The following two slides are from an excellent webinar Mintigo hosted with Sirius Decisions and Sales Hacker. It’s a fascinating look at how machine learning is improving sales effectiveness. Source: Give Your SDRs An Unfair Advantage with Predictive (webinar slides on Slideshare).

  1. Identifying and defining the sales projections of specific customer segments and microsegments using RFM (recency, frequency and monetary) modeling within machine learning apps is becoming pervasive. Using RFM analysis as part of a machine learning initiative can provide accurate definitions of the best customers, most loyal, biggest spenders, almost lost, lost customers and lost cheap customers.
  2. Optimizing the marketing mix by determining which sales offers, incentive and programs are presented to which prospects through which channels is another way machine learning is revolutionizing marketing. Specific sales offers are created supported by contextual content, offers, and incentives. These items are made available to an optimization engine which uses machine learning logic to continually try to predict the best combination of marketing mix elements that will lead to a new sale, up-sell or cross-sell. Amazon’s product recommendation feature is an example of how their e-commerce site is using machine learning to increase up-sell, cross-sell and recommended products revenue.

Data Sources On Machine Learning’s Impact On Marketing:

4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.
AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn.  Feb. 8, 2018.

AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015.

Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF, 111 pp., no opt-in). January 2017.

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.
Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,
How Artificial Intelligence and Machine Learning Will Reshape Small Businesses, SMB Group (PDF, 8 pp., no opt-in) May 2017.

How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant

Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017

Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence. May 15, 2017.

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) PwC, 2017.

The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.

The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.

Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in)

Using machine learning for insurance pricing optimization, Google Cloud Big Data and Machine Learning Blog, March 29, 2017

What Marketers Can Expect from AI in 2018, Jacob Shama. Mintigo. January 16, 2018.

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