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Posts from the ‘Machine learning’ Category

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it’s time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights.

The greater the accuracy and speed of supply chain-based data integration and knowledge, the greater the accuracy of custom product orders. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case.

Why Channels Need AI-Based Knowledge Sharing Networks Now

Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy. AI-based services, including Amazon Alexa, Microsoft Cortana, and Google Voice and others, rely on knowledge-sharing networks to collaborate with automotive supply chains and strengthen OEM partnerships. The following graphic reflects how successful Amazon’s Alexa Automotive OEM sales team is at using knowledge-sharing networks to gain design wins across their industry.

The following are a few of the many reasons why creating and continually fine-tuning an AI-based knowledge-sharing network is an evolving strategy worth paying attention to:

  • Supply chains are the primary source of knowledge that must permeate an organization’s structure and channels for the company to stay synchronized to broader market demands. For CPQ channel selling strategies to thrive, they need real-time pricing, availability, available-to-promise, and capable-to-promise data to create accurate, competitive quotes that win deals. The better the supplier collaboration across supply chains and with channel partners, the higher the probability of selling more. A landmark study of the Toyota Production System by Professors Jeffrey H Dyer & Kentaro Nobeoka found that Toyota suppliers value shared data more than cash, making knowledge sharing systems invaluable to them (Dyer, Nobeoka, 2000).
  • Smart manufacturing metrics also need to be contributing real-time data to knowledge sharing systems channel partners use, relying on AI to create quotes for products that can be built the fastest and are the most attractive to each customer. Combining manufacturing’s real-time monitoring data stream of ongoing order progress and production availability with supply chain pricing, availability, and quality data all integrated to a cloud-based CPQ platform gives channel partners what they need to close deals now. AI-based knowledge-sharing networks will link supply chains, manufacturing plants, and channel partners to create smart factories that drive more sales. According to a recent Capgemini study, manufacturers are planning to launch 40% more smart factories in the next five years, increasing their annual investments by 1.7 times compared to the previous three years, according to their recent Smart factories @ scale Capgemini survey. The following graphic illustrates the percentage growth of smart factories across key geographic regions, a key prerequisite for enabling AI-based knowledge-sharing networks with real-time production data:
  • By closing the data gaps between suppliers, manufacturing, and channels, AI-based knowledge-sharing networks give resellers the information they need to sell with greater insight. Amazon’s Alexa OEM marketing teams succeeded in getting the majority of design-in wins with automotive manufacturers designing their next-generation of vehicles with advanced electronics and AI features. The following graphic from Dr. Dyer’s and Nobeoka’s study defines the foundations of a knowledge-sharing network. Applying AI to a mature knowledge-sharing network creates a strong network effect where every new member of the network adds greater value.
  • Setting the foundation for an effective knowledge sharing network needs to start with platforms that have AI and machine learning designed in with structure that can flex for unique channel needs. There are several platforms capable of supporting AI-based knowledge-sharing networks available, each with its strengths and approach to adapting to supply chain, manufacturing, and channel needs. One of the more interesting frameworks not only uses AI and machine learning across its technology pillars but also takes into consideration that a company’s operating model needs to adjust to leverage a connected economy to adapt to changing customer needs. BMC’s Autonomous Digital Enterprise (ADE) is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to create innovation ecosystems in a knowledge-sharing network. Knowledge-sharing networks thrive on continuous learning. It’s good to see major providers using adaptive and machine learning to strengthen their platforms, with BMC’s Automated Mainframe Intelligence (AMI) emerging as a leader. Their approach to using adaptive learning to maintain data quality during system state changes and link exceptions with machine learning to deliver root cause analysis is prescient of where continuous learning needs to go.  The following graphic explains the ADE’s structure.

Conclusion

Knowledge-sharing networks have proven very effective in improving supply chain collaboration, supplier quality, and removing barriers to better inventory management. The next step that’s needed is to extend knowledge-sharing networks to resellers and enable knowledge sharing applications that use AI to tailor product and service recommendations for every customer being quoted and sold to. Imagine resellers being able to create quotes based on the most buildable products that could be delivered in days to buying customers. That’s possible using a knowledge-sharing network. Amazon’s success with Alexa design wins shows how their use of knowledge-sharing systems helped to provide insights needed across automotive OEMs wanted to add voice-activated AI technology to their next-generation vehicles.

References

BMC, Maximizing the Value of Hybrid IT with Holistic Monitoring and AIOps (10 pp., PDF).

BMC Blogs, 2019 Gartner Market Guide for AIOps Platforms, December 2, 2019

Cai, S., Goh, M., De Souza, R., & Li, G. (2013). Knowledge sharing in collaborative supply chains: twin effects of trust and power. International journal of production Research51(7), 2060-2076.

Capgemini Research Institute, Smart factories @ scale: Seizing the trillion-dollar prize through efficiency by design and closed-loop operations, 2019.

Columbus, L, The 10 Most Valuable Metrics in Smart Manufacturing, Forbes, November 20, 2020

Jeffrey H Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.

Myers, M. B., & Cheung, M. S. (2008). Sharing global supply chain knowledge. MIT Sloan Management Review49(4), 67.

Wang, C., & Hu, Q. (2020). Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance. Technovation94, 102010.

 

10 Ways Enterprises Are Getting Results From AI Strategies

10 Ways Enterprises Are Getting Results From AI Strategies

  • One in 10 enterprises now use 10 or more AI applications; chatbots, process optimization, and fraud analysis lead a recent survey’s top use cases according to MMC Ventures.
  • 83% of IT leaders say AI & ML is transforming customer engagement, and 69% say it is transforming their business according to Salesforce Research.
  • IDC predicts spending on AI systems will reach $97.9B in 2023.

AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing.

What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value. There are various frameworks available, with BMC’s Autonomous Digital Enterprise (ADE) encapsulating what enterprises need to scale out their AI pilots into production. What’s unique about BMC’s approach is its focus on delivering transcendent customer experiences by creating an ecosystem that uses technology to cater to every touchpoint on a customer’s journey, across any channel a customer chooses to interact with an enterprise on.

10 Areas Where AI Is Delivering Proven Value Today

Having progressed from pilot to production across many of the world’s leading enterprises, they’re great examples of where AI is delivering value today. The following are 10 areas where AI is delivering proven value in enterprises today

  • Customer feedback systems lead all implementations of AI-based self-service platforms. That’s consistent with the discussions I’ve had with manufacturing CEOs who are committed to Voice of the Customer (VoC) programs that also fuel their new product development plans. The best-run manufacturers are using AI to gain customer feedback better also to improve their configure-to-order product customization strategies as well. Mining contact center data while improving customer response times are working on AI platforms today. Source: Forrester study, AI-Infused Contact Centers Optimize Customer Experience Develop A Road Map Now For A Cognitive Contact Center.
  • McKinsey finds that AI is improving demand forecasting by reducing forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. McKinsey’s initial use case analysis is finding that AI can reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively. With AI, 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? McKinsey & Company.

10 Ways Enterprises Are Getting Results From AI Strategies

  • The majority of CEOs and Chief Human Resource Officers (CHROs) globally plan to use more AI within three years, with the U.S. leading all other nations at 73%. Over 63% of all CEOs and CHROs interviewed say that new technologies have a positive impact overall on their operations. CEOs and CHROs introducing AI into their enterprises are doing an effective job at change management, as the majority of employees, 54%, are less concerned about AI now that they see its benefits. C-level executives who are upskilling their employees by enabling them to have stronger digital dexterity skills stand a better chance of winning the war for talent. Source: Harris Interactive, in collaboration with Eightfold Talent Intelligence And Management Report 2019-2020 Report.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where AI can contribute to solving complex constraints, cost, and delivery problems manufacturers are facing today. For example, AI is providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as 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).
  • A semiconductor manufacturer is combining smart, connected machines with AI to improve yield rates by 30% or more, while also optimizing fab operations and streamlining the entire production process. They’ve also been able to reduce supply chain forecasting errors by 50% and lost sales by 65% by having more accurate product availability, both attributable to insights gained from AI. They’re also automating quality testing using machine learning, increasing defect detection rates up to 90%. These are the kind of measurable results manufacturers look for when deciding if a new technology is going to deliver results or not. These and many other findings from the semiconductor’s interviews with McKinsey are in the study, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? . The following graphic from the study illustrates the many ways AI and machine learning are improving semiconductor manufacturing.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.
  • AI is reducing logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration, when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra.
  • Detecting and acting on inconsistent supplier quality levels and deliveries using AI-based applications is reducing the cost of bad quality across electronic, high-tech, and discrete manufacturing. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. Using AI, manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery, and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour.

10 Ways Enterprises Are Getting Results From AI Strategies

  • Optimizing Shop Floor Operations with Real-Time Monitoring and AI is in production at Hitachi today. Combining real-time monitoring and AI to optimize shop floor operations, providing insights into machine-level loads and production schedule performance, is now in production at Hitachi. 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 AI.  Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics, and AI Are Making Factories Intelligent and Agile, Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D.

10 Ways Enterprises Are Getting Results From AI Strategies

Additional reading:

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

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,

DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

2018 (43 pp., PDF, free, no opt-in).

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

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)

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

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

bes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

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

McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

McKinsey & Company, Notes from the AI frontier: Modeling the impact of AI on the world economy, September 2018 By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control28(11/12), 873-876.

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

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

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

World Economic Forum, Supply Chain 4.0 Global Practices, and Lessons Learned for Latin America and the Caribbean (PDF, 44 pp., no opt-in)

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

 

Six Areas Where AI Is Improving Customer Experiences

Six Areas Where AI Is Improving Customer Experiences

Bottom Line: This year’s hard reset is amplifying how vital customer relationships are and how much potential AI has to find new ways to improve them.

  • 30% of customers will leave a brand and never come back because of a bad experience.
  • 27% of companies say improving their customer intelligence and data efforts are their highest priority when it comes to customer experience (CX).
  • By 2023, 30% of customer service organizations will deliver proactive customer services by using AI-enabled process orchestration and continuous intelligence, according to Gartner.
  • $13.9B was invested in CX-focused AI and $42.7B in CX-focused Big Data and analytics in 2019, with both expected to grow to $90B in 2022, according to IDC.

The hard reset every company is going through today is making senior management teams re-evaluate every line item and expense, especially in marketing. Spending on Customer Experience is getting re-evaluated as are supporting AI, analytics, business intelligence (BI), and machine learning projects and spending. Marketers able to quantify their contributions to revenue gains are succeeding the most at defending their budgets.

Fundamentals of CX Economics

Knowing if and by how much CX initiatives and strategies are paying off has been elusive. Fortunately, there are a variety of benchmarks and supporting methodologies being developed that contextualize the contribution of CX. KPMG’s recent study, How Much Is Customer Experience Worth? provides guidance in the areas of CX and its supporting economics. The following table provides an overview of key financial measures’ interrelationships with CX. The table below summarizes their findings:

The KPMG study also found that failing to meet customer expectations is two times more destructive than exceeding them. That’s a powerful argument for having AI and machine learning ingrained into CX company-wide. The following graphic quantifies the economic value of improving CX:

Six Areas Where AI Is Improving Customer Experiences

 

Where AI Is Improving CX

For AI projects to make it through the budgeting crucible that the COVID-19 pandemic has created, they’re going to have to show a contribution to revenue, cost reduction, and improved customer experiences in a contactless world. Add in the need for any CX strategy to be on a resilient, proven platform and the future of marketing comes into focus. Examples of platforms and customer-centric digital transformation networks that can help re-center an organization on data- and AI-driven customer insights include BMC’s Autonomous Digital Enterprise (ADE) and others. The framework is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to improve every aspect of the customer (CX) and  employee experience (EX). BMC believes that providing employees with the digital resources they need to excel at their jobs also delivers excellent customer experiences.

Having worked my way through college in customer service roles, I can attest to how valuable having the right digital resources are for serving customers What I like about their framework is how they’re trying to go beyond just satisfying customers, they’re wanting to delight them. BMC calls this delivering a transcendent customer experience. From my collegiate career doing customer service, I recall the e-mails delighted customers sent to my bosses that would be posted along a wall in our offices. In customer service and customer experience, you get what you give. Having customer service reps like my younger self on the front line able to get resources and support they need to deliver more authentic and responsive support is key. I see BMC’s ADE doing the same by ensuring a scalable CX strategy that retains its authenticity even as response times shrink and customer volume increases.

The following are six ways AI can improve customer experiences:

  • Improving contactless personalized customer care is considered one of the most valuable areas where AI is improving customer experiences. 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. (PDF, 28 pp).

Six Areas Where AI Is Improving Customer Experiences

  • Anticipating and predicting how each customers’ preferences of where, when, and what they will buy will change and removing roadblocks well ahead of time for them. Reducing the friction customers face when they’re attempting to buy within a channel they’ve never purchased through before can’t be left to chance. Using augmented, predictive analytics to generate insights in real-time to customize the marketing mix for every individual Customer improves sales funnels, preserves margins, and can increase sales velocity.
  • Knowing which customer touchpoints are the most and least effective in improving CX and driving repurchase rates. Successfully using AI to improve CX needs to be based on data from all trackable channels that prospects and customers interact with. Digital touchpoints, including mobile app usage, social media, and website visits, all need to be aggregated into data sets ML algorithms to use to learn more about every Customer continually and anticipate which touchpoint is the most valuable to them and why. Knowing how touchpoints stack up from a customer’s point of view immediately says which channels are doing well and which need improvement.
  • Recruiting new customer segments by using CX improvements to gain them as prospects and then convert them to customers. AI and ML have been used for customer segmentation for years. Online retailers are using AI to identify which CX enhancements on their mobile apps, websites, and customer care systems are the most likely to attract new customers.
  • Retailers are combining personalization, AI-based pattern matching, and product-based recommendation engines in their mobile apps enabling shoppers to try on garments they’re interested in buying virtually. Machine learning excels at pattern recognition, and AI is well-suited for fine-tuning recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.

Six Areas Where AI Is Improving Customer Experiences

  • Relying on AI to best understand customers and redefine IT and Operations Management infrastructure to support them is a true test of how customer-centric a business is. Digital transformation networks need to support every touchpoint of the customer experience. They must have AI and ML designed to anticipate customer needs and deliver the goods and services required at the right time, via the Customer’s preferred channel. BMC’s Autonomous Digital Enterprise Framework is a case in point. Source: Cognizant, The 2020 Customer Experience.

Six Areas Where AI Is Improving Customer Experiences

Additional Resources

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.

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.

Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227-243.

David Simchi-Levi

Earley, S. (2017). The Problem of Personalization: AI-Driven Analytics at Scale. IT Professional, 19(6), 74-80.

Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,

Gacanin, H., & Wagner, M. (2019). Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives. IEEE Network, 33(2), 188-194.

Hildebrand, C., & Bergner, A. (2019). AI-Driven Sales Automation: Using Chatbots to Boost Sales. NIM Marketing Intelligence Review11(2), 36-41.

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

Kaczmarek, J., & Ryżko, D. (2009). Quantifying and optimising user experience: Adapting AI methodologies for Customer Experience Management.

KPMG, Customer first. Customer obsessed. Global Customer Experience Excellence report, 2019 (92 pp., PDF)

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

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

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

OpenText, AI in customer experience improves loyalty and retention (11 pp., PDF)

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

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

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

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

How To Reduce The Unemployment Gap With AI

How To Reduce The Unemployment Gap With AI

It’s time for AI startups to step up and use their formidable technology expertise in AI to help get more Americans back to work now.

Bottom Line: A.I.’s ability to predict and recommend job matches will help get more Americans back to work, helping to reduce the 22 million unemployed today.

One in ten Americans is out of work today based latest U.S. Department of Labor data. They’re primarily from the travel and hospitality, food services, and retail trade and manufacturing industries, with many other affected sectors. McKinsey & Company’s recent article, A new AI-powered network, is helping workers displaced by the coronavirus crisis provides context around the scope of challenges involved in closing the unemployment gap. McKinsey, Eightfold A.I., and the FMI – The Food Industry Association combined efforts to create the Talent Exchange, powered by Eightfold.ai in a matter of weeks. McKinsey insights across a broad base of industries to help Eightfold and FMI create the Talent Exchange in record time. “In talking with clients across the U.S., it became very clear that there is a huge labor mismatch, and individuals are being affected very differently—from retailers furloughing tens of thousands of workers to other organizations needing to hire more than 100,000 workers quickly. We’re excited to help bring a scalable offering to the market,” said McKinsey partner Andrew Davis. McKinsey and FMI collaborating with Eightfold speak volumes to how Americans are coming together to combat the COVID-19 fallout as a team.

And with the food & agriculture, transportation, and logistics industries considered essential, critical infrastructure by Cybersecurity and Infrastructure Security Agency (CISA), demand for workers is more urgent than ever. Eightfold’s Talent Exchange launched last weekend and already has more than 600,000 jobs uploaded that employers need to fill and is available in 15 languages. Eightfold is making the Talent Exchange available free of charge through the COVID-19 epidemic. The Talent Exchange is also being extended to other industries and eco-systems, illustrating how the Eightfold A.I. platform can provide transferability of skills across roles and industries.

Getting Americans Back To Work Using A.I.

Earlier this week Eightfold, FMI – The Food Industry Association and Josh Bersin, the noted global research analyst, public speaker, and writer on many aspects of human resources and talent management, hosted the webinar, COVID-19: Helping the food industry on the front lines with A.I. It’s available to watch here and includes a walk-through of the Eightfold Talent Exchange. The following graphic explains how the Talent Exchange addresses the needs of downsizing companies, impacted workers and hiring companies:

How To Reduce The Unemployment Gap With AI

Eightfold’s Talent Exchange Is A Model For How To Use A.I. For Good

Eightfold’s Talent Exchange uses A.I. algorithms to match candidates with available roles, based on each individual’s skills and previous experience.

Current employers who have to furlough or lay off employees can invite employees to participate in the program. Eightfold also designed in a useful feature that enables employers to add lists of impacted employees and send them a link to register for the Exchange. Employers can view their entire impacted workforce in a single dashboard and can filter by role, department, or location to see details about the talent needs from hiring companies and how their impacted employees are getting placed in new roles. The following is the Talent Exchange dashboard  for current employers showing progress in placing employees with furlough and outplacement partners, including the number of offers accepted by each:

How To Reduce The Unemployment Gap With AI

Employees impacted by a furlough or lay-off can create and update profiles free on the Talent Exchange, defining their job preferences, skills, and experience. That’s invaluable data for hiring companies relying on the platform to make offers and fill positions quickly.  How current employers handle furloughs and lay-offs today will be their identity for years to come, a point John Bersin made during the webinar saying “employers who thrive in the future are going to build long-term relationships with employees today.” Employees receive the following when their current employer adds their name to the Eightfold Talent Exchange. The fictional Company Travel Air is used for this example:

Hiring companies see candidate matches generated by the Exchange, so they can contact these prospects or immediately offer them new jobs. Eightfold’s A.I. engineering teams have automated and personalized this contact as well, expediting the process even further. Hiring companies can add onboarding instructions to allow new hires to start as soon as they are ready and have real-time views of their hiring dashboard shown below:

How To Reduce The Unemployment Gap With AI

Conclusion

Combining A.I.’s innate strengths with H.R. and talent management professionals’ expertise and insights is closing the unemployment gap today. Employers furloughing or laying off employees need to look out for them and get their profile data on the Talent Exchange, helping them find new jobs with hiring companies. As was so well-said by Josh Bersin during the webinar this week, “smart employers should think of their hourly workers as talent, not fungible, replaceable bodies.” For hiring companies in a war for proven employees with talent today, that mindset is more important than ever.

How To Know If An E-Mail Is Trustworthy

How To Know If An E-Mail Is Trustworthy

 

Bottom Line: Phishing is the leading cause of all breaches, succeeding because impersonation, redirection, and social engineering methods are always improving. And, phishing is only one way e-mails are used in fraud. Businesses need to understand if an e-mail address can be trusted before moving forward with a transaction.

Microsoft thwarts billions of phishing attempts a year on Office365 alone by relying on heuristics, detonation, and machine learning, strengthened by Microsoft Threat Protection Services. In 2018 Microsoft blocked 5 billion phish e-mails in Office 365 and detonated 11 billion unique items by ATP sandboxing. Microsoft is succeeding with its cybersecurity partners in defeating phishing attacks. Phishers are going to extraordinary lengths to discover new techniques to evade detection and successfully carry out phishing attempts. By analyzing Office 365 ATP signals, Microsoft sees phishers attempt to abuse many legitimate cloud services, including Amazon, Google, Microsoft Office365, Microsoft Azure, and others. Microsoft is creating processes that identify and destroy phishing attempts without impacting legitimate applications’ performance.

Phishers’ Favorite Trojan Horse Is Office365 Followed By Cybersecurity Companies  

Phishers are hiding malicious links, scripts and, in some cases, mutated software code behind legitimate Microsoft files and code to evade detection. Using legitimate code and links as a Trojan Horse to successfully launch a phishing campaign became very popular in 2019 and continues today. Cybercriminals and state-sponsored hackers have been mutating legitimate code and applications for years attempting to exfiltrate priceless data from enterprises and governments globally. Office365 is the phisher’s Trojan Horse of choice, closely followed dozens of cybersecurity companies that have seen hackers attempt to impersonate their products. Cybersecurity companies targeted include Citrix, Comodo, Imperva, Kaspersky, LastPass, Microsoft, BitDefender, CyberRoam, and others.

Using Trojan Horses To Hijack Search Results

In 2019 Microsoft discovered a sophisticated phishing attack that combined impersonation, redirection, and social engineering methods. The phishing attack relied on using links to Google search results as a Trojan Horse to deliver URLs that were poisoned so that they pointed to an attacker-controlled page, which eventually redirected to the phishing page. Microsoft discovered that a traffic generator ensured that the redirector page was the top result for specific keywords. The following graphic explains how the phishing attack was used to poison search results:

Using this workflow, phishers attempted to send phishing e-mails that relied on legitimate URLs as their Trojan Horses from legitimate domains to take advantage of the recipient’s trust. Knowing which e-mails to trust or not is becoming foundational to stopping fraud and phishing attacks.

How Kount Is Battling Sophisticated Attacks 

Meanwhile, e-mail addresses can be a valuable source of information for businesses looking to prevent digital fraud. Misplaced trust can lead to chargebacks, manual reviews, and other undesirable outcomes. But, Kount’s Real-Time Identity Trust Network calculates Identity Trust Levels in milliseconds, reducing friction, blocking fraud, and delivering improved user experiences. Kount discovered that e-mail age is one of the most reliable identity trust signals there are for identifying and stopping automated fraudulent activity.

Based on their research and product development, Kount announced Email First Seen capabilities as part of its AI-powered Identity Trust Global Network. Email First Seen applies throughout the customer journey, from payments to account login to account creation. The Identity Trust Global Network consists of fraud and trust signals from over half a billion e-mail addresses. It also spans 32 billion annual interactions and 17.5 billion devices across 75 business sectors and 50-plus payment providers and card networks. The network is linked by Kount’s next-generation artificial intelligence (AI) and works to establish real-time trust for each identity behind a payment transaction, log in or account creation

E-mail Age Is Proving To Be A Reliable Indicator Of Trust

A favorite tactic of cybercriminals is to create as many new e-mail aliases as they need to deceive online businesses and defraud them of merchandise and payments. Kount is finding that when businesses can identify the age of an e-mail address, they can more accurately determine identity trust. Kount’s expertise is in fraud prevention effectiveness, relying on a combination of fraud and risk signals to generate a complete picture of authentication details. The following graphic illustrates what a Kount customer using Email First Seen will see in every e-mail they receive.

Kount’s Identity Trust Global Network relies on AI-based algorithms that can analyze all available identifiers or data points to establish real-time links between identity elements, and return identity trust decisions in real-time. Kount’s unique approach to using AI to improve customer experiences by reducing friction while blocking fraud reflects the future of fraud detection. Also, Kount’s AI can discern if additional authentication is needed to verify the identity behind the transaction and relies on half a billion e-mail addresses that are integral to AI-based analysis and risk scoring algorithms. Kount is making Email First Seen available to all existing customers for no charge. It’s been designed to be native on the Kount platform, allowing the information to be accessible in real-time to inform fraud and trust decisions.

Conclusion

In 2020 phishing attempts will increasingly rely on legitimate code, links, and executables as Trojan Horses to evade detection and launch phishing attacks at specific targets. Microsoft’s research and continued monitoring of phishing attempts uncovered architecturally sophisticated approaches to misdirecting victims through impersonation and social engineering.

What You Need To Know About Location Intelligence In 2020

What You Need To Know About Location Intelligence In 2020

  • 53% of enterprises say that Location Intelligence is either critically important or very important to achieving their goals for 2020.
  • Leading analytics and platform vendors who offer Location Intelligence include Alteryx, Microsoft, Qlik, SAS, Tableau and TIBCO Software.
  • Location Intelligence vendors providing specialized apps and platforms include CARTO, ESRI, Galigeo, MapLarge, and Pitney Bowes.
  • Product Managers need to consider how adding Location Intelligence can improve the contextual accuracy of marketing, sales, and customer service apps and platforms.
  • Marketers need to look at how they can capitalize on smartphones’ prolific amounts of location data for improving advertising, buying, and service experiences for customers.
  • R&D, Operations, and Executive Management lead all other departments in their adoption and use of Location Intelligence this year.
  • Enterprises favor cloud-based Location Intelligence deployments in 2020, with on-premise deployments also seeing new sales this year.

These and many other fascinating insights are from Dresner Advisory Services’ 2020 Location Intelligence Market Study, their 7th annual report that examines enterprise end-users’ requirements and features including geocoding support, location intelligence visualization, analytics capabilities, and third-party GIS integration. The study is noteworthy for its depth of insights into industry adoption of Location Intelligence and how user requirements drive industry capabilities. Dresner Advisory Services defines location intelligence as a form of Business Intelligence (BI), where the dominant dimension used for analysis is location or geography. Most typically, though not exclusively, analyses are conducted by viewing data points overlaid onto an interactive map interface.

“When we began covering Location Intelligence in 2014, we saw the potential for the topic to gain mainstream interest,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “With the growth in visualization and the emergence of the Internet of Things (IoT), incorporating maps and location into business analyses have become increasingly important to many organizations.” Please see page 11 for a description of the methodology and page 13 for an overview of study demographics. Wisdom of Crowds® research is based on data collected on usage and deployment trends, products, and vendors.

Key insights from the study that provides an excellent background on the current state of location intelligence in 2020 include the following:

  • R&D, Operations, and Executive Management lead all enterprise areas in adoption with Location Intelligence being considered critical to their ongoing operations. The majority of Marketing & Sales leaders see Location Intelligence as very important to their ongoing operations. The following graphic compares how important Location Intelligence is to each of the seven departments included in the survey:
  • 90% of Government organizations consider Location Intelligence to be critical or very important to their ongoing operations. Healthcare providers have the second-highest number of organizations who rate Location Intelligence as critical. The study found that mean importance levels are similar across Business Services, Financial Services, Manufacturing, and Consumer Services organizations and decline further among Technology, Retail/Wholesale, and Higher Education segments.
  • Data visualization/mapping dominates all other Location Intelligence use cases in 2020, with over 70% of organizations considering it critical or very important to accomplishing their goals. The study found that the majority of other use cases haven’t achieved the broad adoption data visualization & mapping has. Despite the lower levels of criticality assigned to the nine other use cases, they each show the potential to streamline essential marketing, sales, and operational areas of an enterprise. Site planning/site selection, geomarketing, territory management/optimization, and logistics optimization make up a tier of secondary interest that taken together streamlines supply chains while making an organization easier to buy from. The Dresner research team also defines the third tier of use cases led by fleet routing and citizen services, followed by IoT & smart cities, indoor mapping, and real estate investment/pricing analysis. Despite IoT being over-promoted by vendors, just over 50% of enterprises say the technology is not important to them at this time. The following graphic compares Location Intelligence use cases by the level of criticality as defined by responding organizations:
  • R&D leads all departments in data visualization/mapping adoption, reflecting the high level of importance this use case has across entire enterprises as well. Additional departments and functional areas relying on data visualization/mapping include Operations, Business Intelligence Competency Center (BICC), and Executive Management. Geomarketing is seeing the most significant adoption in Marketing & Sales. Operations lead all other functional areas in the adoption of logistics optimization and fleet routing use cases. Dresner’s research team found that R&D’s interest in Location Intelligence, which varies across use cases, may reflect the use of packaged applications as well as select custom development.
  • Map-based visualization, dashboard inclusion of maps, and drill-down navigation through map interfaces are the three highest priority features enterprises look for today. These three features are considered very important to between 64% to 67% of leaders interviewed. Layered visualizations, multi-layer support, and custom region definition are the next most important features. The following graphic provides an overview of prioritized Location intelligence visualization features.
  • Executive Management, BICC, and Operations have the highest level of interest in map-based visualizations that further accelerate the adoption of Location Intelligence across enterprises. Executive Management also leads all others in their interest in dashboard inclusion of maps and custom map support. Executive Management’s increasing adoption of multiple Location intelligence use cases is a catalyst driving greater enterprise-wide adoption. R&D’s prioritizing the layering of visualizations on top of maps, offline mapping and animation of data on maps are leading indicators of these use cases attaining greater enterprise adoption in future years.
  • Four of the top ten Location Intelligence features are considered very important/critical to enterprises, reflecting a maturing market. The most popular (counting, quantifying, or grouping) is critical or very important to 46% of organizations and at least important to nearly 70%. Another indicator of how quickly Location Intelligence is maturing in enterprises is the advanced nature of analytics features being relied on today. Predicting trends and volatility, detecting clusters and outliers, and measuring distances reflect how multiple departments in enterprises are collaborating using Location Intelligence to achieve their shared goals.
  • Government dominates the use of data visualization/mapping with a strong interest in site planning/site selection, citizen services, fleet routing, and territory management. Business Services are most interested in using Location Intelligence for Indoor Mapping and IoT & Smart Cities. Geomarketing is the most adopted feature in Higher Education, Financial Services, Healthcare, and Retail/Wholesale. Manufacturing and Retail/Wholesale lead all other industries in their adoption of Logistics Optimization. The following graphic provides insights into Location Intelligence use case by industry:
  • Executive Management and Business Intelligence Competency Centers (BICC) most prioritize Location Intelligence applications that have built-in or native geocoding. Enterprises are looking at how built-in or native geocoding can scale across their Location Intelligence use cases and broader BI strategy with Executive Management taking the lead on achieving this goal. Automated geocoding support and street-level geocoding support are also a high priority to Executive Management. Marketing/Sales lead all other departments in their interest in geofencing/reverse geofencing, indicating enterprises are beginning to use these geocoding features to achieve greater accuracy in their marketing and selling strategies. It’s interesting to note that geofencing/reverse geofencing has progressed from R&D in previous studies to Marketing/Sales putting the highest priority on it today. Dresner’s research team interprets the shift to customer-facing strategies being an indicator of broader enterprise adoption for geofencing/reverse geofencing.
  • 61% of organizations say Google integration is essential to their Location Intelligence strategies. Google continues to dominate organizations’ roadmaps as the integration of choice for adding more GIS data to Location Intelligence strategies. ESRI is the second choice with 45% of organizations naming it as an integration requirement. Database extensions (30%) are the next most cited, followed by OpenStreetMap (20%). All other choices are requirements at less than 20% of organizations.

How To Redefine The Future Of Fraud Prevention

How To Redefine The Future Of Fraud Prevention

Bottom Line: Redefining the future of fraud prevention starts by turning trust into an accelerator across every aspect of customer lifecycles, basing transactions on identity trust that leads to less friction and improved customer experiences.

Start By Turning Trust Into A Sales & Customer Experience Accelerator

AI and machine learning are proving to be very effective at finding anomalies in transactions and scoring, which are potentially the most fraudulent. Any suspicious transaction attempt leads to more work for buying customers to prove they are trustworthy. For banks, e-commerce sites, financial institutes, restaurants, retailers and many other online businesses, this regularly causes them to lose customers when a legitimate purchase is being made, and trusted customer is asked to verify their identity. Or worse, a false positive that turns away a good customer all together damages both that experience and brand reputation.

There’s a better way to solve the dilemma of deciding which transactions to accept or not. And it needs to start with finding a new way to establish identity trust so businesses can deliver better user experiences. Kount’s approach of using their Real-Time Identity Trust Network to calculate Identity Trust Levels in milliseconds reduces friction, blocks fraud, and delivers an improved user experience. Kount is capitalizing on their database that includes more than a decade of trust and fraud signals built across industries, geographies, and 32 billion annual interactions, combined with expertise in AI and machine learning to turn trust into a sales and customer experience multiplier.

How Real-Time AI Linking Leads To Real-Time Identity Trust Decisions

Design In Identity Trust So It’s The Foundation of Customer Experience

From an engineering and product design standpoint, the majority of fraud prevention providers are looking to make incremental gains in risk scoring to improve customer experiences. None, with the exception of Kount, are looking at the problem from a completely different perspective, which is how to quantify and scale identity trust. Kount’s engineering, product development, and product management teams are concentrating on how to use their AI and machine learning expertise to quantify real-time identity trust scores that drive better customer experiences across the spectrum of trust. The graphic below illustrates how Kount defines more personalized user experiences, which is indispensable in turning trust into an accelerator.

An Overview of Kount’s Technology Stack

How To Redefine The Future Of Fraud Prevention

Realize Trust Is the Most Powerful Revenue Multiplier There Is

Based on my conversations with several fraud prevention providers, they all agree that trust is the most powerful accelerator there is to reducing false positives, friction in transactions, and improving customer experiences. They all agree trust is the most powerful revenue multiplier they can deliver to their customers, helping them reduce fraud and increase sales. The challenge they all face is quantifying identity trust across the wide spectrum of transactions their customers need to fulfill every day.

Kount has taken a unique approach to identity trust that puts the customer at the center of the transactions, not just their transactions’ risk score. By capitalizing on the insights gained from their Identity Trust Global Network, Kount can use AI and machine learning algorithms to deliver personalized responses to transaction requests in milliseconds. Using both unsupervised and supervised machine learning algorithms and techniques, Kount can learn from every customer interaction, gaining new insights into how to fine-tune identity trust for every customer’s transaction.

In choosing to go in the direction of identity trust in its product strategy, Kount put user experiences at the core of their platform strategy. By combining adaptive fraud protection, personalized user experience, and advanced analytics, Kount can create a continuously learning system with the goal of fine-tuning identity trust for every transaction their customers receive. The following graphic explains their approach for bringing identity trust into the center of their platform:

Putting Customers & Their Experiences First Is Integral To Succeeding With Identity Trust

How To Redefine The Future Of Fraud Prevention

 

Improving customer experiences needs to be the cornerstone that drives all fraud prevention product and services road maps in 2020 and beyond. And while all fraud prevention providers are looking at how to reduce friction and improve customer experiences with fraud scoring AI-based techniques, their architectures and approaches aren’t going in the direction of identity trust. Kount’s approach is, and it’s noteworthy because it puts customer experiences at the center of their platform. How to redefine the future of fraud prevention needs to start by turning trust into a sales and customer experience accelerator, followed by designing in identity trust. Hence, it’s the foundation of all customer experiences. By combining the power of networked data and adaptive AI and machine learning, more digital businesses can turn trust into a revenue and customer experience multiplier.

Five Factors Predicting The Future Of MacOS Management And Security

Bottom Line: Going into 2020, CISOs’ sense of urgency for managing their fleets of Android, Apple iOS & macOS, Windows Phone, and Windows 10 devices all from an integrated Unified Endpoint Management (UEM) is transforming the MacOS Management and Security landscape.

For many, CISOs, the highest priority project they’re starting the New Year with is getting their diverse fleet of devices on a common unified endpoint management platform. “We’ve gone through no less than a dozen UEMs (Unified Endpoint Management) systems, and they are either very good at supporting iOS and macOS or terrible at every other operating system or vice versa,” the CISO of a leading insurance and financial services firm told me over lunch recently. “Our sales, marketing, graphic artists, DevOps, and Customer Success teams all are running on Macs and iPhones, which makes it even more of a challenge to get everyone on the same endpoint management platform.” He went on to explain that the majority of macOS and iOS endpoint management systems aren’t built to support the advanced security he needs for protecting Android, Windows Phone, and Windows 10 devices.

Unified Endpoint Management is a key CISO priority in 2020

macOS and iOS devices had their own endpoint management tools in previous years when they were limited in use. Now they’re common in the enterprise and need to be considered part of an organization-wide fleet of devices, making it a high priority to add them to the unified endpoint management platform all other devices are on. Further accelerating this change is the success of BYOD policies that give employees the choice of using the tablets, smartphones, and laptops they’re the most productive with. One CISO told me their BYOD program made it clear macOS and iOS are the de facto standard across their enterprise.

While endpoint management platforms are going through an Apple-driven inflection point, forcing the need for a more inclusive unified endpoint management strategy, CISOs are focusing on how to improve application and content control at the same time. How enterprises choose to solve that challenge are predicting the future of MacOS management and security.

Five Factors Driving the Future of macOS Management and Security

CISOs piloting and only buying platforms that can equally protect every device operating system, macOS, and iOS’ rapidly growing enterprise popularity and better support for adaptive access are a few of the catalysts redefining the landscape today. The following five factors are defining how MacOS Management and Security will improve in 2020:

  • Enterprises need more effective endpoint and application management that includes Android, Apple iOS & macOS, Windows Phone, and Windows 10. There’s a major gap in how effective endpoint protection is across the UEM platforms today. Data-at-risk encryption and App distribution, or how well a UEM system can create, update, and distribute macOS applications are two areas cybersecurity teams are focusing on today.

Five Factors Predicting The Future Of MacOS Management And Security

  • System integration options needs to extend beyond log reports and provide real-time links to Security Information and Event Management (SIEM) systems. CISOs and their cybersecurity teams need real-time integration to incident management systems so they can be more effective troubleshooting potential breach attempts. Sharing log files across other systems is a first step, yet real-time integration is clearly what’s needed to protect enterprises’ many devices and threat surfaces today. The following Splunk dashboard illustrates the benefits of having real-time integration beyond log reports, encompassing SIEM systems:

Five Factors Predicting The Future Of MacOS Management And Security

  • UEM platforms that differentiate between corporate-owned and personal devices, content and authentication workflows, and data are defining the future of macOS Management and Security. Key factors that CISOs need in this area of unmanaged device support include more effective content separation, improved privacy settings, support for actions taken on personally-owned devices, and role-based privacy settings. MobileIron is a leader in this area, with enterprises currently using their role-based workflows to limit and verify access to employee-owned devices. MobileIron can also limit IT’s scope of control over an employee device, including turning off location tracking.
  • Support and proven integration of Identity solutions such as Okta, Ping Identity, Microsoft, and Single sign-on (SSO) are defining the future of adaptive access today. This is the most nascent area of UEM platform development today, yet the one area that CISOs need the greatest progress on this year. Endpoint protection and system integration are the two areas that most define how advanced a given UEM providers’ platform is today.
  • The ability to provision, revoke, and manage device certificates over their lifecycles is becoming a must-have in enterprises today. UEM platforms, in large part, can handle certificate device provisioning, yet Certificate Authority (CA) integration is an area many struggle with. CISOs are asking for more effective certificate lifecycle management, especially given the proliferation of macOS and iOS devices.

Conclusion

The five factors of MacOS management and security are transforming the Unified Endpoint Management (UEM) solution landscape. CISOs often speak of wanting to have a more integrated UEM strategy, one that can provide better SIEM system integration, differentiate between corporate-owned and personal devices, and also manage the lifecycles of device certificates. MobileIron has proven their ability to scale in a BYOD world and is a UEM vendor to watch in 2020.

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence

Bottom Line: By securing every endpoint with a persistent connection and the resiliency to autonomously self-heal, CIOs are finding new ways to further improve network security by capitalizing on each IT assets’ intelligence.

Capturing real-time data from IT assets is how every organization can grow beyond its existing boundaries with greater security, speed, and trust. Many IT and cybersecurity teams and the CIOs that lead them, and with whom I’ve spoken with, are energized by the opportunity to create secured perimeterless networks that can flex in real-time as their businesses grow. Having a persistent connection to every device across an organizations’ constantly changing perimeter provides invaluable data for achieving this goal. The real-time data provided by persistent device connections give IT and cybersecurity teams the Asset Intelligence they need for creating more resilient, self-healing endpoints as well.

How Asset Intelligence Drives Stronger Endpoint Security 

Real-time, persistent connections to every device in a network is the foundation of a strong endpoint security strategy. It’s also essential for controlling device operating expenses (OPEX) across the broad base of device use cases every organization relies on to succeed. Long-term persistent connections drive down capital expenses (CAPEX) too, by extending the life of every device while providing perimeterless growth of the network. By combining device inventory and analysis, endpoint data compliance with the ability to manage a device fleet using universal asset management techniques, IT and cybersecurity teams are moving beyond Asset Management to Asset Intelligence. Advanced analytics, benchmarks, and audits are all possible across every endpoint today. The following are the 10 ways Asset Intelligence improves cybersecurity resiliency and persistence:

  • Track, trace and find lost or stolen devices on or off an organizations’ network in real-time, disabling the device if necessary. Every device, from laptops, tablets, and smartphones to desktops and specialized use devices are another threat surface that needs to be protected. Real-time persistent connections to each of these devices make track-and-trace possible, giving CIOs and their teams more control than had been possible before. Real-time track-and-trace data combined with device condition feedback closes security blind spots too. IT and cybersecurity teams can monitor every device and know the state of hardware, software, network and use patterns from dashboards. Of the endpoint providers in this market, Absolute’s approach to providing dashboards that provide real-time visibility and control of every device on a network is considered state-of-the-art. An example of Absolute’s dashboard is shown below:

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence

  • Asset Intelligence enables every endpoint to autonomously self-heal themselves and deliver constant persistence across an organization’s entire network. By capitalizing on the device, network, threat, and use data that defines Asset Intelligence, endpoint agents learn over time how to withstand breach attempts, user errors, and malicious attacks, and most importantly, how to return an endpoint device to its original safe state. Asset Intelligence is the future of endpoint security as it’s proving to be very effective at enabling self-healing persistence across enterprise networks.
  • Asset Intelligence solves the urgent problem created from having 10 or more agents installed on a single endpoint that collide, conflict and decay how secure the endpoint is. Absolute Software’s 2019 Endpoint Security Trends Report found that the more agents that are added to an endpoint, the greater the risk of a breach. Absolute also found that a typical device has ten or more endpoint security agents installed, often colliding and conflicting with the other. MITRE’s Cybersecurity research practice found there are on average, ten security agents on each device, and over 5,000 common vulnerabilities and exposures (CVEs) found on the top 20 client applications in 2018 alone.
  • Asset Intelligence sets the data foundation for achieving always-on persistence by tracking every devices’ unique attributes, identifiers, communication log history and more. Endpoint security platforms need a contextually-rich, real-time stream of data to know how and when to initialize the process of autonomously healing a given endpoint device. Asset Intelligence provides the centralized base of IT security controls needed for making endpoint persistence possible.
  • Having a real-time connection to every device on a perimeterless network contributes to creating a security cloud stack from the BIOS level that delivers persistence for every device. CIOs and CISOs interested in building secured perimeterless networks are focused on creating persistent, real-time connections to every device as a first step to creating a security cloud stack from each devices’ BIOS level. They’re saying that the greater the level of Asset Intelligence they can achieve, the broader they can roll out persistence-based endpoints across their networks that have the capacity to self-diagnose and self-heal.
  • Device fleets are churning 20% a year or more, increasing the urgency CIOs have for knowing where each device is and its current state, further underscoring Asset Intelligence’s value. Gavin Cockburn of ARUP is the global service lead for workplace automation and endpoint management, including how the firm acquires devices, manages and reclaims them. ARUP is using the Absolute Persistence platform for managing the many high-value laptops and remote devices their associates use on global projects. During a recent panel discussion he says that device replacements “becomes part of our budgeting process in that 33% of devices that we do replace every year, we know where they are.” Gavin is also using API calls to gain analytical data to measure how devices are being used, if the hard drive is encrypted or not and run Reach scripts to better encrypt a device if there is not enough security on them.
  • The more Asset Intelligence an organization has, the more they can predict and detect malware intrusion attempts, block them and restore any damage to any device on their perimeter. When there’s persistent endpoint protection across a perimeterless network, real-time data is enabling greater levels of Asset Intelligence which is invaluable in identifying, blocking and learning from malware attempts on any device on the network. Endpoint protection platforms that have persistence designed in are able to autonomously self-heal back to their original state after an attack, all without manual intervention.
  • Persistent endpoints open up the opportunity of defining geofencing for every device on a perimeterless network, further providing valuable data Asset Intelligence platforms capitalize on. Geofencing is proving to be a must-have for many organizations that have globally-based operations, as their IT and cybersecurity teams need to track the device location, usage, and compliance in real-time. Healthcare companies are especially focused on how Asset Intelligence can deliver geofencing at scale. Janet Hunt, Senior Director, IT User Support at Apria Healthcare recently commented during a recent panel discussion that “our geo-fencing is extremely tight. I have PCs that live in the Philippines. I have PCs that live in India. I have one PC or actually two PCs that live in Indonesia. If somebody goes from where they say that they’re going to be to another part of Indonesia, that device will freeze because that’s not where it’s supposed to be and that’s an automatic thing. Don’t ask forgiveness, don’t ask questions, freeze the device and see what happens. It’s one of the best things we’ve done for ourselves.”  Gavin Cockburn says, “We actually do some kind of secretive work, government work and we have these secure rooms, dotted around the organization. So we know if we put a device in that room, what we do is, what we say is this device only works in this area and we can pinpoint that to a pretty decent accuracy.”  From healthcare to secured government contracting, geofencing is a must-have in any persistent endpoint security strategy.
  • Automating customer and regulatory audits and improving compliance reporting by relying on Asset Intelligence alleviates time-consuming tasks for IT and cybersecurity teams. When persistent endpoint protection is operating across an organization’s network, audit and compliance data is captured in real-time and automatically fed into reporting systems and dashboards. CIOs and their cybersecurity teams are using dashboards to monitor every device’s usage patterns, audit access, and application activity, and check for compliance to security and reporting standards. Audits and compliance reporting are being automated today using PowerShell, BASH scripts and API-based universal asset commands. Gavin Cockburn of ARUP mentioned how his firm gives customers the assurance their data is safe by providing them ongoing audits while project engagements are ongoing. “We need to show for our clients that we look after their data and we can prove that. And we show that again and again. I mean similar story, we’ve seen machines go missing, either breaking into cars, re-image three times. We wipe it every time. Put the new hard drive in, think it might be a hard drive issue, it wipes again. We never see it come online again, “ he said.
  • Asset Intelligence improves data hygiene, which has a direct effect on how effective all IT systems are and the customer experiences they deliver. CIOs and their teams’ incentives center on how effective IT is at meeting internal information needs that impact customer experiences and outcomes. Improving data hygiene is essential for IT to keep achieving their incentive plans and earning bonuses. As Janet Hunt, Senior Director, IT User Support at Apria Healthcare said, “right now we are all about hygiene and what I mean by that is we want our data to be good. We want all the things that make IT a valued partner with the business operation to be able to be reliable.” The more effective any organization is at achieving and sustaining a high level of data hygiene, the more secure their perimeterless network strategies become.

 

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

  • Duolingo, HOVER, Ironclad, Orbital Insight, People.ai, Dataiku, DeepMap, Cobalt, Aktana, Chorus.ai, Noodle Analytics, Inc. (Noodle.ai), Signal AI, Augury, SparkCognition, and KONUX are the most likely to be recommended by their employees to friends looking for a machine learning startup to work for in 2020.
  • 96% of the employees of the 15 highest rated machine learning startups would recommend their company to a friend looking for a new job, and 98% approve of their CEOs.
  • Across all machine learning startups with Glassdoor ratings, 74% of employees would recommend the startup they work for to a friend, and 81% approve of their CEO.
  • There are over 230 cities globally who have one or more machine learning startups in operation today with Crunchbase finding 144 in San Francisco, 60 in London, 69 in New York, 82 in Tel Aviv, 22 in Toronto, 20 in Paris, 18 in Seattle and the remainder distributed over 223 global locations.

These and many other insights are from a Crunchbase Pro analysis completed today using Glassdoor data to rank the best machine learning startups to work for in 2020. Demand reminds high for technical professionals with machine learning expertise.  According to Indeed, Machine Learning Engineer job openings grew 344% between 2015 to 2018 and have an average base salary of $146,085 according to their  Best Jobs In The U.S. Study. You can read the study shows that technical professionals with machine learning expertise are in an excellent position to bargain for the average base salary of at least $146,085 or more.

Methodology

In response to readers’ most common requests of which machine learning startups are the best to work for, a Crunchbase Pro query was created to find all machine learning startups who had received Seed, Early Stage Venture, or Late Stage Venture financing. The 2,682 machine learning startups Crunchbase is tracking were indexed by Total Funding Amount by startup to create a baseline.

Next, Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO were used to find the best startups to work for. 79 of the 150 machine learning startups have 15 or more Glassdoor reviews and are included in the analysis. 41 have less than 15 reviews and 30 have no reviews. The table below is a result of the analysis, and you can find the original Microsoft Excel data set here.

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

 

 

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