#customerexperienceassurance

[vikram739]

How can Real-Time Visibility and Predicting End-User Experience Go Together

Delivering a superior end-user experience has become the sine-qua-non for the success of any software application. In addition, real-time digital experience monitoring can assist businesses in staying ahead of the competition. The practice of tracking user behavior while the latter interacts with a website, mobile, or web application can yield valuable data for stakeholders to understand the former’s behavior. Monitoring the digital customer experience is considered a key capability of an end-to-end application performance management solution.

In fact, business enterprises can identify application glitches/issues and minimize any mean time to repair (MTTR) by tracking every customer transaction in real-time. Today, modern applications speedily deliver a host of end-user services, including the popular voice technology. The humongous data generated therein can overwhelm the IT systems. This is why businesses should ensure the reliability and performance of distributed systems across multi-cloud and on-premise environments.

Why is real-time visibility into the end-user experience important?

Any real-time visibility into customer behavior garners critical data about the browsing and buying patterns of customers. This can help enterprises tweak the quality of their products, improve the supply chain, and track their assets better. Also, it enables businesses to get real-time insights into data-driven predictive customer decisions and augment capabilities. These capabilities may include customer branding, personalized product recommendations, intelligent and secure communication, driving efficiency, customer scheduling, and improving capacity utilization, among others. By providing an omnichannel customer experience and real-time visibility into the deliverables, you can help build customer confidence in the product or service.

Digital experience monitoring for individual end-user transactions can help determine the performance of a website, web, or mobile application. Teams, using performance monitoring tools, can simulate customer interactions and record, test, and monitor test transactions in an omnichannel environment. By using a digital experience assurance tool, teams can monitor the performance of digital platforms across channels, devices, and geographic locations and come to know of any performance issues in quick time. This preempts the possibility of such issues turning into serious outages.

By monitoring the performance of a digital customer experience platform using tools, businesses can evaluate site performance, eliminate errors, save time, and improve the efficiency of processes. It can also offer a centralized view of key performance indicators and business-critical site performance metrics that are easy to understand. Monitoring any digital experience platform can deliver instant visibility into the end-users’ interactions with the platform and help stakeholders understand their behavior.

In manufacturing or retail, for example, offering real-time visibility into the inventory data right from the point of distribution to the point of sales can achieve a host of benefits. These include maximizing operational output, optimizing stocking, and driving better financial outcomes. An insight into the inventory data can significantly reduce shopping cart abandonment given that customers are willing to abandon their cart if some items are not in stock. Transparency in retail stock positions can motivate customers to purchase products if they see the availability of limited stock of items.

How to provide real-time visibility into the end-user experience

Predicting customer buying patterns can be the touchstone to improving the delivery of service, augmenting inventory stocks in real-time, and eliminating supply chain bottlenecks. This is where digital tools such as Artificial Intelligence (AI) and Machine Learning (ML) can help enable supply chain competencies, make them resilient, and improve agility and predictability. AI and ML systems can learn continuously from data generated in the supply chain and dynamically update the automated systems. These enable the management of supply chains to become more efficient and predictable and help match supply to demand.

Another methodology to transform supply chains is hyper-automation. Here, Robotic Process Automation (RPA) combines with AI and ML to streamline supply chains and make them more resilient. Since RPA automates various repetitive tasks in the supply chain, it helps to reduce errors and frees employees to execute more complex tasks. Both RPA and AI can complement each other to predict risks, analyze data, and improve decision making. The future of supply chains will be powered by real-time data to deliver accurate forecasts.

Conclusion

Business enterprises need to deliver a seamless customer experience for success. Any level of customer experience assurance can only be achieved if performance issues are monitored in real-time using specific tools. These tools provide in-depth insights into the customers’ journey and key performance metrics to help prevent outages and bottlenecks.

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James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: nasscom.in

[vikram739]

Why is Digital Experience Monitoring Important for Business Enterprises?

With digitization having an overarching presence in every aspect of our lives and the economies of nations, it is important to understand how the same is benefiting end-users. It is no one’s argument that technology alone can provide solutions for all intractable problems. Care has to be taken to ensure the technology is adaptable, easy-to-use, secure, and high-performing for the end-users. This is because the end-user experience is ultimately what matters as far as the outcome of digitization for enterprises goes. It helps to drive customer acquisition and retention - the holy grail that every enterprise seeks. Further, with more enterprises migrating their assets to the cloud and an increasing number of people working from home, it has become a challenge to keep track of things on a real-time basis. This is where digital experience monitoring (DEM) enters the scene to monitor user experience, detect performance issues, and improve processes, among others.

What is digital experience monitoring (DEM)?

The process of digital experience monitoring enables enterprises to analyze the efficacy of their processes, services, and applications. It helps to bridge the gap between how an application performs in real-time and how the customers use it. This way, it offers an insight into the whole digital customer experience. As a hybrid capability blending end-user experience and infrastructure monitoring, it helps connect software deployments, IT purchases, and service provisioning, among other elements of a business.

The comprehensive DEM process uses metrics like net promoter score and revenue to measure the success of any enterprise in delivering superior user experiences. If the conventional tools for monitoring performance (and other parameters) are mostly from a technology perspective, a digital experience assurance tool can help to understand the user perspective. Even though statistics point out its increased adoption by enterprises,15% to 70% (2020 to 2025) and generating revenue of between $600 million and $800 million at present (Source: Gartner).

Why is digital experience monitoring important for enterprises?

Thanks to digitization, the various arms of a business enterprise have become increasingly intertwined. The synergy unleashed by the process of digitization aims at achieving a seamless customer journey. But does this happen all the time? Experience suggests that the IT operations involved in managing the front-end and back-end of any enterprise are not always maintained and operated by a single centralized entity.

Instead, in most cases, specialized teams manage the functioning of individual departments within the IT landscape of an enterprise. These teams may include cyber security, digital marketing, web development, and social business, among others. These teams, in most cases, work independently in silos with their own intrinsic communication systems and jargon. To achieve superior customer experience assurance, all cogs in the wheel as mentioned above must operate seamlessly. However, things can often go wrong for the customer journey, with issues such as system latency, outages, disruptions, or downtime rearing their ugly heads.

A digital experience assurance tool can help identify the issues, glitches, or vulnerabilities plaguing the system and contribute to the outages, latency, downtime, or disruptions. It is only after assessing the root causes of such issues that the tool can recommend suitable solutions for mitigation. Since the digital customer experience platform combines both human and machine-enabled interactions, the solutions offered for performance monitoring are optimal. No wonder many business enterprises are adopting DEM instead of the traditional way of monitoring the end-user experience.

Analyzing the efficacy of an enterprise’s applications and services

The real capability of any digital experience platform lies in analyzing the efficacy of the IT infrastructure, services, and applications of an enterprise. This can be done by implementing activities such as real user monitoring, synthetic transaction monitoring, endpoint monitoring, and network visibility. Let us discuss these in some detail:

Real-user monitoring: It entails capturing the performance data from an application’s perspective without installing any device at the customer’s end. It helps to conduct root cause analysis of the problems related to the performance of the application and help mitigate them. Real-user monitoring evaluates the performance of the application across devices, operating systems, browsers, and networks. It also involves measuring voice capabilities and browser interactions.

Endpoint monitoring: It assesses the application’s performance using a piece of code running on the device. Suitable for monitoring remote applications, endpoint monitoring can help identify the impact of any app configuration changes on the users.

Synthetic transaction monitoring: It makes an estimation about the application’s user experience by testing the performance of all IT entities (services, components, and networks) that are involved in deploying the application. This can be a great choice to analyze the performance of SaaS applications and their impact on the user experience.

Network visibility: One of the major bottlenecks that business enterprises or their end customers often face is that of the network through which the traffic flows. Network visibility allows businesses to better understand the traffic behavior on their networks and work towards improving the performance, efficiency, and security of such networks. It is about having an awareness of the various components that are part of the network to get insight into the performance, analytics, and user traffic. Here, data pertaining to the performance of an application is collected, organized, distributed, and delivered to be used by the tools.

Conclusion

A strong DEM strategy helps to tie the digital initiatives of enterprises to their business outcomes. It enables businesses to be competitive and transformational in driving partnerships, growth, innovation, and setting a high-quality benchmark. By leveraging a robust customer experience assurance platform, businesses across domains can unlock new opportunities, innovate, streamline processes, mitigate risks, derive superior user experiences, and achieve growth.

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James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: wattpad.com 

[vikram739]

What are the Use Cases and Benefits of Customer Sentiment Analysis?

A customer may display varied emotions (positive or negative) while engaging with a brand. These emotions determine if the customer is likely to revisit the brand or junk it altogether. According to a McKinsey study, more than 85 percent of customers are likely to purchase from a brand again after having a positive customer experience. A negative customer experience, on the other hand, can reduce the number of customers by 70%. So, how do businesses know whether their customers are happy while engaging with their brand? Instead of merely depending on assumptions, the answer lies in analyzing customer sentiment.

Customer sentiment analysis can let businesses know about the different emotions customers go through while dealing with a particular brand, product, or service. Customer experience insights can help brands offer more of the elements that make customers happy. Moreover, it can improve the customer satisfaction index, customer lifetime value, and customer loyalty, thereby maximizing profits. So, let us discuss this important metric in detail.

What is customer sentiment analysis? 

Customer sentiment analysis helps detect customers’ emotions when the latter interact with a brand, product(s), and service(s). The process leverages Natural Language Processing (NLP) and algorithms to identify whether the emotions are neutral, positive, or negative. Here, “neutral” means the customers are happy with the brand but expect more. The algorithms evaluating customer sentiment use two parameters to categorize:

Polarity: Shows whether the emotions are positive or negative.

Magnitude: Shows the intensity of the emotions.

Further, the sentiments driving customer experience assurance can be analyzed from customer responses in the form of comments, tweets, or feedback. This process is used by businesses to understand how their customers feel about their brand, products, or services and to improve them. This helps them drive sales and stay competitive.  

What are the benefits of customer sentiment analysis?

The process of customer sentiment analysis is leveraged by various businesses for real-time user monitoring, improving customer experiences, and driving profits. The benefits of the process are as follows:

Deliver better customer service: Since bad customer service can make the customer feel unwanted and uncared for, the chances of him or her rejecting the brand or its products and services are fairly high. However, by analyzing the sentiments of customers while using specific products or services, businesses can gauge the issues (if any), and work towards mitigating them. The process offers insights into the negative emotions experienced by customers and the factors responsible for them. Thereafter, businesses can eliminate the factors that contribute to the negative sentiments.

Enhance the quality of products and services: Sentiment analysis can not only capture complaints from customers but alert businesses about the inherent vulnerabilities/issues/glitches plaguing the products and/or services. These can then be fixed by businesses, thereby improving the quality of products and services. Customer responses analyzed through the process can act as a guideline for quality improvement, better product development and service delivery, minimizing customer churn, and enhancing product marketing and presentation.

Develop better marketing strategies: With more insights gained from using the customer experience assurance tools, business enterprises can develop an effective marketing strategy. Further, specific marketing campaigns can be created for the target audience by observing customer conversations related to the brand.  

Increase sales revenue: With sentiment analysis capturing the impressions of customers towards a brand, products, and/or services, businesses can work towards optimizing the quality of the product/service. This can make the customers feel good when they come to know that the organization is all ears to their problems. This “feel good” sentiment can enhance their trust in the company and drive them to buy more, thereby increasing the company's sales revenue. 

Improve crisis management: Customer complaints can quickly turn into a crisis situation unless they are promptly addressed by taking cues from sentiment analysis. By taking timely preventive actions, businesses can nip any crisis in the bud and save themselves from inviting a chain of negative comments in the public domain.

Use cases scenarios

Some of the use cases to enhance user experience monitoring are:

Identify your key promoters and detractors: Customers are wont to comment on various aspects of a business, and it may not be advisable to get worked up with every comment of theirs. Businesses need to identify the areas/aspects that may affect their NPS score using data science. It is only by correlating such aspects with both promoters and detractors that businesses can understand the ones influencing the NPS score more.

Target detractors: It is said that negative comments travel fast and their impact can be more debilitating for the business. Think about the scathing remarks made by such detractors on social media and the impact they can have on people’s perceptions of a brand. So, how about targeting such people spewing venom about the brand on a priority basis? This can be done by approaching them, understanding their pique, and working towards defusing the situation. For instance, if an apparel company identifies people who are not happy with the return process and works towards mitigating their issues, they can even be turned around to become promoters of the brand.

Track customers’ sentiments: Mere tracking of the NPS may not always yield the right results. What is needed is to track customers’ sentiments related to the specific areas of a business. For example, customers buying merchandise from an eCommerce portal may not be happy with the latter’s return policy and customer service. It is only by tracking their sentiments over time that the portal can improve these segments and address the issues plaguing customers.

Conclusion

After understanding the nitty-gritty of customer sentiment analysis, businesses need to apply the same, especially to customer feedback. Starting with collecting data using marketing surveys, the next step is to analyze the data and assign scores. This can either be done manually or by using specific software applications. Finally, the outcomes of this analysis need to be implemented to garner great results.  

Resource

James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: community.nasscom.in

[incight]

Customer Experience Assurance | App Ratings and Reviews

Enhance your CX with Incight, predict digital customer experiences, app rating & reviews that includes product analysis of pre, post & real-time data.