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Posts tagged ‘AI’

10 Charts That Will Change Your Perspective Of AI In Marketing

  • Top-performing companies are more than twice as likely to be using AI for marketing (28% vs. 12%) according to Adobe’s latest Digital Intelligence Briefing.
  • Retailers are investing $5.9B this year in AI-based marketing and customer service solutions to improve shoppers’ buying experiences according to IDC.
  • Financial Services marketers lead all other industries in AI application adoption, with 37% currently using them today.
  • Sales and Marketing teams most often collaborate using Configure-Price-Quote (CPQ) and Marketing Automation AI-based applications, with sales leaders predicting AI adoption will increase 155% across sales teams in two years.

Artificial Intelligence enables marketers to understand sales cycles better, correlating their strategies and spending to sales results. AI-driven insights are also helping to break down data silos so marketing and sales can collaborate more on deals. Marketing is more analytics and quant-driven than ever before with the best CMOs knowing which metrics and KPIs to track and why they fluctuate.

The bottom line is that machine learning and AI are the technologies CMOs and their teams need to excel today. The best CMOs balance the quant-intensive nature of running marketing with qualitative factors that make a company’s brand and customer experience unique. With greater insight into how prospects make decisions when, where, and how to buy, CMOs are bringing a new level of intensity into driving outcomes. An example of this can be seen from the recent Forbes Insights and Quantcast research, Lessons of 21st-Century Brands Modern Brands & AI Report (17 pp., PDF, free, opt-in). The study found that AI enables marketers to increase sales (52%), increase in customer retention (51%), and succeed at new product launches (49%). AI is making solid contributions to improving lead quality, persona development, segmentation, pricing, and service.

The following ten charts provide insights into how AI is transforming marketing:

  • 21% of sales leaders rely on AI-based applications today, with the majority collaborating with marketing teams sharing these applications. Sales leaders predict that their use of AI will increase 155% in the next two years. Sales leaders predict AI will reach critical mass by 2020 when 54% expect to be using these technologies. Marketing and sales are relying on AI-based marketing automation, configure-price-quote (CPQ), and intelligent selling systems to increase revenue and profit growth significantly in the next two years. Source: Salesforce Research, State of Sales, 3rd edition. (58 pp., PDF, free, opt-in).

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service is the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

  • 22% of marketers currently are using AI-based applications with an additional 57% planning to use in the next two years. There are nine dominant use cases marketers are concentrating on today, ranging from personalized channel experiences to programmatic advertising and media buying to predictive customer journeys and real-time next best offers. Source: Salesforce’s State of Marketing Study, 5th edition

  • Content personalization and predictive analytics from customer insights are the two areas CMOs most prioritize AI spending today. The CMO study found that B2B service companies are the top user of AI for content personalization (62.2%) and B2B product companies use AI for augmented and virtual reality, facial recognition and visual search more than any other business types. Source: CMOs’ Top Uses For AI: Personalization and Predictive Analytics. Marketing Charts. March 14, 2019

  • Personalizing the overall customer journey and driving next-best offers in real-time are the two most common ways marketing leaders are using AI today, according to Salesforce. Improving customer segmentation, improving advertising and media buying, and personalizing channel experiences are the next fastest-growing areas of AI adoption in marketing today. Source: Salesforce’s State of Marketing Study, 5th edition

  • 81% of marketers are either planning to or are using AI in audience targeting this year. 80% are currently using or planning to use AI for audience segmentation. EConsultancy’s study found marketers are enthusiastic about AI’s potential to increase marketing effectiveness and track progress. 88% of marketers interviewed say AI will enable them t be more effective in getting to their goals. Source: Dream vs. Reality: The State of Consumer First and Omnichannel Marketing. EConsultancy (36 pp., PDF, free, no opt-in).

  • Over 41% of marketers say AI is enabling them to generate higher revenues from e-mail marketing. They also see an over 13% improvement in click-thru rates and 7.64% improvement in open rates. Source: 4 Positive Effects of AI Use in Email Marketing, Statista (infographic), March 1, 2019.

Additional data sources on AI’s use in Marketing:

15 examples of artificial intelligence in marketing, eConsultancy, February 28, 2019

4 Positive Effects of AI Use in Email Marketing, Statista, March 1, 2019

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions), Forbes, Kate Harrison, January 20, 2019

AI: The Next Generation Of Marketing Driving Competitive Advantage Throughout The Customer Life Cycle, Forrester Consulting. February 2017 (10 pp., PDF, free, no opt-in).

Artificial Intelligence for Marketing (complete book) (361 pp., PDF, free, no opt-in)

Artificial Intelligence Roundup, eMarketer, May 2018 (15 pp., PDF, free, no opt-in)

Digital Intelligence Briefing, Adobe, 2018 (43 pp., PDF, free, no opt-in).

How 28 Brands Are Using AI to Enhance Their Marketing [Infographic], Impact Blog

How AI Is Changing Sales, Harvard Business Review, July 30, 2018

How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce

How To Win Tomorrow’s Car Buyers – Artificial Intelligence in Marketing & Sales, McKinsey Center for Future Mobility, McKinsey & Company. February 2019. (44 pp., PDF, free, no opt-in)

IDC MarketScape: Worldwide Artificial Intelligence in Enterprise Marketing Clouds 2017 Vendor Assessment, (11 pp., PDF, free, no opt-in.)

In-depth: Artificial Intelligence 2019, Statista Digital Market Outlook, February 2019 (client access reqd).

Leading reasons to use artificial intelligence (AI) for marketing personalization according to industry professionals worldwide in 2018, Statista.

Lessons of 21st-Century Brands Modern Brands & AI Report, Forbes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

Powerful pricing: The next frontier in apparel and fashion advanced analytics, McKinsey & Company, December 2018

Share of marketing and agency professionals who are comfortable with AI-enabled technology automated handling of their campaigns in the United States as of June 2018, Statista.  

The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

Visualizing the uses and potential impact of AI and other analytics, McKinsey Global Institute, April 2018.  Interactive page based on Tableau data set can be found here.

What really matters in B2B dynamic pricing, McKinsey & Company, October 2018

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales, McKinsey & Company, February 2019

Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide, IDC; March 11, 2019

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What’s Next For You? How AI Is Transforming Talent Management

Bottom Line: Taking on the talent crisis with greater intelligence and insight, delivering a consistently excellent candidate experience, and making diversity and inclusion a part of their DNA differentiates growing businesses who are attracting and retaining employees. The book What’s Next For You? by Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, President of eightfold.ai provide valuable insights and a data-driven roadmap of how AI is helping to solve the talent crisis for any business.

The Talent Crisis Is Real

  • 78% of CEOs and Chief Human Resource Officers (CHROs) say talent programs are important, with 56% say their current programs are ineffective.
  • 83% of employees want a new job yet only 53% want to leave for a new company.
  • 57% of employees say diversity and inclusion initiatives aren’t working, and 40% say their companies lack qualified diverse talent.
  • Nearly 50% of an organizations’ top talent will leave their jobs in the first two years of being hired.
  • 28% of open positions today won’t be filled in the next 12 months.

The above findings are just a sample of the depth of data-driven content and roadmap the book What’s Next For You? delivers. Co-authors Ashutosh Garg’s and Kamal Ahluwalia’s expertise in applying AI and machine learning to talent management problems with a strong data-first mindset is evident throughout the book. What makes the book noteworthy is how the authors write from the heart first with empathy for applicants and hiring managers, supporting key points with data. The empathetic, data-driven tone of the book makes the talent crisis relatable while also illustrating how AI can help any business make better talent management decisions.

“Businesses are having to adapt to technology changes and changes in customer expectations roughly every 10 years – a timeframe that is continuing to shrink. As a result, business leaders need to really focus on rethinking their business strategy and the associated talent strategy, so they have the organizational capability to transform and capitalize on the inevitable technology shifts,” writes John Thompson, Venture Partner, Lightspeed Venture Partners and Chairman of the Board at Microsoft in the forward.

The book cites talent management researchers and experts who say “our current knowledge base has a half-life of about two years, and the speed of technology is outperforming us as humans because of what it can do quickly and effectively“ (p.64). John Thompson’s observations in the forward that the time available for adapting to change is shrinking is a unifying thread that ties this book together. One of the most convincing is the fact that using today’s Applicant Tracking Systems (ATS) and hiring processes prone to biases, there’s a 30% chance a new hire will not make it through their first year. If the new hire is a cloud computing professional, this equates to a median salary of $146,350 and taking best-case 46 days to find their replacement. The cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. Any manager who has lost a new hire within a year can relate to how real the talent crisis is and how urgent it is to solve it.

The Half-Life Of Skills Is Shrinking Fast

The most compelling chapter of the book illustrates how today’s talent crisis can be solved by taking an AI-enabled approach to every aspect of talent management. Chapter 4, The Half-Life Of Skills Is Shrinking Fast, delves into how AI can find candidates who can unlearn old concepts, and quickly master new ones. The book calls out this attribute of any potential new hire as being essential for them to adapt.  Using higher quality data than is available in traditional ATS systems, the authors illustrate how AI-based systems can be used for evaluating both the potential and experiences of applicants to match them with positions they will excel in. The authors make a convincing argument that AI can increase the probability of new candidate success. They cite a well-known Leadership IQ statistic of 46% of all new employee hires failing to adapt within 18 months, and the Harvard Business Review study finding between 40% to 60% of new upper management hires fail within 18 months. The authors contend that even Leonardo Da Vinci, one of the primary architects of the Renaissance, would have trouble finding work using a traditional resume entered into an ATS system today because his exceptional capabilities and potential would have never been discovered. When our existing process of recruiting is based on practices over 500 years old, as this copy of Leonardo Da Vinci’s resume illustrates, it’s time to put AI to work matching peoples’ potential with unique position requirements.

When Employees Achieve Their Potential, Companies Do Too   

Attracting the highest potential employees possible and retaining them is the cornerstone of any digital business’ growth strategy today and in the future. The book addresses the roadblocks companies face in attaining that goal, with bias being one of the strongest. “For example, McKinsey & Co., a top consulting agency, studied over 1,000 companies across 12 countries and found that firms in the top quartile of gender diversity were a fifth more likely to have above-average profits than those in the bottom quartile,” (p. 105). Further, “diverse executive boards generate better financial returns, and gender-diverse teams are more creative, more productive and more confident.” (p. 105).

In conclusion, consider this book a roadmap of how hiring and talent management can change for the better based on AI. The authors successfully illustrate how combining talent, personalization at scale, and machine learning can help employees achieve their potential, enabling companies to achieve theirs in the process.

Indeed’s 10 Most Popular AI & Machine Learning Jobs This Year

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • AI and Machine Learning job postings on Indeed rose 29.10% over the last year between May 2018 and May 2019.
  • Machine Learning and Deep Learning Engineers are the most popular jobs posted on Indeed between 2018 and 2019.
  • Machine Learning Engineers are earning an average salary of $142,858.57 in 2019 based on an analysis of all open positions on Indeed.
  • Indeed is seeing a leveling off of candidate-initiated searches for AI & Machine Learning (ML) jobs, dropping 14.5% between May 2018 and May 2019

These and many other insights are from Indeed’s recent report of the top 10 AI Jobs, and Salaries. Indeed’s analytics team completed an analysis of AI and machine learning hiring trends in 2019 to discover the top positions, highest salaries, and where the best opportunities are. The following are key insights from their latest study of AI and machine learning recruiting and hiring trends:

  • Machine Learning Engineers earn an average salary of $142,858.57 in 2019 based on an analysis of all open positions on Indeed. The Indeed analytics team found that the average annual salary for Machine Learning Engineers has grown by $8,409 in just a year, increasing 5.8%. Algorithm engineer’s average annual salary rose to $109,313 this year, an increase of $5,201, or 5%. Both salary bumps are likely a result of organizations’ spending more to attract talent to these crucial roles in a competitive AI job market

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • Machine Learning and Deep Learning Engineers are the most sought-after, popular jobs posted on Indeed between 2018 and 2019.  The Indeed analytics team identified the top 10 positions with the highest percentage of job descriptions that include the keywords “artificial intelligence” or “machine learning.” New jobs appearing on the list for the first time include Senior Data Scientist, Junior Data Scientist, Developer Consultant, Director of Data Science, and Lead Data Scientist. The inclusion of five new titles and the mix of skills shown in the table below reflects organizations’ growing expertise using AI, deep learning, and machine learning to drive business outcomes.

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • AI and Machine Learning job postings on Indeed rose 29.10% over the last year between May 2018 and May 2019.  Indeed found the increase is significantly less than it was for the previous two years. During the same period, May 2017 to May 2018 AI job postings on Indeed rose 57.91%, and a whopping 136.29% between May 2016 and May 2017.
  • Indeed is seeing a leveling off of candidate-initiated searches for AI & Machine Learning (ML) jobs, dropping 14.5% between May 2018 and May 2019. In comparison, searches increased 32% between May 2017 and May 2018 and 49.1% between May 2016 and May 2017. There are demand-and supply-side explanations for the 14.5% drop. From the demand side, the effects of AI and machine learning reaching broader adoption and maturing in organizations is leading to a greater variety of skills being recruited for. The 14.5% reduction reflects the broadening base of skills enterprises need to get the most out of AI and machine learning. From a supply side, potential job candidates are seeing the broadening base of skills they need to get hired, which are quickly making job descriptions from two years ago or longer obsolete. Finding candidates who have capabilities and potential to excel in AI and machine learning positions needs to get beyond just relying on job descriptions. Eightfold is doing just that by relying on machine learning algorithms to match candidates who have the optimal set of capabilities and potential for every open position an organization has.
  • New York, San Francisco, and Washington D.C. are the top three cities for AI and machine learning jobs in 2019. Indeed’s 2018 study also found New York and San Francisco leading all other metropolitan areas in open positions. New York’s diverse industries that range from banking, financial services, institutional investing, insurance to a growing AI startup community all contribute to its ranking first in the U.S. for AI positions.

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

Salesforce Now Has Over 19% Of The CRM Market

 

  • Salesforce dominated the worldwide CRM market with a 19.5% market share in 2018, over double its nearest rival, SAP, at 8.3% share.
  • Worldwide spending on customer experience and relationship management (CRM) software grew 15.6% to reach $48.2B in 2018.
  • 72.9% of CRM spending was on software as a service (SaaS) in 2018, which is expected to grow to 75% of total CRM software spending in 2019.
  • Worldwide enterprise application software revenue totaled more than $193.6B in 2018, a 12.5% increase from 2017 revenue of $172.1B. CRM made up nearly 25% of the entire enterprise software revenue market.

CRM remains the largest and fastest growing enterprise software category today according to the latest market sizing, and market share research Gartner published this weekGartner defines CRM as providing the functionality to companies across the four segments of customer service and support, digital commerce, marketing, and sales. All four subsegments of the CRM market grew by more than 13.7%, with marketing emerging as the fastest growing segment, increasing by 18.8% and representing more than 25% of the entire CRM market. Customer service and support retain its No. 1 position, contributing 35.7% of CRM market revenue, attaining $17.1B in revenues in 2018.

Key insights include the following:

  • With 19.5% market share, Salesforce has over 2X the CRM sales SAP has and over 3X of Oracle. Salesforce continues to dominate CRM globally, increasing its market share from 18.3% in 2017 to 19.5% in 2018. Adobe is the only other vendor to grow its market share in 2018. Microsoft and SAP successfully held onto to market share while Oracle lost share.

  • Adobe and Salesforce grew faster than the overall market, increasing CRM revenues 21.7% and 23.2% respectively. Adobe’s CRM sales jumped from $2B in 2017 to $2.4B in 2018. Salesforce CRM revenues increased from $7.6B in 2017 to $9.4B in 2018, growing the fastest of all competitors in this market. SAP grew 15.5% between 2017 and 2018, just below the overall market growth of 15.6%. Microsoft (15%) and Oracle (7.1%) grew slower than the market. The following graphic compares growth rates between 2017 and 2018.

  • Adobe dominates the marketing subsegment of CRM with 19% market share in 2018. Salesforce has 11.7% of the marketing subsegment, followed by IBM (5.7%), SAP (4%), Oracle (3.6%) and HubSpot (3.4%). Gartner estimates the marketing subsegment was a $12.2B market in 2018, increasing from $10.3B in 2017, achieving 18.8% growth in just a year.
  • Eastern and Western Europe were the fastest growing regions at 19.7% and 17.5% respectively. North America and Western Europe were the largest two regions with North America growing at 15.2% to reach $28.1B in revenue.

Sources:

Gartner Says Worldwide Customer Experience and Relationship Management Software Market Grew 15.6% in 2018

Market Share: Customer Experience and Relationship Management, Worldwide, 2018 (client access required)

Customer Experiences Define Success In A Digital-First World

Customer Experiences Define Success In A Digital-First World

  • 91% of enterprises have adopted or have plans to adopt a digital-first strategy. Of these enterprises, 48% already have a digital-first approach in place.
  • Creating better customer experiences (67%), improving process efficiency through automation (53%), and driving new revenue (48%) are the top three digital business strategies enterprises are investing in today.
  • 35% of enterprises have experienced revenue growth due to digital business initiatives over the past 12 months.
  • 5G, Artificial Intelligence, and Machine Learning are the top technologies being researched by enterprises who are defining digital business strategies.
  • Enterprises are planning to spend $15.3M on digital initiatives over the next 12 months. 59% will be allocated to technology, and 41% will be dedicated to people and skills.

These and many other fascinating insights are from the second annual IDG Digital Business study, The State of Digital Business Transformation 2019. You can download a summary of the slides here (7 pp., PDF, opt-in). The survey’s methodology is based on 702 interviews across nine industries with technology, financial services, and business services (consulting, legal and real estate) comprising 43% of all respondents. IDG relied on CIO, Computerworld, CSO, InfoWorld, and Network World visitors as their primary respondent base. For additional details regarding the methodology, please see page 2 of the study.

The study’s primary goal was to gain a better understanding of where organizations are in their approaches to becoming digital-first businesses. The study captures the strategies and technologies businesses are adopting to ensure digitally-driven growth with customer experience improvements being proven as a growth catalyst. Key insights from the survey include the following:

  • 52% of enterprises define digital business as meeting customer experience expectations, jumping to 65% for financial services enterprises. Customer expectations rule all other categories of how an enterprise defines a digital business. 49% define digital business as enabling worker productivity with mobile apps, data access, and AI-assisted automation. The following graphic compares how enterprises define their digital business. Please click on the graphic to expand for easier reading.

Customer Experiences Define Success In A Digital-First World

  • Mobile devices and apps are enterprises’ platform of choice for launching digital-first strategies in 2019. Mobile apps and the platforms supporting them provide the needed scale, speed-to-market, and performance gains through application-level improvements that all businesses need to gain initial adoption and growth with their digital-first strategies. IDG found that private cloud and business process management are the second- and third-most used technologies to drive digital-first initiatives. Enterprises also have a considerable lead when it comes to mobile app availability: 74% have mobile apps today compared to 51% of SMBs.

  • Internet of Things (IoT), Artificial Intelligence (AI) and machine learning are the leading three initiatives enterprises have in pilot today as part of their digital-first initiatives. 21% of all organizations surveyed are in one or more IoT pilots, and 20% of organizations are piloting AI and machine learning projects today. Nearly a third of all organizations (29%) have multi-cloud configurations in production today, and 25% have software-defined Wide Area Networks (WANs).

  • 57% of enterprises (companies with over 1K employees) say improving new product and service offerings by digitally enabling operations is the single greatest source of revenue growth. Digitally enabling or streamlining new product and development processes and the systems supporting them also improve the ability to innovate and size new opportunities (49%). It makes sense that once the new product development process is more digitally enabled, an organization will be able to more efficiently launch new capabilities (47% in enterprises) and improve sales capacity including upsell and cross-sell (41% overall).

  • Creating better customer experiences (67%), improving process efficiency through automation (53%), and driving new revenue (48%) are the top three digital business strategies enterprises are investing in today. Business Management, including General Managers with P&L responsibility, are placing a high priority on creating a better customer experience, far above all else. They’re the revenue drivers of businesses adopting a digital-first strategy today as well, over 10% higher than IT Management and 12% higher than IT executives.

  • In the most successful digital-first businesses, the CIO the most visible, vocal, and successful in leading change management initiatives. Six of the nine core dimensions of a successful digital enablement strategy are dominated by CIOs. Technology Needs Assessment (48%), IT Skills Assessment (48%) and Change Management (33%) are the three areas CIOs are making the greatest contribution to digital-first strategies on the part of their businesses. It’s important to note that CIOs are far and away, the champion and leader of data management strategies as well.

  • Enterprises are placing a high priority on data security and protection as part of the digital-first initiatives, with 27% having cybersecurity systems in place. It’s encouraging to see business and IT leaders making data and system security their highest priority, getting results quickly in this area. Technology needs assessment, and IT skills assessment (both 24%) are also areas where enterprises are making strong progress. As the CIO owns these areas and is also the person most likely to be owning change management, it’s understandable how advanced digital-first businesses are on these two dimensions. The following graphic compares the progress enterprises are making in becoming a digitally-driven business.

How To Get Your Data Scientist Career Started

The most common request from this blogs’ readers is how to further their careers in analytics, cloud computing, data science, and machine learning. I’ve invited Alyssa Columbus, a Data Scientist at Pacific Life, to share her insights and lessons learned on breaking into the field of data science and launching a career there. The following guest post is authored by her.

Earning a job in data science, especially your first job in data science, isn’t easy, especially given the surplus of analytics job-seekers to analytics jobs.

Many people are looking to break into data science, from undergraduates to career changers, have asked me how I’ve attained my current data science position at Pacific Life. I’ve referred them to many different resources, including discussions I’ve had on the Dataquest.io blog and the Scatter Podcast. In the interest of providing job seekers with a comprehensive view of what I’ve learned that works, I’ve put together the five most valuable lessons learned. I’ve written this article to make your data science job hunt easier and as efficient as possible.

  • Continuously build your statistical literacy and programming skills. Currently, there are 24,697 open Data Scientist positions on LinkedIn in the United States alone. Using data mining techniques to analyze all open positions in the U.S., the following list of the top 10 data science skills was created today. As of April 14, the top 3 most common skills requested in LinkedIn data scientist job postings are Python, R, and SQL, closely followed by Jupyter Notebooks, Unix Shell/Awk, AWS, and Tensorflow. The following graphic provides a prioritized list of the most in-demand data science skills mentioned in LinkedIn job postings today. Please click on the graphic to expand for easier viewing.

Hands-on training is the best way to develop and continually improve statistical and programming skills, especially with the languages and technologies LinkedIn’s job postings prioritize.  Getting your hands dirty with a dataset is often much better than reading through abstract concepts and not applying what you’ve learned to real problems. Your applied experience is just as important as your academic experience, and taking statistics, and computer science classes help to translate theoretical concepts into practical results. The toughest thing to learn (and also to teach) about statistical analysis is the intuition for what the big questions to ask of your dataset are. Statistical literacy, or “how” to find the answers to your questions, come with education and practice. Strengthening your intellectual curiosity or insight into asking the right questions comes through experience.

  • Continually be creating your own, unique portfolio of analytics and machine learning projects. Having a good portfolio is essential to be hired as a data scientist, especially if you don’t come from a quantitative background or have experience in data science before. Think of your portfolio as proof to potential employers that you are capable of excelling in the role of a data scientist with both the passion and skills to do the job. When building your data science portfolio, select and complete projects that qualify you for the data science jobs, you’re the most interested in. Use your portfolio to promote your strengths and innate abilities by sharing projects you’ve completed on your own. Some skills I’d recommend you highlight in your portfolio include:
    • Your programming language of choice (e.g., Python, R, Julia, etc.).
    • The ability to interact with databases (e.g., your ability to use SQL).
    • Visualization of data (static or interactive).
    • Storytelling with data. This is a critical skill. In essence, can someone with no background in whatever area your project is in look at your project and gain some new understandings from it?
    • Deployment of an application or API. This can be done with small sample projects (e.g., a REST API for an ML model you trained or a nice Tableau or R Shiny dashboard).

Julia Silge and Amber Thomas both have excellent examples of portfolios that you can be inspired by. Julia’s portfolio is shown below.

  • Get (or git!) yourself a website. If you want to stand out, along with a portfolio, create and continually build a strong online presence in the form of a website.  Be sure to create and continually add to your GitHub and Kaggle profiles to showcase your passion and proficiency in data science. Making your website with GitHub Pages creates a profile for you at the same time, and best of all it’s free to do. A strong online presence will not only help you in applying for jobs, but organizations may also reach out to you with freelance projects, interviews, and other opportunities.
  • Be confident in your skills and apply for any job you’re interested in, starting with opportunities available in your network.  If you don’t meet all of a job’s requirements, apply anyway. You don’t have to know every skill (e.g., programming languages) on a job description, especially if there are more than ten listed. If you’re a great fit for the main requirements of the job’s description, you need to apply. A good general rule is that if you have at least half of the skills requested on a job posting, go for it. When you’re hunting for jobs, it may be tempting to look for work on company websites or tech-specific job boards. I’ve found, as have many others, that these are among the least helpful ways to find work. Instead, contact recruiters specializing in data science and build up your network to break into the field. I recommend looking for a data science job via the following sources, with the most time devoted to recruiters and your network:
    • Recruiters
    • Friends, family, and colleagues
    • Career fairs and recruiting events
    • General job boards
    • Company websites
    • Tech job boards.

Alyssa Columbus is a Data Scientist at Pacific Life and member of the Spring 2018 class of NASA Datanauts. Previously, she was a computational statistics and machine learning researcher at the UC Irvine Department of Epidemiology and has built robust predictive models and applications for a diverse set of industries spanning retail to biologics. Alyssa holds a degree in Applied and Computational Mathematics from the University of California, Irvine and is a member of Phi Beta Kappa. She is a strong proponent of reproducible methods, open source technologies, and diversity in analytics and is the founder of R-Ladies Irvine. You can reach her at her website: alyssacolumbus.com.

Seven Things You Need To Know About IIoT In Manufacturing

  • Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.
  • IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies.
  • Connected IoT technologies are enabling a new era of smart, connected products that often expand on the long-proven platforms of everyday products. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020.

These and many other fascinating insights are from IoT Analytics’ study, IIoT Platforms For Manufacturing 2019 – 2024 (155 pp., PDF, client access reqd). IoT Analytics is a leading provider of market insights for the Internet of Things (IoT), M2M, and Industry 4.0. They specialize in providing insights on IoT markets and companies, focused market reports on specific IoT segments and Go-to-Market services for emerging IoT companies. The study’s methodology includes interviews with twenty of the leading IoT platform providers, executive-level IoT experts, and IIoT end users. For additional details on the methodology, please see pages 136 and 137 of the report. IoT Analytics defines the Industrial loT (lloT) as heavy industries including manufacturing, energy, oil and gas, and agriculture in which industrial assets are connected to the internet.

The seven things you need to know about IIoT in manufacturing include the following:

  • IoT Analytics’ technology architecture of the Internet of Things reflects the proliferation of new products, software and services, and the practical needs manufacturers have for proven integration to make the Industrial Internet of Things (IIoT) work. IoT technology architectures are in their nascent phase, showing signs of potential in solving many of manufacturing’s most challenging problems. IoT Analytics’ technology architecture shown below is designed to scale in response to the diverse development across the industry landscape with a modular, standardized approach.

  • IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies. IoT Analytics is seeing IIoT platforms begin to replace existing industrial software systems that had been created to bridge the IT and OT gaps in manufacturing environments. Their research teams are finding that IIoT Platforms are an adjacent technology to these typical industrial software solutions but are now starting to replace some of them in smart connected factory settings. The following graphic explains how IoT Analytics sees the IIoT influence across the broader industrial landscape:

  • Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years. IoT Analytics is finding that manufacturing is the largest IoT platform industry segment and will continue to be one of the primary growth catalysts of the market through 2024. For purposes of their analysis, IoT Analytics defines manufacturing as standardized production environments including factories, workshops, in addition to custom production worksites such as mines, offshore oil gas, and construction sites. The lloT platforms for manufacturing segment have experienced growth in the traditionally large manufacturing-base countries such as Japan and China. IoT Analytics relies on econometric modeling to create their forecasts.

  • In 2018, the industrial loT platforms market for manufacturing had an approximate 60%/40% split for within factories/outside factories respectively. IoT Analytics predicts this split is expected to remain mostly unchanged for 2019 and by 2024 within factories will achieve slight gains by a few percentage points. The within factories type (of lloT Platforms for Manufacturing) is estimated to grow from a $1B market in 2018 to a $1.5B market by 2019 driven by an ever-increasing amount of automation (e.g., robots on the factory floor) being introduced to factory settings for increased efficiencies, while the outside factories type is forecast to grow from $665M in 2018 to become a $960M market by 2019.

  • Discrete manufacturing is predicted to be the largest percentage of Industrial IoT platform spending for 2019, growing at a CAGR of 46% from 2018. Discrete manufacturing will outpace batch and process manufacturing, becoming 53% of all IIoT platform spending this year. IoT Analytics sees discrete manufacturers pursuing make-to-stock, make-to-order, and assemble-to-order production strategies that require sophisticated planning, scheduling, and tracking capabilities to improve operations and profitability. The greater the production complexity in discrete manufacturing, the more valuable data becomes. Discrete manufacturing is one of the most data-prolific industries there are, making it an ideal catalyst for IIoT platform’s continual growth.

  • Manufacturers are most relying on IIoT platforms for general process optimization (43.1%), general dashboards & visualization (41.1%) and condition monitoring (32.7%). Batch, discrete, and process manufacturers are prioritizing other use cases such as predictive maintenance, asset tracking, and energy management as all three areas make direct contributions to improving shop floor productivity. Discrete manufacturers are always looking to free up extra time in production schedules so that they can offer short-notice production runs to their customers. Combining IIoT platform use cases to uncover process and workflow inefficiencies so more short-notice production runs can be sold is driving Proof of Concepts (PoC) today in North American manufacturing.

  • IIoT platform early adopters prioritize security as the most important feature, ahead of scalability and usability. Identity and Access Management, multifactor-factor authentication, consistency of security patch updates, and the ability to scale and protect every threat surface across an IIoT network are high priorities for IIoT platform adopters today. Scale and usability are the second and third priorities. The following graphic compares IIoT platform manufacturers’ most important needs:

For more information on the insights presented here, check out IoT Analytics’ report: IIoT Platforms For Manufacturing 2019 – 2024.

Microsoft Leads The AI Patent Race Going Into 2019

  • There have been over 154,000 AI patents filed worldwide since 2010 with the majority being in health fields (29.5%), Industry-specific solutions (25.3%) and AI-based digital security (15.7%).
  • AI-based marketing patents are the fasting growing global category, reaching a Compound Annual Growth Rate (CAGR) of 29.3% between 2010 and 2018.
  • The second- and third-fastest growing global AI patent categories between 2010 and 2018 are AI-based digital security (23.4% CAGR) and AI-based mobility (23% CAGR).
  • 79,936 patents were filed in the United States between 2010 and 2018, with the majority being in the health field (32.6%) followed by Industry-specific solutions (20.5%) and AI-based digital security (18%).
  • Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks.

These and many other insights are from an excellent presentation recently given by Kai Gramke, Managing Director of EconSight titled Artificial Intelligence As A Key Technology and Driver of Technological Progress. EconSight clients include the Swiss Federal Council, German Federal Chancellery, leading European think tanks, research institutes and half of the German DAX-30 companies.  The presentation and information shared in this post were generated using the PatentSight analytics platform. PatentSight is a LexisNexis company and you can learn more about them here.  The following are the key takeaways from Kai’s recent research and presentation using PatentSight:

  • EconSight finds that Microsoft leads the AI patent race going into 2019 with 697 world class patents that the firm classifies as having a significant competitive impact as of November 2018. Out of the top 30 companies and research institutions as defined by EconSight in their recent analysis, Microsoft has created 20% of all patents in the global group of patent-producing companies and institutions. The following graphic provides a comparison of the top 3o in the group. Please click on the graphic to expand it for easier reading.

  • Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks.  Machine learning is based on the foundational concepts of Bayesian analysis, data mining, and predictive analytics. Machine learning algorithms and the applications they rely on are designed to find patterns in large-scale data sets, while also being able to solve complex, constraint-based problems by learning from the data.  Enterprise software companies including Microsoft, SAP, and others are actively developing AI technologies that integrate into their existing platforms, streamlining adoption across their many customers. Please click on the graphic to expand for easier reading.

  • There have been 225,833 AI-based patents filed globally since 2000, with 30.7% being Industry specific (Industry 4.0 on the graphic below) followed by health-related patents (28.1%) 13.8% of all AI-based patents are for digital security and 11.9% for energy. It’s interesting to note that the fastest growing patents between 2000 and 2018 are for applying AI to marketing (22% CAGR) and AI-based digital security (18.8% CAGR). Please click on the graphic to expand for easier reading.

Using Machine Learning To Find Employees Who Can Scale With Your Business

  • Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
  • Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
  • Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves throughout their careers.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last. These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing The Probability Hiring Game Versus Making Data-Driven Decisions

Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.

Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.

A Data-Driven Approach To Finding Employees Who Can Scale

Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.

Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills, and strengths they need to fill open positions.

Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. Ashutosh Garg, Eightfold founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.”  One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.

Conclusion

Machine learning algorithms capable of completing millions of pattern matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst most often responsible for making the greatest contributions to a company’s growth.

Which CRM Applications Matter Most In 2018

 

According to recent research by Gartner,

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.

These and many other fascinating insights are from Gartner’s What’s Hot in CRM Applications in 2018 by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj, Sandy Shen and Olive Huang, published on August 14, 2018. Gartner clients can access the study here  (10 pp., PDF, client access reqd.).

Gartner continually tracks and analyzes the areas their clients have the most interest in and relies on that data to complete their yearly analysis of CRM’s hottest areas. Inquiry topics initiated by clients are an excellent leading indicator of relative interest and potential demand for specific technology solutions. Gartner organizes CRM technologies into the four category areas of Marketing, Sales, Customer Service, and Digital Commerce.

The following graphic from the report illustrates the top CRM applications priorities in Marketing, Sales, Customer Service, and Digital Commerce.

Key insights from the study include the following:

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation. In my opinion and based on discussions with CMOs, interest in marketing analytics is soaring as they are all looking to quantify their team’s contribution to lead generation, pipeline growth, and revenue. I see analytics- and data-driven clarity as the new normal. I believe that knowing how to quantify marketing contributions and performance requires CMOs and their teams to stay on top of the latest marketing, mobile marketing, and predictive customer analytics apps and technologies constantly. The metrics marketers choose today define who they will be tomorrow and in the future.
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service. It’s been my experience that AI and machine learning are revolutionizing selling by guiding sales cycles, optimizing pricing and enabling CPQ to define and deliver smart, connected products. I’m also seeing CMOs and their teams gain value from Salesforce Einstein and comparable intelligent agents that exemplify the future of AI-enabled selling. CMOs are saying that Einstein can scale across every phase of customer relationships. Based on my previous consulting in CPQ and pricing, it’s good to see decades-old core technologies underlying Price Optimization and Management are getting a much-needed refresh with state-of-the-art AI and machine learning algorithms, which is one of the factors driving their popularity today. Using Salesforce Einstein and comparable AI-powered apps I see sales teams get real-time guidance on the most profitable products to sell, the optimal price to charge, and which deal terms have the highest probability of closing deals. And across manufacturers on a global scale sales teams are now taking a strategic view of Configure, Price, Quote (CPQ) as encompassing integration to ERP, CRM, PLM, CAD and price optimization systems. I’ve seen global manufacturers take a strategic view of integration and grow far faster than competitors. In my opinion, CPQ is one of the core technologies forward-thinking manufacturers are relying on to launch their next generation of smart, connected products.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation. It’s fascinating to visit with CMOs and see the pilots and full production implementations of AI being used to streamline customer service. One CMO remarked how effective AI is at providing greater contextual intelligence and suggested recommendations to customers based on their previous buying and services histories. It’s interesting to watch how CMOs are attempting to integrate AI and its associated technologies including ChatBots to their contribution to Net Promoter Scores (NPS). Every senior management team running a marketing organization today has strong opinions on NPS. They all agree that greater insights gained from predictive analytics and AI will help to clarify the true value of NPS as it relates to Customer Lifetime Value (CLV) and other key metrics of customer profitability.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.  It’s my observation that machine learning’s potential to revolutionize sales is still nascent with many high-growth use cases completely unexplored. In speaking with the Vice President of Sales for a medical products manufacturer recently, she said her biggest challenge is hiring sales representatives who will have longer than a 19-month tenure with the company, which is their average today.  Imagine, she said, knowing the ideal attributes and strengths of their top performers and using machine learning and AI to find the best possible new sales hires. She and I discussed the spectrum of companies taking on this challenge, with Eightfold being one of the leaders in applying AI and machine learning to talent management challenges.

Source: Gartner by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj,  Sandy Shen and Olive Huang, published on August 14, 2018.

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