With the Shift-Up series thus far, we have explored the importance of testing and thinking as a customer. The basic premise is that we need to add another dimension to Quality Assurance other than Shift-Left and Shift-Right. This new dimension focuses on how your customer is actually using your application and if the intersection of your application, customer behavior, and your company’s business objectives all align.
Performance testing just got a lot easier.
While delivering quality at scale is essential, simply setting up a performance test can be a challenging and time-consuming exercise, and then you need to analyze it. So, despite its importance, performance testing is not commonly used during development cycles.
“Effortless Performance Testing” in Eggplant Performance 9.0 changes all that, with a simple-to-use load testing capability that re-uses your existing Eggplant AI assets. So you can now run performance load tests on your system with no technical knowledge and no coding.
Click. That’s the sound of a customer seeking out your competitor because your point-of-sale (POS) system didn’t deliver the experience they wanted or expected. You know that your QA teams tested the code and it worked. So, what happened?
No matter what industry you’re in, providing an exceptional digital experience to your customers is paramount. It’s particularly tricky in financial services, as more and more users ditch physical branch locations for online banking. By 2019 in the UK, mobile banking is expected to overtake desktop as the preferred channel. And for two-thirds of Americans, a recent survey found that online and mobile banking represent their primary banking channels.
To keep up with DevOps, testing and QA teams typically adopt a shift-up approach to move quality further up the software development lifecycle. The goal is to complete system testing, integration testing, and user acceptance testing (UAT) to ensure a bug-free release. While product quality has a direct correlation to increased revenue and positive business outcomes, this isn’t enough in the 21st-century marketplace. QA’s job isn’t just to de-risk applications by finding defects earlier but to help de-risk business strategy and potential problems with your user base by reporting customer experience defects.
Customer experience transformation is a key initiative for any business that wants to position itself for the 21st century. Two important concepts involve updating and digitizing technology, and creating persistent customer relationships. According to Bain & Company, customer experience transformation starts with “… simplifying your core business and digitizing it where it matters.” McKinsey & Company writes that in any customer experience transformation, “… the voice of the customer can be used to identify upstream and cross-functional issues and address the root causes of problems.” In short, to see positive results, you need well-tested, high-quality digital assets that reflect ever-evolving customer needs and desires.
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.
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?
Sometimes I feel as if I’m the Forrest Gump of quality assurance (QA). Since 1998, I’ve been through the beginning of automated integration testing and service virtualization through being a co-founder of Class I.Q. (now IBM Greenhat). I’ve been through the first phases of an automated testing center of excellence (ACOE). I’ve been there for the start of risk-based testing, and I’ve been a part of the transformation of QA from a somewhat necessary function to something that is now the core and chief concern of any company putting out quality software and apps.