AIOps, also known as Artificial Intelligence for IT Operations, are multi-layered technology platforms that use big data to automate and improve IT operations with the help of data analytics, machine learning (ML), and artificial intelligence (AI).AIOps platforms collect data from a number of IT/ops tools, devices, and platforms to pinpoint issues and respond in real-time. They provide traditional analytics tools as well. Organizations use AIOps tools to overcome department siloeing, since IT/Ops so often remains separate from engineering, design, and other teams. By aggregating data across monitoring systems, teams across an organization can access automation-driven insights that help organizations to continuously address improvements and enhance their digital products. This is known as Continuous Integration and Deployment, or CI/CD for short.

Agile development is an operational approach – a set of frameworks and practices that describe how developers work together in a self-organizing and collaborative fashion. Kanban and Scrum are two of the most well known Agile methodologies. In the 1990s, Agile revolutionized product development by helping teams deliver code more quickly and with less waste through greater clarity, shorter cycles and by enabling iterative and continuous delivery. Teams that practice Agile methods overcome outdated hierarchies in order to continuously respond to change, navigate through uncertain code changes, and uncover what’s actually happening in a specific environment. This helps teams figure out what they need to do on a day-to-day basis so that they can deliver high-quality software--and fast! Other Agile benefits include:

Anomaly detection, also known as outlier analysis or outlier detection, is the process of identifying data points or events that diverge significantly from the majority of the dataset. Developers, operations teams, and other stakeholders rely on a number of anomaly detection techniques, powered by machine learning and AI, to pinpoint bugs, glitches, and rare events. This helps teams to identify new business opportunities and drive conversion rates.

Customer experience analytics, also called CX analytics, is the practice of collecting and analyzing customer data in order to better empathize with customers. CX analytics helps teams to understand the entire user journey, including any pain points. The customer experience involves every step of the sales funnel and involves sales, marketing, customer service, social media, and review sites like G2. Think about the customer experience as starting the moment that the customer first learns about your product or brand. Advertising, product features, accessibility, and reliability all factor heavily into the customer experience. Overall customer satisfaction, then, can be calculated by subtracting the negative customer experiences from the positive ones. This means that each and every encounter with a brand, also referred to as touch points, matter. In the B2B context, measuring customer experience is primarily focused on how effective the company is at solving their customer's business problem.

Continuous Integration / Continuous Development, often abbreviated as CI/CD, is a set of operating principles and practices that encourage a culture of delivering code changes frequently. CI/CD is an agile methodology, generally used for application development. CI/CD automates deployment, which makes it easier for developers to focus on improving code and satisfying the business’s key performance indicators (KPIs).With CI/CD tools, developers (who need to push frequent changes) and IT/Ops (who dream of stability) can work together to ensure that applications remain as stable as possible, even during the development process.

Digital experience monitoring (DEM) helps teams to monitor and measure the user experience of customers, and sometimes employees, across an organization’s digital portfolio, including applications, services, and devices. Unlike traditional IT tools, which emphasize the technology, DEM helps teams to understand what is occurring from the user’s perspective.

Rage clicks occur when users click repeatedly on a particular element or certain area of your app or website over a short period of time, usually less than a minute. In general, rage clicks signal slow responses times, browser issues, broken elements, dead links, bugs, design flaws, and other usability issues.Users also rage click because a website or mobile application isn’t loading fast enough, though it’s also common to see rage clicking occur because of client-side JavaScript errors and console errors. Rage clicks that can’t be traced back to errors can help teams to enhance the user experience (UX) of their website or application. Misleading buttons (elements that seem like they do something but don’t) and confusing copy can cause just as many problems as JavaScript errors. For instance, a user might rage click on a visual element that looks like it contains a link, but doesn’t. In this case, the design team might modify the element to include a micro conversion. Some users engage in rage clicking by moving their mouse erratically, as opposed to actually clicking. Such movements usually indicate that users were confused, lost, or impatiently waiting for a page to load. But many people casually click as they scroll through a website out of habit or to highlight the website’s text. In this case, rage clicks can actually be a false signal.

Real user monitoring (RUM), also known as real user measurement, page performance and end-user experience monitoring, is a passive monitoring technique that allows organizations to monitor how pages, applications and devices are performing from the user’s point of view. Real user monitoring also helps teams track site speed, page speed, and overall app performance. At the core of real user monitoring is the ability to capture and analyze each user interaction on a website and gauge system performance, both front end and server side. As a passive service, real user monitoring tools function in the background, tracking things like load time and transaction paths. In fact, real time monitoring never stops. Most tools collect data from each user, across an organization’s entire digital portfolio, across each individual request. Depending on the service and goals, real user monitoring tools can give teams a view of the front-end browser, back-end database, and server-level issues. Most bottom-up real user monitoring platforms capture server-side data to reconstruct the end-user experience. Top-down real user monitoring platforms, on the other hand, focus on the client-side, meaning that they show how users interact with, and experience, an app or website. Now more than ever before, users are engaging with more hybrid environments such as cloud, widgets, and apps. Because of this shift, monitoring app usage from the client’s perspective has become a priority. So real time monitoring requires teams to collect data from various individuals and consolidate it into one database. Real user monitoring requires teams to sift through large amounts of data. To help them understand what it means, real user monitoring platforms generally offer data visualizations and segmentation tools. These features help teams understand data points from many users across many different types of metrics, all at a glance. Software as a service (SaaS) companies and application service providers (ASP) employ real user monitoring techniques to ensure that they’re delivering standout services to their clients.

Real-time digital analytics are used to collect and analyze data from a variety of sources, such as websites, mobile apps, and kiosks. At its core digital analytics is about helping organizations improve the online experience by better understanding how customers engage with digital products. To engage in digital analytics, organizations collect, measure, and analyze both quantitative and qualitative data in order to modify and enhance their business practices. Digital analytics thus leads to continuous improvement and is a core element of Continuous Product Design.

UX/UI analytics is a qualitative and quantitative approach to measuring user activity on a website or an application that provides insights into how certain features can be modified to meet the needs of current and future users. Quantitative UX analytics methods focus on measuring important data, such as how often users click on a particular feature or how much time they spend on a page. When UX designers use digital analytics dashboards and reports, they are engaging in quantitative research. Qualitative UX analytics, on the other hand, analyze how users interact with a product or service based on non-numerical measurements, such as surveys, user research, and NPS scores. UX analytics are crucial for engaging in data-driven design, which is the practice of using actual data to build products that users want and need. By turning to UX analytics, design and development teams are better equipped to understand bounce rates, track & optimize the customer journey, make the product more accessible to the target audience, uncover pain points that reduce conversion rate, and step into the user’s shoes. By focusing on macro/micro conversions and metrics, UX designers can more quickly address content & visual design issues, such as confusing wording or counterintuitive designs.

User replay—also known as user session replay, experience viewing, and session playback—is a recording of a user’s experience and interactions on a website or application. Like recorded videos, user replays capture exactly (or almost exactly) how each user navigated through a digital product, including clicks, typing, swiping, tapping, scrolling, and cursor movements. Tealeaf was one of the first tools to incorporate session replay technology, though largely for a technical audience, such as IT. Today, teams across the organization use session replay technology to monitor and improve the user experience on customer-facing and employing-facing applications across a company’s digital portfolio, including across devices such as tablets, smartphones, and desktop computers. With the help of user replay tools, teams can identify conversion-blocking problems that are related to UX design flaws, as well as technical errors and bugs. While tools such as Google Analytics and Adobe can help teams to identify where a conversion drop occurred, they do not provide insights into why the drop occurred. User replay tools, on the other hand, help teams to quickly validate issues with macro and micro conversions. For example, watching a user replay can reveal hard-to-find moments of customer friction, such as a button that isn’t working properly or a confusing form. Some user replay tools enable co-browsing with customers, which allows support agents to view a customer’s experience as they’re browsing. Advanced user replay tools tend to come with additional features to monitor and analyze data, including segmentation tools and advanced machine learning technology. For instance, many platforms allow you to watch session replays and analyze crucial behavioral, technical, and business data using anomaly detection technology, machine learning, and AI. In general, session replay tools record basic web pages built in HTML and CSS, as well as CSS animations, audio and video built in HTML5, web components, and more. Most session replay tools, however, can’t record things like Flash, Java, Silverlight, and other plugs. User replay technology can record sessions across a company’s digital portfolio, including across devices such as tablets, smartphones, and desktop computers.