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Posts from the ‘Louis Columbus’ blog’ Category

CPQ Needs To Scale And Support Smarter, More Connected Products

  • For smart, connected product strategies to succeed they require a product lifecycle view of configurations, best attained by integrating PLM, CAD, CRM, and ERP systems.
  • Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020.
  • In 2018, $985B will be spent on IoT-enabled smart consumer devices, soaring to $1.49B in 2020, attaining a 23.1% compound annual growth rate (CAGR) according to Statista.
  • Industrial manufacturers will spend on average $121M a year on smart, connected products according to Statista.

Succeeding with a smart, connected product strategy is requiring manufacturers to accelerate their IoT & software development expertise faster than they expected. By 2020, 50% of manufacturers will generate the majority of their revenues from smart, connected products according to Capgemini’s recent study. Manufacturers see 2019 as the breakout year for smart, connected products and the new revenue opportunities they provide.

Industrial Internet of Things (IIoT) platforms has the potential of providing a single, unified data model across an entire manufacturing operation, giving manufacturers a single unified view of product configurations across their lifecycles. Producing smart, connected products at scale also requires a system capable of presenting a unified view of configurations in the linguistics each department can understand. Engineering, production, marketing, sales, and service all need a unique view of product configurations to keep producing new products. Leaders in this field include Configit and their Configuration Lifecycle Management approach to CPQ and product configuration.

Please see McKinsey’s article IIoT platforms: The technology stack as a value driver in industrial equipment and machinery which explores how the Industrial Internet of things (IIoT) is redefining industrial equipment and machinery manufacturing. The following graphic from the McKinsey explains why smart, connected product strategies are accelerating across all industries. Please click on the graphic to expand it for easier reading.

CPQ Needs To Scale Further To Sell Smart, Connected Products

Smart, connected products are redefining the principles of product design, manufacturing, sales, marketing, and service. CPQ systems need to grow beyond their current limitations by capitalizing on these new principles while scaling to support new business models that are services and subscription-based.

The following are the key areas where CPQ systems are innovating today, making progress towards enabling the custom configuration of smart, connected products:

  • For smart, connected product strategies to succeed they require a product lifecycle view of configurations, best attained by integrating PLM, CAD, CRM, and ERP systems. Smart, connected product strategies require real-time integration between front-end and back-end systems to optimize production performance. And they also require advanced visualization that provides prospects with an accurate, 3D-rendered view that can be accurately translated to a Bill of Materials (BOM) and into production. The following graphic is based on conversations with Configit customers, illustrating how they are combining PLM, CAD, CRM and ERP systems to support smart, connected products related to automotive manufacturing. Please click on the graphic to expand it for easier reading.

  • CPQ and product configuration systems need to reflect the products they’re specifying are part of a broader ecosystem, not stand-alone. The essence of smart, connected products is their contributions to broader, more complex networks and ecosystems. CPQ systems need to flex and support much greater system interoperability of products than they do today. Additional design principles include designing in connected service options, evergreen or long-term focus on the product-as-a-platform and designed in support for entirely new pricing models.
  • Smart, connected products need CPQ systems to reduce physical complexity while scaling device intelligence through cross-sells, up-sells and upgrades. Minimizing the physical options to allow for greater scale and support for device intelligence-based ones are needed in CPQ systems today. For many CPQ providers, that’s going to require different data models and taxonomies of product definitions. Smart, connected products will be modified after purchase as well, evolving to customers’ unique requirements.
  • After-sales service for smart, connected products will redefine pricing and profit models for the better in 2019, and CPQ needs to keep up to make it happen. Giving products the ability to send back their usage rates and patterns, reliability and performance data along with their current condition opens up lucrative pricing and services models. CPQ applications need to be able to provide quotes for remote diagnostics, price breaks on subscriptions for sharing data, product-as-a-service and subscription-based options for additional services. Many CPQ systems will need to be updated to support entirely new services-driven business models manufacturers are quickly adopting today.
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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.

High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019

Bottom Line: High-tech manufacturers need to urgently solve the paradox of improving supply chain security while attaining greater visibility across supplier networks if they’re going make the most of smart, connected products’ many growth opportunities in 2019.

The era of smart, connected products is revolutionizing every aspect of manufacturing today, from suppliers to distribution networks. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50 percent of their products to be smart, connected products by 2020, according to Capgemini’s Digital Engineering: The new growth engine for discrete manufacturers. The study is downloadable here (PDF, 40 pp., no opt-in).

Smart, connected products free manufacturers and their supply chains from having to rely on transactions and the price wars they create. The smarter the product, the greater the services revenue opportunities. And the more connected a smart product is using IoT and Wi-Fi sensors the more security has to be designed into every potential supplier evaluation, onboarding, quality plan, and ongoing suppliers’ audits. High-tech manufacturers are undertaking all of these strategies today, fueling them with real-time monitoring using barcoding, RFID and IoT sensors to improve visibility across their supply chains.

Gaining even greater visibility into their supply chains using cloud-based track-and-trace systems capable of reporting back the condition of components in transit to the lot and serialized pack level, high-tech suppliers are setting the gold standard for supply chain transparency and visibility. High-tech supply chains dominate many other industries’ supplier networks on accuracy, speed, and scale metrics on a consistent basis, yet the industry is behind on securing its vast supplier network. Every supplier identity and endpoint is a new security perimeter and taking a Zero Trust approach to securing them is the future of complex supply chains. With Zero Trust Privilege, high-tech manufacturers can secure privileged access to infrastructure, DevOps, cloud, containers, Big Data, production, logistics and shipping facilities, systems and teams.

High-Tech Needs to Confront Its Supply Chain Security Problem, Not Dismiss It

It’s ironic that high-tech supply chains are making rapid advances in accuracy and visibility yet still aren’t vetting suppliers thoroughly enough to stop counterfeiting, or worse. Bloomberg’s controversial recent article,The Big Hack: How China Used a Tiny Chip to Infiltrate U.S. Companies, explains how Amazon Web Services (AWS) was considering buying Portland, Oregon-based Elemental Technologies for its video streaming technology, known today as Amazon Prime Video. As part of the due diligence, AWS hired a third-party company to scrutinize Elemental’s security all the way up to the board level. The Elemental servers that handle the video compression were assembled by Super Micro Computer Inc., a San Jose-based company in China. Nested on the servers’ motherboards, the testers found a tiny microchip, not much bigger than a grain of rice, that wasn’t part of the boards’ original design that could create a stealth doorway into any network the machines were attached to. Apple (who is also an important Super Micro customer) and AWS deny this ever happened, yet 17 people have confirmed Supermicro had altered hardware, corroborating Bloomberg’s findings.

The hard reality is that the scenario Bloomberg writes about could happen to any high-tech manufacturer today. When it comes to security and 3rd party vendor risk management, many high-tech supply chains are stuck in the 90s while foreign governments, their militaries and the terrorist organizations they support are attempting to design in the ability to breach any network at will. How bad is it?  81% of senior executives involved in overseeing their companies’ global supply chains say 3rd party vendor management including recruiting suppliers is riskiest in China, India, Africa, Russia, and South America according to a recent survey by Baker & McKenzie.

PriceWaterhouseCoopers (PwC) and the MIT Forum for Supply Chain Innovation collaborated on a study of 209 companies’ supply chain operations and approaches to 3rd party vendor risk management. The study, PwC and the MIT Forum for Supply Chain Innovation: Making the right risk decisions to strengthen operations performance, quantifies the quick-changing nature of supply chains. 94% say there are changes in the extended supply chain network configuration happening frequently. Relying on trusted and untrusted domain controllers from server operating systems that are decades old can’t keep up with the mercurial pace of supply chains today.

Getting in Control of Security Risks in High-Tech Supply Chains

It’s time for high-tech supply chains to go with a least privilege-based approach to verifying who or what is requesting access to any confidential data across the supply chains. Further, high-tech manufacturers need to extend access request verification to include the context of the request and the risk of the access environment. Today it’s rare to find any high-tech manufacturer going to this level of least-privilege access approach, yet it’s the most viable approach to securing the most critical parts of their supply chains.

By taking a least-privilege access approach, high-tech manufacturers and their suppliers can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and operating costs across their hybrid manufacturing ecosystem.

Key actions that high-tech manufacturers can take to secure their supply chain and ensure they don’t end up in an investigative story of hacked supply chains include the following:

  • Taking a Zero Trust approach to securing every endpoint provides high-tech manufacturers with the scale they need to grow. High-tech supply chains are mercurial and fast-moving by nature, guaranteeing they will quickly scale faster than any legacy approaches enterprise security management. Vetting and then onboarding new suppliers needs to start by protecting every endpoint to the production and sourcing level, especially for next-generation smart, connected products.
  • Smart, connected products and the product-as-a-service business models they create are all based on real-time, rich, secured data streams that aren’t being eavesdropped on with components no one knows about. Taking a Zero Trust Privilege-based approach to securing access to diverse supply chains is needed if high-tech manufacturers are going to extend beyond legacy Privileged Access Management (PAM) to secure data being generated from real-time monitoring and data feeds from their smart, connected products today and in the future.
  • Quality management, compliance, and quality audits are all areas high-tech manufacturers excel in today and provide a great foundation to scale to Zero Trust Privilege. High-tech manufacturers have the most advanced quality management, inbound inspection and supplier quality audit techniques in the world. It’s time for the industry to step up on the security side too. By only granting least-privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment, high-tech manufacturers can make rapid strides to improve supply chain security.
  • Rethink the new product development cycles for smart, connected products and the sensors they rely on, so they’re protected as threat surfaces when built. Designing in security to the new product development process level and further advancing security scrutiny to the schematic and board design level is a must-do. In an era of where we have to assume bad actors are everywhere, every producer of high-tech products needs to realize their designs, product plans, and roadmaps are at risk. Ensuring the IOT and Wi-Fi sensors in smart, connected products aren’t designed to be hackable starts with a Zero Trust approach to defining security for supplier, design, and development networks.

Conclusion

The era of smart, connected products is here, and supply chains are already reverberating with the increased emphasis on components that are easily integrated and have high-speed connectivity. Manufacturing CEOs say it’s exactly what their companies need to grow beyond transaction revenue and the price wars they create. While high-tech manufacturers excel at accuracy, speed, and scale, they are falling short on security. It’s time for the industry to re-evaluate how Zero Trust can stabilize and secure every identity and threat surface across their supply chains with the same precision and intensity quality is today.

86% Of Enterprises Increasing IoT Spending In 2019

  • Enterprises increased their investments in IoT by 4% in 2018 over 2017, spending an average of $4.6M this year.
  • 38% of enterprises have company-wide IoT deployments in production today.
  • 84% of enterprises expect to complete their IoT implementations within two years.
  • 82% of enterprises share information from their IoT solutions with employees more than once a day; 67% are sharing data in real-time or near real-time.

These and many other fascinating insights are from Zebra Technologies’ second annual Intelligent Enterprise Index (PDF, 25 pp., no opt-in). The index is based on the list of criteria created during the 2016 Strategic Innovation Symposium: The Intelligent Enterprise hosted by the Technology and Entrepreneurship Center at Harvard (TECH) in 2016. An Intelligent Enterprise is one that leverages ties between the physical and digital worlds to enhance visibility and mobilize actionable insights that create better customer experiences, drive operational efficiencies or enable new business models, “ according to Tom Bianculli, Vice President, Technology, Zebra Technologies.

The metrics comprising the index are designed to interpret where companies are on their journeys to becoming Intelligent Enterprises. The following are the 11 metrics that are combined to create the Index: IoT Vision, Business Engagement, Technology Solution Partner, Adoption Plan, Change Management Plan, Point of use Application, Security & Standards, Lifetime Plan, Architecture/Infrastructure, Data Plan and Intelligent Analysis. An online survey of 918 IT decision makers from global enterprises competing in healthcare, manufacturing, retail and transportation and logistics industries was completed in August 2018. IT decision makers from nine countries were interviewed, including the U.S., U.K./Great Britain, France, Germany, Mexico, Brazil, China, India, and Australia/New Zealand. Please see pages 24 and 25 for additional details regarding the methodology.

Key insights gained from the Intelligent Enterprise Index include the following:

  • 86% of enterprises expect to increase their spending on IoT in 2019 and beyond. Enterprises increased their investments in IoT by 4% in 2018 over 2017, spending an average of $4.6M this year. Nearly half of enterprises globally (49%) interviewed are aggressively pursuing IoT investments with the goal of digitally transforming their business models this decade. 38% of enterprises have company-wide IoT deployments today, and 55% have an IoT vision and are currently executing their IoT plans.

  • 49% of enterprises are on the path to becoming an Intelligent Enterprise, scoring between 50 – 75 points on the index. The percent of enterprises scoring 75 or higher on the Intelligent Enterprise Index gained the greatest of all categories in the last 12 months, increasing from 5% to 11% of all respondents. The majority of enterprises are improving how well they scale the integration of their physical and digital worlds to enhance visibility and mobilize actionable insights. The more real-time the integration unifying the physical and digital worlds of their business models, the better the customer experiences and operational efficiencies attained.

  • The majority of enterprises (82%) share information from their IoT solutions with employees more than once a day, and 67% are sharing data in real-time or near real-time. 43% of enterprises say information from their IoT solutions is shared with employees in real-time, up 38% from last year’s index. 76% of survey respondents are from retailing, manufacturing, and transportation & logistics. Gaining greater accuracy of reporting across supplier networks, improving product quality visibility and more real-time data from distribution channels are the growth catalysts companies competing in retail, manufacturing, and transportation & logistics need to grow. These findings reflect how enterprises are using real-time data monitoring to drive quicker, more accurate decisions and be more discerning in which strategies they choose. Please click on the graphic to expand to view specifics.

  • Enterprises continue to place a high priority on IoT network security and standards with real-time monitoring becoming the norm. 58% of enterprises are monitoring their IoT networks constantly, up from 49%, and a record number of enterprises (69%) have a pre-emptive, proactive approach to IT security and network management. It’s time enterprises consider every identity a new security perimeter, including IoT sensors, smart, connected products, and the on-premise and cloud networks supporting them. Enterprises need to pursue a “never trust, always verify, enforce least privilege” approach and are turning to Zero Trust Privilege (ZTP) to solve this challenge today. ZTP grants least privilege access based on verifying who is requesting access, the context of their request, and ascertaining the risk of the access environment. Designed to secure infrastructure, DevOps, cloud, containers, Big Data, and scale to protect a wide spectrum of use cases, ZTP is replacing legacy approaches to Privileged Access Management by minimizing attack surfaces, improving audit and compliance visibility, and reducing risk, complexity, and costs for enterprises. Leaders in this field include Centrify for Privileged Access Management, Idaptive, (a new company soon to be spun out from Centrify) for Next-Gen Access, as well as CiscoF5 and Palo Alto Networks in networking.

  • Analytics and security dominate enterprise’ IoT management plans this year. 66% of enterprises are prioritizing analytics as their highest IoT data management priority this year, and 63% an actively investing in IoT security. The majority are replacing legacy approaches to Privilege Access Management (PAM) with ZTP.  Enterprises competing in healthcare and financial services are leading ZTS’ adoption today, in addition to government agencies globally. Enterprises investing in Lifecycle management solutions increased 11% between 2017 and 2018. Please click on the graphic to expand to view specifics.

Predicting The Future Of Digital Marketplaces

  • The U.S. B2B eCommerce market is predicted to be worth $1.2T by 2022 according to Forrester.
  • 75% of marketing executives say that reaching customers where they prefer to buy is the leading benefit a company gains from selling through an e-commerce marketplace according to Statista.
  • 67% strongly agree to the importance of B2B e-commerce being critical to their business’s advantages and results in their industry.

Digital Marketplaces are flourishing today thanks to the advances made in Artificial Intelligence (AI), machine learning, real-time personalization and the scale and speed of the latest generation of cloud platforms including the Google Cloud Platform. Today’s digital marketplaces are capitalizing on these technologies to create trusted, virtual trading platforms and environments buyers and sellers rely on for a wide variety of tasks every day.

Differentiated from B2B exchanges and communities from the 90s that often had high transaction costs, proprietary messaging protocols, and limited functionality, today’s marketplaces are proving that secure, trusted scalability is achievable on standard cloud platforms. Kahuna recently partnered with Brian Solis of The Altimeter Group to produce a fascinating research study, The State (and Future) of Digital Marketplaces. The report is downloadable here (PDF, 14 pp., opt-in). A summary of the results is presented below.

Kahuna Digitally Transforms Marketplaces With Personalization

The essence of any successful digital transformation strategy is personalization, and to the extent, any organization can redefine every system, process, and product to that goal is the extent to which they’ll grow. Digital marketplaces are giving long-established business and startups a platform to accelerate their digital transformation efforts by delivering personalization at scale.

Kahuna’s approach to solving personalization at scale across buyers and sellers while creating trust in every transaction reflects the future of digital marketplaces. They’ve been able to successfully integrate AI, machine learning, advanced query techniques and a cloud platform that scales dynamically to handle unplanned 5x global traffic spikes. Kahuna built its marketplace platform on Google App EngineGoogle BigQuery, and other Google Cloud Platform (GCP).

Kahuna’s architecture on GCP has been able to scale and onboard 80+ million users a day without any DevOps support, a feat not possible with the exchange and community platforms of the 90s. By integrating their machine learning algorithms designed to enhance their customers’ ability to personalize marketing messages with Google machine learning APIs to drive TensorFlow, Kahuna has been able to deliver fast response times to customers’ inquiries. Their latest product,  Kahuna Subject Line Optimization, analyzes the billions of emails their customers use to communicate with customers to see what has and hasn’t worked in the past.  Marketplace customers will receive real-time recommendations as they are in the email editor composing an email subject line. Kahuna scores the likely success of the subject lines in appealing to target audiences so that marketers can make adjustments on the fly.

The State (And Future) Of Digital Marketplaces

Digital marketplaces are rapidly transforming from transaction engines to platforms that deliver unique, memorable and trusted personal experiences.
Anyone who has ever used OpenTable to get a last-minute reservation with friends at popular, crowded restaurant has seen the power of digitally enabled marketplace experiences in action. Brian Solis noted futurist, author, and analyst with The Altimeter Group recent report,  The State (and Future) of Digital Marketplaces is based on 100 interviews with North American marketing executives across eight market segments.
Key insights and lessons learned from the study include the following:

  • Altimeter found that 67% of marketplaces are generating more than $50M annually and 32% are generating more than $100M annually with the majority of marketplaces reporting a Gross Merchandise Volume (GMV) of between $500M to $999M. When the size of participating companies is taken into account, it’s clear digital marketplaces are one form of new digital business models larger organizations are adopting, piloting and beginning to standardize on. It can be inferred from the data that fast-growing, forward-thinking smaller organizations are looking to digital marketplaces to help augment their business models. Gross merchandise volume (GMV) is the total value of merchandise sold to customers through a marketplace.
  • 59% of marketing executives say new product/service launches are their most important marketplace objective for 2019. As marketplaces provide an opportunity to create an entirely new business model, marketing executives are focused on how to get first product launches delivering revenue fast. Revenue growth (55%), customer acquisition (54%) and margin improvement (46%) follow in priority, all consistent with an organizations’ strategy of relying on digital marketplaces as new business models.

  • Competitive differentiation, buyer retention, buyer acquisition, and social media engagement and the four most common customer-facing challenges marketplaces face today. 39% of marketing execs say that differentiating from competitors is the greatest challenge, followed by buyer retention (32%), buyer acquisition (29%) and effective social media campaigns (29%) Further validation that today’s digital marketplaces are enabling greater digital transformation through personalization is found in just 22% of respondents said customer experience is a challenge.
  • Marketplaces need to scale and provide a broader base of services that enable “growth as a ” to keep sellers engaged. Marketplaces need to continually be providing new services and adding value to buyers and sellers, fueling growth-as-a-service. The three main reasons sellers leave a marketplace are insufficient competitive differentiation (46%), insufficient sales (33%) and marketplace service fees (31%). Additionally, sellers claim that marketing costs (28%) and the lack of buyers (26%) are critical business issues.
  • Lack of sellers who meet their needs (53%) is the single biggest reason buyers leave marketplaces. Buyers also abandon marketplaces due to logistical challenges including shipping costs and fees added by sellers (49%) and large geographic distances between buyers and sellers (39%). These findings underscore why marketplaces need to be very adept at creating and launching new value-added services and experiences that keep buyers active and loyal. Equally important is a robust roadmap of seller services that continually enables greater sales effectiveness and revenue potential.

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.

The State Of IoT Intelligence, 2018

  • Sales, Marketing and Operations are most active early adopters of IoT today.
  • Early adopters most often initiate pilots to drive revenue and gain operational efficiencies faster than anticipated.
  • 32% of enterprises are investing in IoT, and 48% are planning to in 2019.
  • IoT early adopters lead their industries in advanced and predictive analytics adoption.

These and many other fascinating insights are from Dresner Advisory Services’ latest report,  2018 IoT Intelligence® Market Study, in its 4th year of publication. The study concentrates on end-user interest in and demand for business intelligence in IoT. The study also examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. “While the market is still in an early stage, we believe that IoT Intelligence, the means to understand and leverage IoT data, will continue to expand as organizations mature in their collection and leverage of sensor level data,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. 70% of respondents work at North American organizations (including the United States, Canada, and Puerto Rico). EMEA accounts for about 20%, and the remainder is distributed across Asia-Pacific and Latin America. Please see pages 11, 15 through 18 of the study for specifics regarding the methodology and respondent demographics.

Key insights gained from the study include the following:

  • Sales, Marketing and Operations are most active early adopters of IoT today. Looking to capitalize on IoT’s potential to gain real-time customer feedback on products’ and services’ performance, Sales and Marketing lead all departments in their prioritizing IoT’s value in the enterprises. 12% of Operations leaders say that IoT is critical to attaining their goals. Executive Management and Finance have yet to see the value that Sales, Marketing and Operations do.

  • Manufacturers see IoT as the most critical to achieving their product quality, production scheduling and supply chain orchestration goals. Insurance industry leaders also view IoT as critical to operations as their business models are now concentrating on automating inventory and safety management. Insurance firms also track vehicles in shipping and logistics fleets to gain greater visibility into how route operations can be optimized at the lowest possible risk of accidents. Financial Services and Healthcare are the next most interested in IoT with Higher Education and Business Services assign the lowest levels of importance by industry.

  • Investment in IoT analytics, application development and defining accurate, reliable metrics to guide development is the most critical aspect of IoT adoption today. Investments in the data supply chain including data capture, movement, data prep, and management is the second-most critical area followed by investments in IoT infrastructure.  Analytics, application development, and accurate, reliable metrics guiding DevOps are consistent with the study’s finding that early adopters have an excellent track record adopting and applying advanced and predictive analytics to challenging logistical, operations, sales, and marketing problems.

  • IoT early adopters or advocates prioritize dashboards, reporting, IoT use cases that provide data streams integral to analytics, advanced visualization, and data mining. IoT early adopters and the broader respondent base differ most in the prioritization of IT analytics, location intelligence, integration with operational processes, in-memory analysis, open source software, and edge computing. The data reflects how IoT early adopters quickly become more conversant in emerging technologies with the goal of achieving exponential scale across analytics and IoT platforms.

  • The criticality of advanced and predictive analytics to all leaders surveyed is at an all-time high. Attaining a (weighted-mean) importance score of 3.6 on a 5.0 scale, advanced and predictive analytics is today considered “critical” or “very important” to a majority of respondents. Despite a mild decline in 2017, importance sentiment (the perceived criticality of advanced and predictive analytics) is on an uptrend across the five years of our study. Mastery of advanced and predictive analytics is a leading indicator of IoT adoption, indicating the potential for more analytics pilots and in-production IoT projects next year.

  • The most valuable features for advanced and predictive analytics apps include support for a range of regression models, hierarchical clustering, descriptive statistics, and recommendation engine support. Model management is important to more than 90% of respondents, further indicating IoT analytics scale is a goal many are pursuing. Geospatial analysis (highly associated with mapping, populations, demographics, and other web-generated data), Bayesian methods, and automatic feature selection is the next most required series of features.

  • Access to advanced analytics for predictive and temporal analysis is the most important usability benefit to IoT adopters today. Second is support for easy iteration, and third is a simple process for continuous modification of models. The study evaluated a detailed set of nine usability benefits that support advanced and predictive activities and processes. All nine benefits are important to respondents, with the last one of a specialist not being required important to a majority of them at 70%.

Reinventing After-Sales Service In A Subscription Economy World

  • 91% of manufacturers are investing in predictive analytics in the next 12 months, and 50% consider Artificial Intelligence (AI) a major planned investment for 2019 to support their subscription-based business models.
  • New subscription business models and smart, connected products are freeing manufacturers up from competing for one-time transaction revenues to recurring revenues based on subscriptions.
  • By 2020, manufacturers are predicting 67% of their product portfolios will be smart, connected products according to an excellent study by Capgemini.
  • 71% of manufacturers are using automated sensors for real-time monitoring and data capture of a product’s condition and performance, yet just 25% have the infrastructure in place to analyze it and maximize product uptime.

Manufacturers need to break their dependence on just selling products to selling services if they’re going to grow. Smart, connected products with IoT sensors embedded in them are the future of subscription business models and a key foundation of the subscription economy.

Product Reliability and Uptime Help Create Subscription Economies

In a subscription economy world, whoever excels at product reliability and uptime grows faster than competitors and defines the market. Airlines with the highest on-time ratings have designed in reliability and uptime as part of their company’s identity; their DNA is based on these goals. Worldwide Business Research (WBR) in collaboration with Syncron, a global provider of cloud-based after-sales service solutions focused on empowering the world’s leading manufacturers to maximize product uptime and deliver exceptional customer experiences, recently surveyed to see how manufacturers are addressing the reliability and uptime challenges so critical to growing subscription business.

The research study, Maximized Product Uptime: The Emerging Industry Standard provides insights into how manufacturers can improve their after-sales service solutions. A copy of the study can be downloaded here (PDF, 23 pp., opt-in). Please see pages 20 – 23 for additional details on the report’s methodology. WBR and Syncron designed the survey to gain a deep understanding of manufacturers’ ability to deliver on their customers increasing demand for maximized product uptime, surveying 200 original equipment manufacturers (OEMs), with respondents evenly split between the U.S. and European markets, as well as 100 equipment end-users

Key insights from the study include the following:

  • 34% of manufacturers are ready to compete in a subscription economy and have created a service strategy based on maximized product uptime. 39% are planning to have one in two years, and 22% are predicting it will be in 2020 or later before they have on in place. Capgemini found that manufacturers’ plans for smart, connected products would extend beyond these projections, making it a challenge of manufacturers to realize the new subscription revenue they’re planning on in the future.

  • 71% of manufacturers are using automated sensors including IoT for real-time monitoring and data capture of a product’s condition and performance, yet just 25% have the infrastructure in place to analyze it and maximize product uptime.  51% of manufacturers have systems in place for analyzing the inbound data generated from sensors, yet report they still have more work to do to make them operational.  The 25% of manufacturers with systems in place and at scale will have at least an 18-month jump on competitors who are just now planning on how to make use of the real-time data streams IoT sensors provide.

  • Predicting part failures before they occur (83%), optimizing product functionality based on usage (67%), and using stronger analytics to evaluate product performance (61%) matter most to manufacturers pursuing subscription models. Autonomous product operation (56%) and implementing stronger analytics on ROI ( 50%) are also extremely important. These findings further underscore how manufacturers need to design in reliability and uptime if they are going to succeed with subscription-based business models.

  • 91% of manufacturers are investing in predictive analytics in the next 12 months, and 50% consider Artificial Intelligence (AI) a major planned investment for 2019.  Creating meaningful data models from the massive amount of manufacturing data being captured using automated sensors and IoT devices is making predictive analytics, AI and machine learning extremely important to manufacturers’ IT planning budgets for 2019 and beyond. Combining predictive analytics, AI and machine learning to gain greater insights into pre-emptive maintenance on each production asset, installed product or device is the goal. Knowing when a machine or product will most likely fail is invaluable in ensuring the highest uptime and service reliability levels possible.

  • 77% of manufacturers say having an after-sales service model is critical to their customers’ success today.  Customers are ready to move beyond the legacy transactional, break-fix model of the past and want a more Amazon-like experience when it comes to uptime and reliability of every device they own as consumers and use at work. Speed, scale and simplicity are the foundational elements of a subscription business model, and the majority of manufacturers surveyed say their customers are leading them into a value-added after-sales service model.

Tech Leaders Look To IoT, AI & Robotics To Fuel Growth Through 2021

  • 30% of tech leaders globally predict blockchain will disrupt their businesses by 2021.
  • IoT, Artificial Intelligence (AI) and Robotics have the greatest potential to digitally transform businesses, making them more customer-centered and efficient.
  • 26% of global tech leaders say e-Commerce apps and platforms will be the most disruptive new business model in their countries by 2021.
  • IDC predicts worldwide IoT spending will reach $1.1T by 2021.

These and many other insights are from KPMG’s recent research study Tech Disruptors Outpace The Competition. The study can be downloaded here (PDF, 42 pp., no opt-in.).  The methodology is based on interviews with 750 global technology industry leaders, 85% of whom are C-level executives. For additional details on the methodology, please see pages 32 and 33 of the study. The study found that the three main benefits of adopting IoT, AI, and robotics include improved management of personal information, increased personal productivity, and improved customer experience through personalized real-time information. Key insights gained from the study include the following:

  • IoT, Artificial Intelligence (AI) and Robotics have the greatest potential to digitally transform businesses, making them more customer-centered and efficient. Tech leaders also see these three core technologies enabling the next indispensable consumer technology and driving the greatest benefit to life, society, and the environment. KPMG’s research team found that tech companies are integrating these three technologies to create growth platforms for new business ventures while digitally transforming existing business processes. Tech leaders in the U.K. (21%), Japan (20%) and the U.S. (16%) lead all other nations in their plans for IoT digitally transforming their businesses by 2021. Please click on the graphic below to expand for easier reading.

  • 30% of tech leaders globally predict blockchain will disrupt their businesses by 2021. 50% of Japanese tech leaders predict that blockchain will digitally transform their industries and companies by 2021, leading all nations included in the survey.  IoT processes and the rich, real-time data stream sensors and systems are capable of delivering is predicted by tech leaders to be the primary catalyst that will enable blockchain to digitally transform their businesses. 27% of tech leaders globally expect IoT data and applications combined with blockchain to redefine their companies, supply chains and industries. Identity authentication (24%), automated trading (22%) and contracts (14%) are the 2nd through fourth-most disruptive aspects of blockchain by 2021 according to tech leaders. Please click on the graphic below to expand for easier reading.

  • 26% of global tech leaders say e-Commerce apps and platforms will be the most disruptive new business model in their countries by 2021. 19% see social media platforms creating the majority of new business models, followed autonomous vehicle platforms (14%) and entertainment platforms (11%).  KPMG’s analysis includes a ranking of top business models by country, with e-Commerce dominating four of the five regions included in the survey.

  • 50% of tech leaders expect media, transportation, healthcare, and transportation to experience the greatest digital transformation in the next three years.  Respondents most mentioned Amazon, Netflix, Alibaba, Uber, Google, and Facebook as examples of companies who will digitally transform their industries by 2021.  The following table provides insights into which industries by country will see the greatest digital transformations in the next three years. Entertainment platforms are predicted by tech leaders to have the greatest potential to digitally transform the media industry in the U.S. by 2021.

  • Tech leaders predict IoT’s greatest potential for adoption by 2021 is in consumer products, education, services, industrial manufacturing, and telecom. AI’s greatest potential to digitally transform business models is in healthcare and industrial manufacturing (both 11%), consumer products, financial, and services (10% each).  As would be expected, Robotics’ adoption and contribution to digitally transforming businesses will be most dominant in industrial manufacturing (15%), followed by healthcare (11%) and consumer, financial and services (10%). Please click on the graphic to expand for easier reading.

How Blockchain Can Improve Manufacturing In 2019

  • The business value-add of blockchain will grow to slightly more than $176B by 2025, then exceed $3.1T by 2030 according to Gartner.
  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain.
  • Combining blockchain and IoT will revolutionize product safety, track-and-traceability, warranty management, Maintenance, Repair & Overhaul (MRO), and lead to new usage-based business models for smart, connected products.
  • By 2023, 30% of manufacturing companies with more than $5B in revenue will have implemented Industry 4.0 pilot projects using blockchain, up from less than 5% today according to Gartner.

Blockchain’s greatest potential to deliver business value is in manufacturing. Increasing visibility across every area of manufacturing starting with suppliers, strategic sourcing, procurement, and supplier quality to shop floor operations including machine-level monitoring and service, blockchain can enable entirely new manufacturing business models. Supply chains are the foundation of every manufacturing business, capable of making use of blockchain’s distributed ledger structure and block-based approach to aggregating value-exchange transactions to improve supply chain efficiency first. By improving supplier order accuracy, product quality, and track-and-traceability, manufacturers will be able to meet delivery dates, improve product quality and sell more.

Capgemini Research Institute’s recent study, Does blockchain hold the key to a new age of supply chain transparency and trust? provide valuable insights into how blockchain can improve supply chains and manufacturing. A copy of the study is available here (PDF, 32 pp., no opt-in). Capgemini surveyed 731 organizations globally regarding their existing and planned blockchain initiatives. Initial interviews yielded 447 organizations who are currently experimenting with or implementing blockchain. Please see pages 25 & 26 of the study for additional details regarding the methodology.

Key takeaways of the study include the following:

  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain. Capgemini found that there was 456 food recalls alone in the U.S. last year, costing nearly $3.5B. Blockchain’s general ledger structure provides a real-time audit trail for all transactions secured against modifications making it ideal for audit and compliance-intensive industries.

  • Gaining greater cost savings (89%), enhancing traceability (81%) and enhancing transparency (79%) are the top three drivers behind manufacturer’s blockchain investments today. Additional drivers include increasing revenues (57%), reducing risks (50%), creating new business opportunities (44%) and being more customer-centric (38%). The following graphic from the study illustrates the manufacturer’s priorities for blockchain. Capgemini finds that improving track-and-traceability is a primary driver across all manufacturers, consistent with the broader trend of manufacturers adopting software applications that improve this function today. That’s also understandable given how additional regulatory compliance requirements are coming in 2019 and those manufacturers competing in highly regulated industries including aerospace & defense, medical devices, and pharma are exploring how blockchain can give them a competitive edge now

  • Digital marketplaces, tracking critical supply chain parameters, tracking components quality, preventing counterfeit products, and tracking asset maintenance are the five areas Capgemini predicts blockchain will see the greatest adoption. Based on interviews with industry experts and startups, Capgemini found 24 blockchain use cases which are compared by level of adoption and complexity in the graphic below. The use cases reflect how managing supplier contracts is already emerging as one of the most popular blockchain use cases for manufacturing organizations today and will accelerate as compliance becomes even more important in 2019.

  • Manufacturers have the most at-scale deployments of blockchain today, leading all industries included in the study. Blockchain adoption is still nascent across all industries included in the study, with 6% of manufacturers having at-scale implementations today. Customer products manufacturers lead in pilots, with 15% actively [purusing blockchain in limited scope today. And retailers trail all industries with 91% having only proofs of concept.

  • Combining IoT and blockchain at the shipping container level in supply chains increases authenticity, transparency, compliance to product and contractual requirements while reducing counterfeiting. In highly regulated industries including Aerospace & Defense (A&D), Consumer Packaged Goods (CPG), medical devices, and pharma, combining IoT and blockchain provides real-time data on the shipping container conditions, tamper-proof storage, each shipment’s locational history and if there have been changes in temperature and product condition. Capgemini sees use cases where a change in a shipment’s temperature as measured by a sensor change sends alerts regarding contractual compliance of perishable meats and produce, averting the potential of bad product quality and rejected shipments once they reach their destination.

  • Capgemini found that 13% of manufacturers are Pacesetters and are either implementing blockchain at scale or have pilots in at least one site. Over 60% of Pacesetters believe that blockchain is already transforming the way they collaborate with their partners. Encouraged by these results, Pacesetters are set to increase their blockchain investment by 30% in the next three years. They lead early stage experimenters and all implementers on three core dimensions of organizational readiness. These include end-to-end visibility across functions, detailed and defined supportive processes, and availability of the right talent to succeed.

  • Lack of a clear ROI, immature technology and regulatory challenges are the top three hurdles Pacesetter-class manufacturers face in getting blockchain initiatives accepted and into production. All implementations face these three challenges in addition to having to overcome the lack of complementary IT systems at the partner organizations. The following graphic compares the hurdles all manufacturers face in getting blockchain projects implemented by the level of manufacturers adoption success (Pacesetter, early-stage experimenters, all implementers).

Source: Capgemini Research Institute, Does blockchain hold the key to a new age of supply chain transparency and trust? October, 2018

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