There are certain inalienable truths about businesses: they all want to succeed and they all want to beat their competitors. What's slightly different is how a business defines success. For a healthcare company, it might be lives saved. For an insurance company, it's the number of policies bought. For an e-commerce retailer, it's shopping basket conversions.
Just one hour of downtime cost Amazon an estimated $100 million in lost sales. Unless you were completely off the grid, you’re well aware of the performance issues Amazon and its shoppers experienced on what was touted to be the biggest Prime Day in the company’s history.
Testing is critical for organizations like NASA, the US Army, Northrop Grumman, BAE Systems, Lockheed Martin, MBDA, the UK’s Ministry of Defense and the Metropolitan and Scottish Police, where lives are on the line. As we've worked with customers like these over many years, we've noticed how much more testing is than just making sure the system works — it’s about ensuring we test for mission success and continuously optimize mission outcomes. Whether you're designing systems for command and control (C2); to provide support for complex police operations, such as hostage negotiations; or for shooting down an enemy missile, you should plan your testing and monitoring strategy to continuously test against the desired mission outcomes.
If you’re an online retailer, there’s a good chance you’re busy gearing up for the pre-holiday rush. Black Friday and Cyber Monday have been pushing retail sites to the limits of their ability to cope with surges in visitor numbers.
Earlier this month, we released Eggplant Functional v18.1. This latest version introduces a variety of new features designed with speed and efficiency in mind, and to bring Eggplant Functional and Eggplant AI closer together. Read on to see what’s in store when you upgrade.
The weather, the tennis, the football — with all the distractions, you’d think those of us on the Real User Monitoring team would be kicking our feet up, right? Not a chance! I'm super excited to tell you about our latest release: a brand-new version of our Performance Trends Report.
While it may seem like a distant prospect, Black Friday is coming and retailers are busy preparing for the busiest shopping period of the year. The holiday season is normally a busy time for us too, as we start carrying out performance tests on retail sites to get an idea of how they’ll behave when unprecedented visitor numbers put systems under equally unprecedented strain.
Some of my customers are trying to design an automated script to perform specific workflows with a predicted outcome. Unfortunately, the automated workflow they want to execute has many variations in their environment, and they’re having trouble creating a dynamic, automated script that handles environment deviation.
On May 21, 2018, Bank of America announced that it was rolling out its chatbot, Erica, to all its mobile customers. On the surface, the premise makes sense. It’s making the bank more relatable. It’s providing real-time customer support to people where artificial intelligence (AI) assistants like Siri and Alexa are becoming the norm. It doesn’t have the limitations that some phone-based IVRs have, and it aims to provide immediate assistance instead of making us wait for a human (we’ve all shouted “representative” or pressed zero dozens of times to get a real person). Erica is a great way for Bank of America to optimize the customer experience.
But let’s pull back the covers and ask some basic questions. How does Erica know the customer so well? How does Erica pull from different sources of information? How does Erica know what products and services to offer? What systems, both homegrown and third party, does Erica need to be effective?