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Protecting Privileged Identities In A Post-COVID-19 World

Protecting Privileged Identities In A Post-COVID-19 World

Bottom Line: Every organization needs to digitally reinvent their business, starting at the system level to safely sell and serve customers with minimal physical interaction.

The hard reset every business is going through creates a strong sense of urgency to increase the agility, speed, and scale of selling, as well as customer service options that protect the health of employees, customers, and partners. Customer experience needs to be the cornerstone of digital transformation, with the customers’ health and welfare being the highest priority. Businesses need to realize that digitally reinventing themselves is no longer optional. Every customer-facing system is going to need the best infrastructure, security, and stability for any business to survive and grow.

Securing Infrastructure Needs To Come First

COVID-19 was a wake-up call that companies need to operate as multi-channel players, allowing for physical but, more importantly, virtual presence. For instance, in retail, only those that will step up their efforts in building on-line ordering and associated nation-wide logistics networks will survive in the longer-term. If the cloud was considered an option in the past, it now is mandatory. In turn, the need for security has increased.

Starting with infrastructure, hybrid- and multi-cloud environments need to be augmented with additional system support, new apps, and greater security to support the always-on nature of competing in a virtual world. Providing self-service sales and support across any device at any time and keeping all systems synchronized is going to take more real-time integration, better security, more precise pricing, and so much more.

Consumer electronics manufacturers’ biggest challenge is reinventing their infrastructure while selling and serving customers at the same time. Part of their biggest challenge is protecting privileged access credentials that have become fragmented across hybrid- and multi-cloud environments. Everyone I’ve spoken with is balancing the urgent need for new revenue through new channels on the one hand with intensity to secure infrastructure and the most valuable security assets of all, privileged access credentials.

According to a 2019 study by Centrify among 1,000 IT decision-makers, 74% of respondents whose organizations have been breached acknowledged that it involved access to a privileged account. These are typically used by a small set of technical personnel to access the most critical systems in the IT estate, including modern technologies such as cloud, DevOps, microservices, and more. The CIO of a local financial services and insurance company, who is a former student and friend, told me that “it’s often said that privileged access credentials are the keys to the kingdom, and in these turbulent times they’re the keys to keeping any business running.”

CIOs, CISOs, and their teams are focusing on four key areas today while digitally reinventing themselves to provide more flexible options for customers:

  • Secure every new self-service selling and service channel from breaches.
  • Fast-track cloud projects to become 100% virtual and available.
  • Simplify infrastructure management by integrating IT and Operations Management across hybrid and multi-cloud environments.
  • Improve compliance reporting as well as reduce audit costs and associated fines.

Legacy Privileged Access Management (PAM) Can’t Scale For Today’s Threats

Sophisticated social engineering and breach attempts are succeeding in misdirecting human responses to cyber threats, gaining access to valuable privileged access credentials in the process. Legacy PAM systems based on vaulting away shared and root passwords aren’t designed to protect hybrid cloud and multi-cloud environments. These DevOps systems include containers and microservices, APIs, machines, or services. Furthermore, multi-cloud environments create additional challenges because access management tools used for one vendor cannot be used with another.

Switching from in-person to self-service selling and service creates new challenges and an entirely new series of requirements for identity and access management. These requirements include securing a continually-increasing number of workloads that cause the amount of data in the cloud to grow exponentially. There’s also the need to centralize identities for consistent access controls across hybrid and multi-cloud environments – all happening while a business is busy digitally reinventing itself. Compounding all of these challenges is the need to excel at delivering an excellent user experience without sacrificing security in an increasingly self-service, always-on, 24/7 world.

Securing Privileged Access In A Post-COVID-19 World

If you’re looking for a sure sign any business will be around and growing in twelve months, look at how fast they are digitally reinventing themselves at the infrastructure level and protecting privileged access credentials first. Digital-first businesses are taking a more adaptive approach to consistently controlling access to hybrid infrastructure for both on-premises and remote users now.

Centrify and others are making rapid progress in this area, with Centrify’s Identity-Centric PAM taking a “never trust, always verify, enforce least privilege” approach to securing privileged identities. Centrify’s approach to Identity-Centric PAM establishes per-machine trust so it can defend itself from illegitimate users – whether human or machine  – or those without the right entitlements. It then grants least privilege access just-in-time based on verifying who is requesting access, the context of the request, and the risk of the access environment as is illustrated in the graphic below:

Protecting Privileged Identities In A Post-COVID-19 World

Conclusion

Improving customer experiences needs to be at the center of any digital transformation effort. As every business digitally transforms itself to survive and grow in a post-COVID-19 world out of necessity, they must also improve how they secure access to their cloud and on-premises infrastructure. Legacy PAM was designed for a time when all privileged access was constrained to resources inside the network, accessed by humans, using shared/root accounts.

Legacy PAM was not designed for cloud environments, DevOps, containers, or microservices. Furthermore, privileged access requesters are no longer limited to just humans, but also include machines, services, and APIs.

Privileged access requesters need greater agility, adaptability, and speed to support DevOps’ growing roadmap of self-service and increasingly safer apps and platforms. While privileged identities must be protected, DevOps teams need as much agility and speed as possible to innovate at the rapidly changing pace of how customers choose to buy in a post-COVID-19 world.

Six Areas Where AI Is Improving Customer Experiences

Six Areas Where AI Is Improving Customer Experiences

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

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

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

Fundamentals of CX Economics

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

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

Six Areas Where AI Is Improving Customer Experiences

 

Where AI Is Improving CX

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

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

The following are six ways AI can improve customer experiences:

  • Improving contactless personalized customer care is considered one of the most valuable areas where AI is improving customer experiences. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity, according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. (PDF, 28 pp).

Six Areas Where AI Is Improving Customer Experiences

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

Six Areas Where AI Is Improving Customer Experiences

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

Six Areas Where AI Is Improving Customer Experiences

Additional Resources

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

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.

AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn. Feb. 8, 2018.

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

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

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

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

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.

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

David Simchi-Levi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting

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

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

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