Many businesses are considering the use of observability based on continuous intelligence to help improve the availability and performance of their applications and services.
Patience is not a virtue many customers have when it comes to dealing with businesses today. The tolerance for outages and slow responding apps is very low. All the more need for pro-active app performance management solutions that intelligently make sense of data to spot problems in the making and help businesses avoid them.
What is the scope of the situation? Instant gratification is becoming the expected norm these days. Customers expect a company to respond to a question or request within five minutes or less, according to Forrester.
They are even less tolerant when an application is not available or its performance is slow. Two important aspects of delivering a good customer experience digitally are availability and performance.
If an application or digital service is down, a business loses immediate revenue from that specific engagement. The dollar value can be staggering. Major online sites, ranging from Target to Amazon, experience between $10,000 to $220,000 per minute revenue loss during downtime, based on online revenue metrics calculations. And worse, a company could lose customers forever if they quickly find comparable offerings from a competitor.
Simply having services up and running is not enough. They must be responsive. Poor performance irritates customers and leads to bad results. For example, a decade ago, Amazon found that a 100-millisecond latency increase costs them 1% in sales. In 2017, Akamai found that a 100-millisecond delay in website load time can lower conversion rates by 7%. And, in 2018, Google found that when a page load time increases to three seconds from one second, the bounce probability goes up by more than 30%. These issues likely cause an even greater impact today as customers spend so much more time online.
What’s needed to improve app performance?
Many businesses are considering the use of observability based on continuous intelligence to help improve the availability and performance of their applications and services. A solution would need to collect data such as system metrics in real time and then analyzes that data – along with historical data – for performance issues.
CI systems use AI and machine learning to analyze that new and historical data. In some cases, a CI solution can self-identify relationships and dependencies among systems and applications that human operators may not recognize. Such capabilities are critical in businesses offering digital products and services. They help the business ensure their applications and offerings stay up and running and meet customer performance expectations. As such, CI can perhaps help meet customer expectations.