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How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility

Bottom Line: Manufacturers’ most valuable data is generated on shop floors daily, bringing with it the challenge of analyzing it to find prescriptive insights fast – and an ideal problem for machine learning to solve.

Manufacturing is the most data-prolific industry there is, generating on average 1.9 petabytes of data every year according to the McKinsey Global Insititute. Supply chains, sourcing, factory operations, and the phases of compliance and quality management generate the majority of data.

The most valuable data of all comes from product inspections that can immediately find exceptionally strong or weak suppliers, quality management and compliance practices in a factory. Manufacturing’s massive problem is in getting quality inspection results out fast enough across brands & retailers, other factories, suppliers and vendors to make a difference in future product quality.

How A Machine Learning Startup Is Revolutionizing Product Inspections

Imagine you’re a major brand or retailer and you’re relying on a network of factories across Bangladesh, China, India, and Southeast Asia to produce your new non-food consumer goods product lines including apparel. Factories, inspection agencies, suppliers and vendors that brands and retailers like you rely on vary widely on ethics, responsible sourcing, product quality, and transparency. With your entire consumer goods product lines (and future sales) at risk based on which suppliers, factories and product inspection agencies you choose, you and your companies’ future are riding on the decisions you make.

These career- and company-betting challenges and the frustration of gaining greater visibility into what’s going on in supply chains to factory floors led Carlos Moncayo Castillo and his brothers Fernando Moncayo Castillo and Luis Moncayo Castillo to launch Inspectorio. They were invited to the Target + Techstars Retail Accelerator in the summer of 2017, a competition they participated in with their cloud-based inspection platform that includes AI and machine learning and pervasive support for mobile technologies. Target relies on them today to bring greater transparency to their supply chains. “I’ve spent years working in non-food consumer goods product manufacturing seeing the many disconnects between inspections and suppliers, the lack of collaboration and how gaps in information create too many opportunities for corruption – I had to do something to solve these problems,” Carlos said. The many problems that a lack of inspection and supply chain visibility creates became the pain Inspectorio focused on solving immediately for brands and retailers. The following is a graphic of their platform:

Presented below are a few of the many ways the combining of a scalable inspection cloud platform combined with AI, machine learning and mobile technologies are improving inspections, product quality, and supply chain visibility:

  • Enabling the creation of customized inspector workflows that learn over time and are tailored to specific products including furniture, toys, homeware and garments, the factories they’re produced in, quality of the materials used. Inspectorio’s internal research has found 74% of all inspections today are done manually using a pen and paper, with results reported in Microsoft Word, Excel or PDFs, making collaboration slow and challenging. Improving the accuracy, speed and scale of inspection workflows including real-time updates across production networks drive major gains in quality and supply chain performance.
  • Applying constraint-based algorithms and logic to understand why there are large differences in inspection results between factories is enabling brands & retailers to manage quality faster and more completely. Uploading inspections in real-time from mobile devices to an inspection platform that contains AI and machine learning applications that quickly parse the data for prescriptive insights is the future of manufacturing quality. Variations in all dimensions of quality including factory competency, supplier and production assembly quality are taken into account. In a matter of hours, inspection-based data delivers the insights needed to avert major quality problems to every member of a production network.
  • Reducing risk, the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point. When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. One of the most powerful catalysts driving inspections’ inflection point is the combination of automated workflows that deliver high-quality data that machine learning produces prescriptive insights from. And those insights are shared on performance dashboards across every brand, retailer, supplier, vendor and factory involved in shared production strategies today.
  • Matching the most experienced inspector for a given factory and product inspection drastically increases accuracy and quality. When machine learning is applied to the inspector selection and assignment process, the quality, and thoroughness of inspections increase. For the first time, brands, retailers, and factories have a clear, quantified view of Inspector Productivity Analysis across the entire team of inspectors available in a given region or country. Inspections are uploaded in real-time to the Inspectorio platform where advanced analytics and additional machine learning algorithms are applied to the data, providing greater prescriptive insights that would have ever been possible using legacy manual methods. Machine learning is also making recommendations to inspectors on which defects to look for first based on the data patterns obtained from previous inspections.
  • Knowing why specific factories and products generated more Corrective Action/Preventative Action (CAPA) than others and how fast they have been closed in the past and why is now possible. Machine learning is making it possible for entire production networks to know why specific factory and product combinations generate the most CAPAs. Using constraint-based logic, machine learning can also provide prescriptive insights into what needs to be improved to reduce CAPAs, including their root cause.

Predicting The Future Of Next-Gen Access And Zero Trust Security In 2019

Bottom Line:  The most valuable catalyst all digital businesses need to continue growing in 2019 is a Zero Trust Security (ZTS) strategy based on Next-Gen Access (NGA) that scales to protect every access point to corporate data, recognizing that identities are the new security perimeter.

The faster any digital business is growing, the more identities, devices and network endpoints proliferate. The most successful businesses of 2019 and beyond are actively creating entirely new digital business models today. They’re actively recruiting, and onboarding needed experts independent of their geographic locations and exploring new sourcing and patent ideas with R&D partners globally. Businesses are digitally transforming themselves at a faster rate than ever before. Statista projects businesses will spend $190B on digital transformation in 2019, soaring to $490B by 2025, attaining a 14.4% Compound Annual Growth Rate (CAGR) in six years.

Security Perimeters Make Or Break A Growing Business

80% of IT security breaches involve privileged credential access according to a recent Forrester study. The Verizon Mobile Security Index 2018 Report found that 89% of organizations are relying on just a single security strategy to keep their mobile networks safe. A typical data breach cost the average company $3.86M in 2018, up 6.4% from $3.62M in 2017 according to IBM Security’s latest  2018 Cost of a Data Breach Study.

The hard reality for any digital business is realizing that their greatest growth asset is how well they protect the constantly expanding perimeter of their business. Legacy approaches to securing infrastructure that relies on trusted and untrusted domains can’t scale to protect every identity and device that comprises a company’s rapidly changing new security perimeter. All these factors and more are why Zero Trust Security (ZTS) enabled by Next-Gen Access (NGA) is as essential to digital businesses’ growth as their product roadmaps, pricing strategies, and services with Idaptive being an early leader in the market. To learn more about Identity-as-a-Service please see the Forrester report, The Forrester Wave™: Identity-As-A-Service, Q4 2017 (client access required)

Predicting The Future Of Next-Gen Access And Zero Trust Security

The following are predictions of how Next-Gen Access (NGA) powered by Zero Trust Security (ZTS) will evolve in 2019:

  • Behavior-based scoring algorithms will improve markedly in 2019, improving the user experience by calculating risk scores with greater precision than before. Thwarting attacks start with a series of behavior-based algorithms that calculate a risk score based on a wide variety of variables including past access attempts, device security posture, operating system, location, time of day, and many other measurable factors. Expect to see these algorithms and the risk scores they generate using machine learning techniques improve from accuracy and contextual intelligence standpoint in 2019. Leading companies in the field including Idaptive are actively investing in machine learning technologies to accomplish this today.
  • Multifactor Authentication (MFA) adoption soars as digital businesses seek to protect new R&D projects, patents in progress, roadmaps, and product plans. State-sponsored hacking organizations and organized crime see the intellectual property in fast-growing digital businesses as among the most valuable assets they can exfiltrate and sell on the Dark Web. MFA, one of the most effective single defenses against compromised passwords, will be adopted by the most successful businesses in AI, aerospace & defense, chip design for cellular and IoT devices, e-commerce, enterprise software and more.
  • Smart, connected products without adequate security designed in will proliferate in 2019, further challenging the security perimeters of the digital businesses. The era of smart, connected products is here, with Capgemini estimating the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50% 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). With every smart, connected device creating a new threat surface for a company, expect to see at least one device manufacturer design Zero Trust Security (ZTS) support to the board level to increase their sales into enterprises by reducing the threat of a breach starting from their device.
  • Looking for greater track and traceability, healthcare and medical products supply chains will adopt Zero Trust Security (ZTS). What’s going to make this an urgent issue in healthcare and medical products are the combined effects of greater regulatory reporting and compliance, combined with the pressure to improve time-to-market for new products and delivery accuracy for current customers. The pillars of ZTS are a perfect fit for healthcare and medical supply chains’ need for track and traceability. These pillars are real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviors.
  • Real-time Security Analytics Services is going to thrive in 2019 as digital businesses seek insights into how they can fine-tune their ZTS strategies across every threat surface and machine learning algorithms improve. Many enterprises are in for an epiphany in 2019 when they see just how many potential breaches they’ve stopped using a combination of security strategies including Single Sign-On (SSO) and Multi-factor Authentication (MFA). Machine learning algorithms will continue to improve using behavior-based scoring, further improving the user experience. Leaders in the field include Idaptive who is setting a rapid pace of innovation in Real-Time Security Analytics Services.   

Conclusion

Security is at an inflection point today. Long-standing methods of protecting IT systems and a businesses’ assets can’t scale to protect every new identity, device or threat surface. When every identity is a new security perimeter, a new approach is needed to securing any digital business. The pillars of ZTS including real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviors are proving to be effective at thwarting breaches and securing company’ digital assets of all kinds. It’s time for more digital businesses to see security as the growth catalyst it is and take action now to ensure their operations continue to flourish.

Microsoft Leads The AI Patent Race Going Into 2019

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

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

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

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

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

6 Best Practices For Increasing Security In AWS In A Zero Trust World

  • Amazon Web Services (AWS) reported $6.6B in revenue for Q3, 2018 and $18.2B for the first three fiscal quarters of 2018.
  • AWS revenue achieved an impressive 46% year-over-year net sales growth between Q3, 2017 and Q3, 2018 and 49% year-over-year growth for the first three quarters of the year.
  • AWS’ 34% market share is bigger than its next four competitors combined with the majority of customers taken from small-to-medium sized cloud operators according to Synergy Research.
  • The many announcements made at AWS Re:Invent this year reflect a growing focus on hybrid cloud computing, security, and compliance.

Enterprises are rapidly accelerating the pace at which they’re moving workloads to Amazon Web Services (AWS) for greater cost, scale and speed advantages. And while AWS leads all others as the enterprise public cloud platform of choice, they and all Infrastructure-as-a-Service (IaaS) providers rely on a Shared Responsibility Model where customers are responsible for securing operating systems, platforms and data.  In the case of AWS, they take responsibility for the security of the cloud itself including the infrastructure, hardware, software, and facilities. The AWS version of the Shared Responsibility Model shown below illustrates how Amazon has defined securing the data itself, management of the platform, applications and how they’re accessed, and various configurations  as the customers’ responsibility:

Included in the list of items where the customer is responsible for security “in” the cloud is identity and access management, including Privileged Access Management (PAM) to secure the most critical infrastructure and data.

Increasing Security for IaaS in a Zero Trust World

Stolen privileged access credentials are the leading cause of breaches today. Forrester found that 80% of data breaches are initiated using privileged credentials, and 66% of organizations still rely on manual methods to manage privileged accounts. And while they are the leading cause of breaches, they’re often overlooked — not only to protect the traditional enterprise infrastructure — but especially when transitioning to the cloud.

Both for on-premise and Infrastructure-as-a-Service (IaaS), it’s not enough to rely on password vaults alone anymore. Organizations need to augment their legacy Privileged Access Management strategies to include brokering of identities, multi-factor authentication enforcement and “just enough, just-in-time” privilege, all while securing remote access and monitoring of all privileged sessions. They also need to verify who is requesting access, the context of the request, and the risk of the access environment. These are all essential elements of a Zero Trust Privilege strategy, with Centrify being an early leader in this space.

6 Ways To Increase Security in AWS

The following are six best practices for increasing security in AWS and are based on the Zero Trust Privilege model:

  1. Vault AWS Root Accounts and Federate Access for AWS Console

Given how powerful the AWS root user account is, it’s highly recommended that the password for the AWS root account be vaulted and only used in emergencies. Instead of local AWS IAM accounts and access keys, use centralized identities (e.g., Active Directory) and enable federated login. By doing so, you obviate the need for long-lived access keys.

  1. Apply a Common Security Model and Consolidate Identities

When it comes to IaaS adoption, one of the inhibitors for organizations is the myth that the IaaS requires a unique security model, as it resides outside the traditional network perimeter. However, conventional security and compliance concepts still apply in the cloud. Why would you need to treat an IaaS environment any different than your own data center? Roles and responsibilities are still the same for your privileged users. Thus, leverage what you’ve already got for a common security infrastructure spanning on-premises and cloud resources. For example, extend your Active Directory into the cloud to control AWS role assignment and grant the right amount of privilege.

  1. Ensure Accountability

Shared privileged accounts (e.g., AWS EC2 administrator) are anonymous. Ensure 100% accountability by having users log in with their individual accounts and elevate privilege as required. Manage entitlements centrally from Active Directory, mapping roles, and groups to AWS roles.

  1. Enforce Least Privilege Access

Grant users just enough privilege to complete the task at hand in the AWS Management Console, AWS services, and on the AWS instances. Implement cross-platform privilege management for AWS Management Console, Windows and Linux instances.

  1. Audit Everything

Log and monitor both authorized and unauthorized user sessions to AWS instances. Associate all activity to an individual, and report on both privileged activity and access rights. It’s also a good idea to use AWS CloudTrail and Amazon CloudWatch to monitor all API activity across all AWS instances and your AWS account.

  1. Apply Multi-Factor Authentication Everywhere

Thwart in-progress attacks and get higher levels of user assurance. Consistently implement multi-factor authentication (MFA) for AWS service management, on login and privilege elevation for AWS instances, or when checking out vaulted passwords.

Conclusion

One of the most common reasons AWS deployments are being breached is a result of privileged access credentials being compromised. The six best practices mentioned in this post are just the beginning; there are many more strategies for increasing the security in AWS.  Leveraging a solid Zero Trust Privilege platform, organizations can eliminate shared Amazon EC2 key pairs, using auditing to define accountability to the individual user account level, execute on least privilege access across every login, AWS console, and AWS instance in use, enforce MFA and enable a common security model.

Which Analytics And BI Technologies Will Be The Highest Priority In 2019?

  • 82% of enterprises are prioritizing analytics and BI as part of their budgets for new technologies and cloud-based services.
  • 54% say AI, Machine Learning and Natural Language Processing (NLP) are also a high investment priority.
  • 50% of enterprises say their stronger focus on metrics and Key Performance Indicators (KPIs) company-wide are a major driver of new investment in analytics and BI.
  • 43%  plan to both build and buy AI and machine learning applications and platforms.
  • 42% are seeking to improve user experiences by automating discovery of data insights and 26% are using AI to provide user recommendations.

These and many other fascinating insights are from the recent TDWI Best Practices Report, BI and Analytics in the Age of AI and Big Data. An executive summary of the study is available online here. The entire study is available for download here (39 PP., PDF, free, opt-in). The study found that enterprises are placing a high priority on augmenting existing systems and replacing older technologies and data platforms with new cloud-based BI and predictive analytics ones. Transforming Data with Intelligence (TDWI) is a global community of AI, analytics, data science and machine learning professionals interested in staying current in these and more technology areas as part of their professional development. Please see page 3 of the study for specifics regarding the methodology.

Key takeaways from the study include the following:

  • 82% of enterprises are prioritizing analytics and BI applications and platforms as part of their budgets for new technologies and cloud-based services. 78% of enterprises are prioritizing advanced analytics, and 76% data preparation. 54% say AI, machine learning and Natural Language Processing (NLP) are also a high investment priority. The following graphic ranks enterprises’ investment priorities for acquiring or subscribing to new technologies and cloud-based services by analytics and BI initiatives or strategies. Please click on the graphic to expand for easier reading.

  • Data warehouse or mart in the cloud (41%), data lake in the cloud (39%) and BI platform in the cloud (38%) are the top three types of technologies enterprises are planning to use. Based on this finding and others in the study, cloud platforms are the new normal in enterprises’ analytics and Bi strategies going into 2019. Cloud data storage (object, file, or block) and data virtualization or federation (both 32%) are the next-most planned for technologies by enterprises when it comes to investing in the analytics and BI initiatives. Please click on the graphic to expand for easier reading.

  • The three most important factors in delivering a positive user experience include good query performance (61%), creating and editing visualizations (60%), and personalizing dashboards and reports (also 60%). The three activities that lead to the least amount of satisfaction are using predictive analytics and forecasting tools (27% dissatisfied), “What if” analysis and deriving new data (25%) and searching across data and reports (24%). Please click on the graphic to expand for easier reading.

  • 82% of enterprises are looking to broaden the base of analytics and BI platforms they rely on for insights and intelligence, not just stay with the solutions they have in place today. Just 18% of enterprises plan to add more instances of existing platforms and systems. Cloud-native platforms (38%), a new analytics platform (35%) and cloud-based data lakes (31%) are the top three system areas enterprises are planning to augment or replace existing BI, analytics, and data warehousing systems in. Please click on the graphic to expand for easier reading.

  • The majority of enterprises plan to both build and buy Artificial Intelligence (AI) and machine learning (ML) solutions so that they can customize them to their specific needs. 43% of enterprises surveyed plan to both build and buy AI and ML applications and platforms, a figure higher than any other recent survey on this aspect of enterprise AI adoption. 13% of responding enterprises say they will exclusively build their own AI and ML applications.

  • Capitalizing on machine learning’s innate strengths of applying algorithms to large volumes of data to find actionable new insights (54%) is what’s most important to the majority of enterprises. 47% of enterprises look to AI and machine learning to improve the accuracy and quality of information. And 42% are configuring AI and machine learning applications and platforms to augment user decision making by giving recommendations. Please click on the graphic to expand for easier reading.

10 Ways Machine Learning Is Revolutionizing Sales

  • Sales teams adopting AI are seeing an increase in leads and appointments of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70% according to the Harvard Business Review article Why Salespeople Need to Develop Machine Intelligence.
  • 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  • By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes according to Gartner.
  • High-performing sales teams are 4.1X more likely to use AI and machine learning applications than their peers according to the State of Sales published by Salesforce.
  • Intelligent forecasting, opportunity insights, and lead prioritization are the top three AI and machine learning use cases in sales.

Artificial Intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually intensive tasks.

The Race for Sales-Focused AI & Machine Learning Patents Is On

CRM and Configure, Price & Quote (CPQ) providers continue to develop and fine-tune their digital assistants, which are specifically designed to help the sales team get the most value from AI and machine learning. Salesforces’ Einstein supports voice-activation commands from Amazon Alexa, Apple Siri, and Google. Salesforce and other enterprise software companies continue aggressively invest in Research & Development (R&D). For the nine months ended October 31, 2018, Salesforce spent $1.3B or 14% of total revenues compared to $1.1B or 15% of total revenues, during the same period a year ago, an increase of $211M according to the company’s 10Q filed with the Securities and Exchange Commission.

The race for AI and machine learning patents that streamline selling is getting more competitive every month. Expect to see the race of sales-focused AI and machine learning patents flourish in 2019. The National Bureau of Economic Research published a study last July from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe. Using patent analytics from PatentSight and ipsearchIAM published an analysis last month showing Microsoft as the global leader in machine learning patents with 2,075.  The study relied on PatentSight’s Patent Asset Index to rank machine learning patent creators and owners, revealing Microsoft and Alphabet are dominating today. Salesforce investing over $1B a year in R&D reflects how competitive the race for patents and intellectual property is.

10 Ways Machine Learning Is Revolutionizing Sales

Fueled by the proliferation of patents and the integration of AI and machine learning code into CRM, CPQ, Customer Service, Predictive Analytics and a wide variety of Sales Enablement applications, use cases are flourishing today. Presented below are the ten ways machine learning is most revolutionizing selling today:

 

  1. AI and machine learning technologies excel at pattern recognition, enabling sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers. Nearly all AI-enabled CRM applications are providing the ability to define a series of attributes, characteristics and their specific values that pinpoint the highest potential prospects. Selecting and prioritizing new prospects using this approach saves sales teams thousands of hours a year.
  2. Lead scoring and nurturing based on AI and machine learning algorithms help guide sales and marketing teams to turn Marketing Qualified Leads (MQL) into Sales Qualified Leads (SQL), strengthening sales pipelines in the process. One of the most important areas of collaboration between sales and marketing is lead nurturing strategies that move prospects through the pipeline. AI and machine learning are enriching the collaboration with insights from third-party data, prospect’s activity at events and on the website, and from previous conversations with salespeople. Lead scoring and nurturing relies heavily on natural language generation (NLG) and natural-language processing (NLP) to help improve each lead’s score.
  3. Combining historical selling, pricing and buying data in a single machine learning model improves the accuracy and scale of sales forecasts. Factoring in differences inherent in every account given their previous history and product and service purchasing cycles is invaluable in accurately predicting their future buying levels. AI and machine learning algorithms integrated into CRM, sales management and sales planning applications can explain variations in forecasts, provided they have the data available. Forecasting demand for new products and services is an area where AI and machine learning are reducing the risk of investing in entirely new selling strategies for new products.
  4. Knowing the propensity of a given customer to churn versus renew is invaluable in improving Customer Lifetime Value. Analyzing a diverse series of factors to see which customers are going to churn or leave versus those that will renew is among the most valuable insights AI and machine learning is delivering today. Being able to complete a Customer Lifetime Value Analysis for every customer a company has provides a prioritized roadmap of where the health of client relationships are excellent versus those that need attention. Many companies are using Customer Lifetime Value Analysis as a proxy for a customer health score that gets reviewed monthly.
  5. Knowing the strategies, techniques and time management approaches the top 10% of salespeople to rely on to excel far beyond quota and scaling those practices across the sales team based on AI-driven insights. All sales managers and leaders think about this often, especially in sales teams where performance levels vary widely. Knowing the capabilities of the highest-achieving salespeople, then selectively recruiting those sales team candidates who have comparable capabilities delivers solid results. Leaders in the field of applying AI to talent management include Eightfold whose approach to talent management is refining recruiting and every phase of managing an employee’s potential. Please see the recent New York Times feature of them here.
  6. Guided Selling is progressing rapidly from a personalization-driven selling strategy to one that capitalized on data-driven insights, further revolutionizing sales. AI- and machine learning-based guided selling is based on prescriptive analytics that provides recommendations to salespeople of which products, services, and bundles to offer at which price. 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  7. Improving the sales team’s productivity by using AI and machine learning to analyze the most effective actions and behaviors that lead to more closed sales. AI and machine learning-based sales contact and customer predictive analytics take into account all sources of contacts with customers and determine which are the most effective. Knowing which actions and behaviors are correlated with the highest close rates, sales managers can use these insights to scale their sales teams to higher performance.
  8. Sales and marketing are better able to define a price optimization strategy using all available data analyzing using AI and machine learning algorithms. Pricing continues to be an area the majority of sales and marketing teams learn to do through trial and error. Being able to analyze pricing data, purchasing history, discounts are taken, promotional programs participated in and many other factors, AI and machine learning can calculate the price elasticity for a given customer, making an optimized price more achievable.
  9. Personalizing sales and marketing content that moves prospects from MQLs to SQLs is continually improving thanks to AI and machine learning. Marketing Automation applications including HubSpot and many others have for years been able to define which content asset needs to be presented to a given prospect at a given time. What’s changed is the interactive, personalized nature of the content itself. Combining analytics, personalization and machine learning, marketing automation applications are now able to tailor content and assets that move opportunities forward.
  10. Solving the many challenges of sales engineering scheduling, sales enablement support and dedicating the greatest amount of time to the most high-value accounts is getting solved with machine learning. CRM applications including Salesforce can define a salesperson’s schedule based on the value of the potential sale combined with the strength of the sales lead, based on its lead score. AI and machine learning optimize a salesperson’s time so they can go from one customer meeting to the next, dedicating their time to the most valuable prospects.
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