Enhancing Customer Experience Through Observability Solutions

In an interaction with Asia Business Outlook, Pradeep Seshadri, Head of Solutions Consulting, India, New Relic, shares his views on enhancing customer experience through observability solutions, hurdles in implementing effective observability solutions across complex, distributed systems, challenges that exist in harnessing the full potential of AI-driven observability and more.

Enhancing customer experience through observability solutions involves leveraging real-time data and insights to optimise systems, services, and interactions, ensuring seamless and personalised experiences that drive customer satisfaction and loyalty. What are the primary challenges that companies face in accomplishing it?

No matter your industry, software defines the experiences customers seek at almost every level, from first impressions to detailed interactions. Ensuring experiences are personalized, always available, and seamless are some of the tenets of meeting these customer expectations. But software doesn’t always stay up and running. In India, 54% of businesses experienced high-business-impact outages at least once per week, according to the New Relic Observability Forecast 2023.

The biggest challenge, however, is monitoring all parts of this complex web of systems and agents operating on different parts of the tech stack, be it remote or on-premise. When something goes wrong, and the software becomes slow or unresponsive, it results in a poor customer experience because businesses take significant time to figure out what went wrong, why it occurred, and how to fix it. This is where full-stack observability comes into play. It consolidates all the telemetry data, offering businesses complete visibility — from backend APIs to frontend devices, from on-prem to cloud infrastructure. It monitors the entire tech stack and tracks customer journeys, so when sudden changes occur, businesses can isolate the issues and fix them before they impact customers. By proactively identifying errors before customers encounter them, observability empowers companies with the insights they need, helping them deliver better customer experiences.

As customer expectations for seamless digital experiences continue to rise, what are the hurdles in implementing effective observability solutions across complex, distributed systems, and how can organisations overcome those hurdles to achieve real-time visibility and actionable insights into their applications and services?

Achieving complete visibility into an IT environment has always been a challenge. The visibility needed to provide seamless digital experiences requires an entire record of everything happening in real-time, including how different systems interact. Businesses with numerous monitoring tools can only achieve the complete visibility and analysis needed to increase uptime if their devices are siloed. In India, 72% of organizations use more than ten tools for observability, leaving ample room for issues to fall between the cracks.

Maintaining steady uptime, where all digital assets are running smoothly, requires an all-in-one observability solution that allows businesses to see how customer experiences trace back to applications, how those applications are connected to the IT infrastructure, and how they are linked to other systems. It helps businesses connect all the dots, making it easier to proactively find what’s going wrong, figure out why things are going wrong, and how to fix it. 

Given the diverse array of data sources and the sheer volume of data generated by customer interactions, how can businesses effectively aggregate, analyse, and derive meaningful insights from the data to drive improvements in customer experience?

Observability is pivotal for seamless performance across cloud, hybrid, and on-premise environments and for identifying third-party performance issues that put the customer experience at risk. They proactively identify sources of latency, errors, and other performance barriers. All-in-one observability solutions tackle the problem of data silos by allowing businesses to make meaningful connections between system performance, customer experience, and business KPIs while informing decisions that boost conversions, revenue, and customer lifetime value.

Observability is central to monitoring customer engagement and interactions, making it effortless to guide businesses toward smarter site investment decisions. It empowers teams to collaborate on problem-solving and enhance customer experiences. DevOps teams are also equipped with the analytics to drive iteration and innovation, dramatically reducing manual toil. With millions of dollars on the line and many things to juggle, businesses must focus on factors that drive great customer experiences and observable solutions to help them achieve that. 

What role does the integration of observability solutions with artificial intelligence and machine learning play in the pursuit of enhancing customer experience, and what challenges exist in harnessing the full potential of AI-driven observability, including data privacy and ethical considerations?

Engineers are consistently under pressure to innovate quickly and deliver highly functional software. They are central to improving the performance of digital assets that drive seamless customer experiences, and AI-assisted observability goes a long way in achieving this. For example, integrated generative-AI observability assistants save time by providing insights drawn from the observability platform’s telemetry data through natural language prompts. This saves time by streamlining troubleshooting and democratizing observability so engineers at any level of experience and employees outside of the engineering organization can benefit from it. It identifies issues quickly and proactively, improving the software development process and customer experience.

With AI and generative AI carrying massive benefits for businesses, ensuring that all the data being generated is secure is also important. When companies look for observability solutions, they must provide data privacy built into the technology. Hence, your accounts, agents, events, attributes, and products and services are secure.

As organisations embrace cloud-native architectures and microservices, what challenges do they encounter in ensuring observability across these dynamic environments, and how can they implement observability practices that are both effective and cost-efficient?

Cloud adoption is still going strong but is increasing in pace. 78% of organizations are implementing cloud strategies to modernize the technology stack and infuse intelligence into business applications. The higher the cloud adoption, the greater the momentum around microservices, adoption of Kubernetes, and containers. These things further drive complexity into these environments, exasperating data management problems. Historically, businesses buy various point solutions to deal with these complexities, driving up the cost of cloud adoption. In the current environment, every organization is looking at driving more value with existing technology investments, and consolidating those technology investments onto one platform seems to be the direction they’re heading toward. 

This is where observability solutions can help businesses optimize their cloud investments. It enables business outcomes across people, processes, and technology by eliminating silos and offering greater visibility. Companies can improve the health of their budgets, too, because most legacy monitoring tools do not report data in real-time and need more flexibility to analyze trends over time. It leads to blind spots and higher long-term costs for maintaining and running such solutions. All-in-one solutions eliminate these silos, provide real-time environment analysis, and help businesses do more with less.

Billing issues also come into play. By paying for what you use via a consumption model, businesses can eliminate overbuying, underbuying, or vendor lock-in issues that can lead to billing traps with an all-in-one solution.

Customer experience often involves multiple touchpoints, both digital and physical. How can observability solutions extend beyond digital channels to encompass the entire customer journey, and what challenges exist in achieving end-to-end visibility to optimise customer interactions and satisfaction?

Consistency in the customer experience between a brand’s digital and physical touchpoints is crucial but challenging to execute. A retailer with a digital presence and physical stores can quickly capture the customer journey touchpoints in the digital space. However, capturing customer experiences from physical stores can be tricky because the interactions and feedback tend to be more manual. The complexity increases when customers interact via both channels since their online and offline interactions have to be mapped in context.

Today, we have multiple examples of customers buying products online. Still, the post-sale process (delivery, installation, service, etc.) tends to be manual, with feedback that may be partially digital. For a retailer, all of these constitute 360-degree feedback and experience in the customer’s journey and thus have to be captured and represented effectively. However, The primary challenge is integrating data from these touch points and feeding them into an observability platform in real-time. A single data repository combined with a unified observability platform that provides comprehensive end-to-end visibility will help address customer experience issues and continuously improve customer service and satisfaction over time.