How Customer Experience and Digital Transformation are Connected

Aditya Chakurkar | August 10, 2021

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The major demands of the current market are the personalized customer experiences (CX) and modernized systems. While the market leaders have already implemented intelligent strategies to attract more and more customers, many other companies are focusing on building a digital strategy to satisfy the needs of their potential customers.

Technology has changed almost every bit of human life. Whether it's finance, medicine, education, retail, marketing, or any other industry, technology is inseparable. As a result, businesses are adopting newer and more modern platforms to match the current market trends, and this digital transformation is rewarding them with improved revenue.

But have you ever wondered who is more responsible for this transformation? Are they the companies or customers?

Well, the answer is — customers. More precisely, the digital customers.

The current market is flourishing in almost every aspect, thanks to digital experience technology. Customers want to get relevant information in an easy-to-consume format on the device of their choice. This fundamental need drives the customer experience transformation, and eventually, the digital transformation.

The advanced customer experience technology allows people to connect with the company anytime they want. And, embracing digitization is the only way for organizations to deliver the best customer experience.

Turns out, the ‘customer first’ mindset is at the heart of the digital customer experience strategy of most companies.

As per the recent IDC report, around two-thirds of the CEOs of the global 2000 organizations believe in building a robust digital customer experience and leave behind the conventional offline business strategies by the end of the year. Furthermore, 34% of companies target a complete adoption of digital transformation within a year or less.

The rise of digital transformation is pretty clear.

Digital Transformation and Customer Experience Go Hand in Hand

Even though these statistics are jaw-dropping, the market is far from a total digital adoption. For example,’s State of Digital Business Report revealed that around 45% of businesses are yet to kick off their digital transformation journey. In comparison, 59% of companies believe that they have already lost the race as they delayed the adoption.

This is the era of mobile applications, IoT devices, AI-powered tools, and high-speed internet. These things have significantly improved customer satisfaction and their overall product/service experience. As a result, companies are looking forward to transforming digitally to understand their customers well and meet their expectations through a multi-channel customer experience.

As a result, almost all businesses hope to strengthen their online presence and deliver the best customer experience.

Is Customer Experience Transformation Possible Without Digitization?

Digital transformation, generally, is an adoption of technology in all different business areas. It leads to a few fundamental alterations in business operations and the value they provide to their consumers.

In layman’s terms, digital transformation is about integrating modern techniques to interact with customers and continue delivering excellent customer experience ALL THE TIME.

As mentioned earlier, this digital transformation has a lot to do with customer satisfaction and experience. According to a PWC study, more than 50% of companies point at customer experience transformation as the most influential factor for digital transformation.

In return, digitally transformed companies get more engaged customers and more sales. The Rosetta consulting study reports,
  • Such highly engaged customers are six times more likely to try a new service or product.
  • Four times more likely to recommend your brand to their family and friends.
  • Two times more likely to buy your product even if your competitor is offering a better product at a relatively lower price.

Moreover, they have three times the annual value, compared to the traditional, average customers. But, of course, this goes without saying that digitally transformed companies get more profits than their traditional peers.

To summarize, customer experience transformation is challenging with traditional offline strategies. However, digitization is the new market reality, and businesses must adapt it to meet the expectations of the new kind of digital customers.

Steps for Improving Customer Experience Through Digital Transformation

Digital customers in the current market expect a lot from a product or service. Therefore, they won’t think twice before moving to another company if your product/service doesn’t meet their needs.

Therefore, companies must evolve and reconsider their efforts to make new customers and retain the existing ones. The following are the basic steps you need to keep in mind for achieving a satisfactory customer experience transformation through digital transformation:
  • Consistently deliver a personalized experience to your customer. Gone are the days of the 'One size fits all’ theory.
  • Focus more on agile product experimentation.
  • Focus more on multichannel, seamless customer experience.
  • Build more customer data for more subscriptions.
  • Train the leadership for a complete focus on digital.
  • Ensure customers can interact with your service on the device of their choice.
  • Keep the customer in mind. ALWAYS!
  • Avoid robotic approaches. Give it a human touch!

Bottom Line

Honestly, it's hard for your business to thrive without digital transformation. Whether it’s about the customer experience, operations, or business models, digitization is the most effective way to satisfy market needs in 2021 and beyond.

Customers want everything ‘right now.’ Embracing technology is the key to fulfill it!

Frequently Asked Questions

What is customer experience transformation?

Customer experience transformation (also known as CX transformation) improves customer experience through the adoption of newer technology, business structures, operations, and even culture. The ultimate aim of CX transformation is to deliver a remarkable customer experience at scale.

What are the four essential areas of digital transformation?

Digital transformation always comes down to the following four major areas:
  • Organizational and cultural transformation
  • Transformation in business processes
  • Transformation in business domains
  • Transformation in business models

What are the top three benefits of excellent customer experience?

Great customer experience can take your business a long way. Here are its top three benefits:
  • Offers cross-sell & upsell opportunities to improve revenue
  • Boosts customer loyalty
  • Improves brand identity


Ntrepid Corp

Ntrepid is a mission-driven provider of cutting-edge technology solutions for government and enterprise to discreetly and safely conduct sophisticated online operations in the most hostile online environments. We leverage our deep experience in the national security community to anticipate our customers' needs and provide solutions before the requirements are expressed. Our heavy investment in R&D allows us to stay ahead of the rapidly changing internet landscape.


5 AI Trends Profoundly Benefiting Business Bottom Lines

Article | November 17, 2020

Expert cites machine learning advancements creating immediate, actionable value to drive data literacy, elevate cognitive insights and increase profitability in kind. In today’s tumultuous business-scape amid increasingly intricate, and often vexing, marketplace conditions, curating and mining data to drive analytics-based decision making is just no longer enough. For competing with maximum, sustained impact and mitigated opportunity loss, it’s rapidly monetizing data that’s now the name of the game—particularly when spurred by artificial intelligence (AI). Indeed, emerging AI methodologies are helping forward-thinking companies achieve and sustain true agility, fuel growth and compete far more aggressively than ever before. AI is critical as a means toward those ends and also certainly with respect to aptly predicting, preparing and responding to prospective crises as with the COVID-19 pandemic the globe is currently immersed in. In fact, Gartner recently cited the need for “smarter, faster, more responsible AI” as its No. 1 top trend that data and analytics leaders should focus on—particularly those looking to “make essential investments to prepare for a post-pandemic reset.” Novel coronavirus matters aside, Gartner underscored just how impactful AI will become, predicting that, “by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.” “To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, Distinguished VP Analyst, Gartner. However, employing AI techniques like machine learning (ML) and natural language processing (NLP) to glean insights and render projections is simply no longer “enough” to get the job done—especially for organizations seeking to compete efficiently on a national, multi-national or global scale. Today’s organizations must endeavor toward a culture of AI-driven data literacy that directly and positively influences their top and bottom lines. “To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain, there are a few key ways to implement and elevate machine intelligence so that it’s far smarter, faster and more accountable than protocols past,” said Microsoft alum Irfan Khan, founder and CEO of CLOUDSUFI—an AI solutions firm automating data supply chains to propel and actualize data monetization. Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now—the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability. Manifesting new market opportunities Today’s machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. Present technology offers the opportunity to wholly analyze image, spoken or written inputs rather than just numerical, helping companies better find connections across these diverse data sets. This generates and maximizes value in a number of ways. Relative to the bottom and top lines, not only can it significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus. Machine learning advancements also help companies better monetize their data and establish new revenue streams. In the above example, of course patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. This includes determining that a certain drug is only effective on woman between certain ages—critical insights for pharmaceutical developers and physicians. Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, as are the resulting solutions, discoveries and profit-producing results thereof. Reconcile emotions with actualities Data generates value, which leads to the generation of money. It’s that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. There existed very rudimentary tools like regression and correlation, but today’s analytics call for gaining a true understanding of what extracted data actually means. How do you convert data into a story you can actually tell? Often, decisions are made based on emotional foundations. Leaders are using data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking, while also prompting them to ask new questions. So, garnering meaning out of a company’s own data provides tremendous advantages. “Human nature is such that unless we can see it touch it feel it, it’s hard to understand it,” Khan says. “We as data scientists haven’t done a really great job of explaining AI-driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies.” Scale statistical models for actionable models We often separate our data as factuals, asserting “this is what happened.” Neural networks connect the “human decision-making process” to those factuals—a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors and such in silos. But when these multiple data sets are connected, it becomes quite evident that no two humans—or customers—are exactly alike. Technology is now allowing us to understand trends on a factual level and then project outward. In the health realm, some companies are using this key learning to project whether or not a person is likely to suffer a certain affliction. It’s also allowing for far more efficacious “if this then what?” scenarios. If a diabetic person takes insulin controls, then their diet the treatment protocol will change. This is enabling highly personalized medicine. But the same processes, principles and benefits hold true in non-health categories as well—encompassing all industries, across the board. Future-proof, anti-fragile data supply chains From data connectors to pipelines; data lakes to statistical models; AI to Quantum; visual storyboards to data driven automation; ML to NLP to Neural Networks and more, there are highly effective methods for future-proofing your data value chain. The data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights. It starts with data acquisition—garnering a wide variety and volume of data from a number of internal and external sources where data is being generated by the millisecond. Once the data is identified and ingested, it needs to brought to a central point where it can be explored, cleansed, transformed, augmented and enriched and finally modelled for use toward a purpose. Then comes statistical and heuristic modeling. These models can be of different types using different algorithms yielding different levels of accuracy in different scenarios. Models then need to be tuned and provided and environment for continuous feedback, learning and monitoring. Finally, is the visualization of outcomes—an explanation demonstrated by drawing cause-effect relationships that highlight where the most impact happens. This leads to a conclusion on how a set of problems can be solved or opportunities uncovered. “Most organizations have some data and drive different levels of business process improvement and strategic decisions with it,” Khan notes. “However, few use data to the fullest. The right approach to data valuation and monetization can uncover limitless possibilities, including customer centricity, operational efficiency, competitive advantage, strategic partnerships, efficient operations, improved profitability and new revenue streams.” Multimedia monetization Up to now, we have been able to write algorithms, generate immense amounts of numerical or written data and make sense of it. However, there is a significant amount of data that comes as images or voice, which has not been easy to process and manage until recent developments. The applications for the processing of visual and auditory inputs are endless. In fact, retail and finance industries have been early adopters of this technology—and with good reason. They’ve seen costs go down, engagement go up, sales increase and benefitted from other highly substantial points of monetization. Now, a large department store can digitize their video data every night and determine that “X” amount of people saw “X” number of jeans, but they had to walk further to get to it. As a result, the department store can put those items closer to the door and walkways to determine if sales increase in kind. Even the education realm is tapping AI-driven data. The technology is tracking retina movement to discern if kids are engaged amid the remote learning paradigm ushered in by the pandemic. They’re exploring how to measure the retina to determine whether or not a child is actually engaged in the lesson. In radiology, they are starting to convert visual data and track it to gain a deeper understanding of digital images and video. MRIs are better able to track brain tumors—whether they are growing or shrinking and at what rate and if they are getting darker or lighter in terms of the regions. This kind of AI-driven learning is helping doctors better detect cancer and treat it more rapidly. Video data processing of the human eye can also be used to determine if a person is drunk, fatigued or even has a disease. Voice machine learning has also keenly evolved. Originally, voice recognition was being utilized to discern if a person was actually suicidal, which could be accurately predicted by inflection points in a person’s voice. Now, if that person can be captured on video, it is deemed to be about 20 times more accurate. “All of this possibly had previously demanded a hefty price tag using systems and solutions of yore,” Khan notes. “Today, integrating multiple processes across hybrid multi-cloud environments has made data processing and analytics much more accessible and outsourceable. This negates the need for companies to purchase cost-prohibitive servers and other machine hardware.” As one of the world's leading experts on building transparency into supply chains, Khan doesn’t just talk the talk, he’s walked the walk. As a revered marketplace change agent, he’s known for driving business transformation and customer-centric turnaround growth strategies in a multitude of environments. In addition to engineering partnerships with MIT, Khan has successfully led organizational changes and process improvement in markets across the Americas, Europe, Middle East and Asia. “New AI solutions and trends will eliminate patchwork processes that cause data, and interpretations thereof, to get lost in translation or, even worse, remain entirely undiscovered,” Khan says. “Next-Gen platforms are solving such problems by executing all functions required to create and govern AI products— single-source systems that pull data, transform, model, tunes and recommend actions with cause-effect transparency.” For niche players, today’s leading-edge AI technology also aptly provides for vertical industry specialization. “Emerging solutions enable common data models, compliance and interoperability requirements that, in turn, accelerate model validation, refinement and implementation that’s specific to a given sector or marketplace,” notes Khan. “All of this ultimately drives speed to insights on previously unsolved problems, which reveals untapped opportunities and automates workflow integrated cognitive solutions.” “Overall, AI is ushering in a new and more sophisticated era of data literacy,” he continues. “It’s a new paradigm founded on automated, comprehensive and holistic data discovery, which is fostering elevated cognitive insights and actionable strategies that positively impact the top and bottom line.” Perhaps the future mandate for AI should not only focus on becoming smarter, faster and more accountable than predecessors, but actually bridge the gap between human intuition and data-backed decisions. Doing so will assuredly advance an organization’s ability to transact with utmost trust.

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Edge Computing Trends in 2021

Article | November 17, 2020

With significant numbers of cloud vendors implementing edge servers in local markets, market analysts predict an incredible growth in edge computing. Furthermore, the rise in the hottest edge computing trends will be supported by the complimentary 5G network offerings. The current demand for edge-driven business models is so intense that even the COVID-19 pandemic failed to alter these predictions. A study from IDC’s worldwide IT predictions claims that the impact of this global pandemic will be one of the biggest reasons behind the growth of around 80% of edge computing investments and platform changes across various industries in the coming years. But firstly, what exactly is this edge computing, and how does it matter to technology trends? The term ‘edge’ in this points at literal geographic distribution. In layman's terms, edge computing is a kind of computing performed at or near the data source. However, unlike cloud computing, it does not rely on cloud data sources to do all the work. This doesn't mean that the cloud will disappear with edge computing; instead, it will come to you. Edge computing, in most cases, uses a variety of functions that need service provisioning closer to users. The most common examples of such functions are telco network functions, machine learning, robotics, AR/VR, and IoT. Using such emerging use cases, edge computing assists in resolving critical challenges in data sovereignty, resiliency, latency, and bandwidth. Furthermore, migration of data resources, applications, and infra results in: Faster responses to business needs Improved business scaling Long-term resilience Improved flexibility Businesses rely on edge computing to define new cloud-based services and products that can exploit security, storage, and processing capabilities at the edge of the network. There will be a need for more distributed compute and data processing power post the COVID-19 pandemic because of new working ways. Moreover, edge devices will be in demand for their ability to fulfill this need without debilitating latency. Top Edge Computing Trends in 2021 Considering the increasing maturation of edge computing capabilities, market analysts predict the rise in investment in edge computing. As edge computing solutions come up as ever more essential to business operations, here are the top edge computing trends you will want to keep an eye on. Artificial Intelligence & Machine Learning will Shift to Edge Computing Although no edge computing strategy can completely substitute the applications of cloud resources, many businesses are trying to put more ML and AI capabilities closer to endpoint devices. In this way, they can achieve the required speed and reliability of such smart processes. Companies that need to bridge the digital and physical world in real-time will be the primary reason for this new trend in computing. Plus, edge computing has the power to replace machine learning models that are often trained at the data center. Instead, with specialized hardware, learning can occur at the edge. Considering the general nature of edge technologies, it will improve real-time predictions and, ultimately, operational reliability. Moreover, edge computing and data security are closely associated. Therefore, with more advancements in edge computing strategies, data security will steadily grow. Pandemic will Push Organizations to the Edge The current global pandemic has made us realize the actual effectiveness of several modern technologies, and it has changed the way we integrate and deploy them. In addition, the growth in the usage of consumer mobile devices, the increased consumption of virtual content, videos, and the increased demand for IoT devices (Internet of Things) has made the critical edge computing trends continue to flourish. Here’s what Jason Mann, the vice president of IoT at SAS, has to say, “From social distancing to thermal imaging, safety device assurance and operational changes such as daily cleaning and sanitation activities, computer vision is an essential technology to accelerate solutions that turn raw IoT data (from video/cameras) into actionable insights.” For example, retailers and shop owners can use edge computing solutions to know when people violate the store’s policies. Edge will Enhance the Connected Ecosystem Adoption Apart from enhancing IoT device usage, edge computing will ease the participation of organizations in the connected ecosystem. And, most importantly, there will be relatively lesser bandwidth and network latency issues. Organizations can quickly expand to several other profitable businesses without incurring hefty infrastructure costs by leveraging the scalability of edge computing. In addition, moving to more fast-streaming and profitable markets will be easy for enterprises because of easy data processing. IT and Operational Technology will Unite The COVID-19 pandemic has revealed several weaknesses of most organizations, and ‘resiliency’ is perhaps one of the most common weaknesses of most companies. IoT-driven devices and other connected equipment power the adoption of edge computing solutions where applications and infrastructure are within the facilities. According to the IDC report, the real-time inference with the help of digital twins and AI models can make this approach critical as they can detect the changes in operating conditions and eventually automate the remediation. With more convergence of IT and operational technology, the IDC report says that the number of operations deployed on edge computing infrastructure will grow from 20% to more than 90% in 2024. As a result, enterprises will prioritize integrating intelligence into different workflows and processes using edge computing capabilities. In 2021, mobile edge computing MEC) trends are expected to enable supply chain resilience. With data analytics at the edge, the ecosystem of supply chain enablers can integrate AI and ML to access near, live insights into data consumptions. Furthermore, it will also allow businesses to look into even the most unprocessed elements of highly composite demand and supply chains. Therefore, organizations need something that can enable 24/7 view across the complete supply chain. MEC-based solutions answer this as they can offer end-to-end monitoring from the point of service or manufacture. Private 5G Adoption will Increase As serious applications will begin to materialize, many businesses will take themselves to the edge. Undoubtedly, there's great potential in a private 5G network for fueling the edge computing trends in 2021 and beyond. The private 5G network is dedicated to specific infrastructures like factories and warehouses. Factory robots and machine tools require low-latency networks and local data processing. Edge computing and 5G can satisfy this need. The general nature of edge technologies and speed, low latency, enhanced coverage, and incredible responsiveness of the 5G network can serve these benefits. Edge will Improve Data Security Edge computing reduces data and internet costs and eventually improves data efficiency compared to the cloud. Plus, the edge offers an additional layer of security and enhances the overall user experience. Unlike the cloud, edge computing does not depend on a single point of storage or application. Instead, it distributes different processes across a variety of devices. Edge Computing in 2021 and Beyond The current and future edge computing trends will enable innovation across several industries such as medical, agricultural, worker safety, insurance, industry 4.0, retail, telecommunications, and more. As more and more companies collaborate to develop an open ecosystem and ultimately make it quick and easy for consumers to integrate as a part of their cloud strategies, edge computing trends will offer more value to organizations in the coming years. To summarize, edge computing trends will continue to redefine business operations by offering enhanced data security and controls, better connectivity even when disconnected from the network, and quick insights and actions. If you have any questions related to your upcoming project, we can help! You can go through our success stories here. Frequently Asked Questions How new is edge computing? The origin of edge computing traces back to the 90s, and it lies in content delivery networks. The primary job of these networks was to serve video and web content from edge servers that are deployed within the facility. What is edge computing used for? Edge computing is used for a wide variety of applications, services, and products. For example, Medical monitoring devices for real-time responses Self-driving cars for real-time reactions Video conferencing for decreasing lag and latency More efficient caching Is edge computing the future? Several researchers estimate that the edge computing trends are proliferating at around 30% of the compound annual growth rate. The edge computing industry is expected to reach more than $7 billion in the next five years. Many businesses have already begun to prepare for the technology change that will take place because of the current and future edge computing trends. { "@context": "", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How new is edge computing?", "acceptedAnswer": { "@type": "Answer", "text": "The origin of edge computing traces back to the 90s, and it lies in content delivery networks. The primary job of these networks was to serve video and web content from edge servers that are deployed within the facility." } },{ "@type": "Question", "name": "What is edge computing used for?", "acceptedAnswer": { "@type": "Answer", "text": "Edge computing is used for a wide variety of applications, services, and products. 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Enterprises Start to Find Uses for AI at the Edge

Article | November 17, 2020

Data-driven experiences are rich, immersive and immediate. But they’re also delay-intolerant data hogs. Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data — quickly. So they can’t sustain latency as data travels to and from the cloud. That to-and-fro takes too long; instead, many of these data-intensive processes must remain localized and processed at the edge and on or near a hardware device.

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Create a Bulletproof IoT Device That Thrives in A Competitive Environment

Article | November 17, 2020

If you think the conventional way of designing and testing an Internet of Things (IoT) device is still relevant today, you might be wrong. Tens of billions of IoT devices surround us today. Billions more will connect to the internet in the next few years. On top of that, IoT deployment is diversifying from consumer-based to mission-critical applications in the areas of public safety, emergency response, industrial automation, autonomous vehicles, and healthcare IoT. While IoT devices offer great convenience, having large numbers of them in a small space increases complexity in device design, test, performance, and security.

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Ntrepid Corp

Ntrepid is a mission-driven provider of cutting-edge technology solutions for government and enterprise to discreetly and safely conduct sophisticated online operations in the most hostile online environments. We leverage our deep experience in the national security community to anticipate our customers' needs and provide solutions before the requirements are expressed. Our heavy investment in R&D allows us to stay ahead of the rapidly changing internet landscape.