Despite the global pandemic and subsequent economic downturn in 2020, there are some companies that are experiencing accelerated growth. Companies such as Verizon posted 242% growth in Q2 when the majority of businesses were still trying to figure out how to operate in the new normal. Amazon, arguably the leader in customer obsession, is described by Fortune Magazine as a company built for a pandemic. As proof, their sales surpassed $10 billion in the first quarter of 2020. Retailers such as Best Buy and Target also posted exponential revenue gains in the second quarter since they were able to quickly adapt to changing consumer behavior. Demand for curbside pickup increased substantially and a new challenge presented itself: How to optimize the customer experience without the benefit of face-to-face interactions?
With social distancing becoming the norm, customers switched to online shopping and optimization efforts became more targeted on the digital user experience. There was the realization that in order to achieve short term revenue targets, optimization efforts needed to mature and accelerate. User experiences needed to remain relevant and agile, and companies needed a way to rapidly incorporate the Voice of the Customer (VOC) into optimization efforts. Increasing the velocity of testing wouldn’t suffice because it doesn’t generate answers quickly enough. And there were just too many variables attached to the digital user experience. Which were the right ones to experiment with?
Efficiency of testing vs optimization
One of the best analogies for comparing the efficiency of testing and optimizing is this: You can test the temperature of water, but optimizing the temperature of water is an entirely different thing, because the optimum temperature will vary from person to person. The reason that companies such as Verizon, Best Buy and Amazon are achieving exponential growth is not just because of their customer obsession—although that’s a big part of it. They’re actively working toward leveraging the VOC and technology to optimize the entire customer journey based on real-time customer feedback.
They’re asking the questions: How can we extract more value from our optimization efforts? Can we grow specific product lines? Are there efficiency gains to be made? Are we optimizing for growth and what are the key metrics we should be looking at? And perhaps most significantly: How fast are we getting answers compared to how fast we need them?
Most companies are focused on multi-year digital transformation initiatives and are in various stages of deployment. But the pandemic has accelerated the need to optimize the digital user experience and do so more efficiently. It’s making companies realize that in order to remain competitive in the marketplace, they need to mature their testing and experimentation capabilities. This means having the ability to scale and accelerate their efforts with a focus on including all the variables influencing user experiences and looking at the entire customer journey. This goes beyond testing in terms of scale, efficiency and speed to get results.
As the water temperature analogy illustrates, testing can indicate which ideas could be implemented. And in the case of A/B testing, which one of two ideas would be better. But it can’t always show which ideas or combinations of ideas are most effective at getting users to actually buy. In other words what is the optimum water temperature that is driving conversions? This is a major difference between testing and optimizing. Optimizing not only takes into consideration many more ideas, it ties testing and experimentation efforts to specific outcomes. It makes testing more effective because the results are linked to specific metrics such as growth in terms of revenue, customer acquisition or customer retention. Optimization helps transform testing results into meaningful gains, making it more effective.
AI scales customer obsession
In the marketplace there are thousands of factors that influence buyer behavior and they are changing all the time. The number of tests and amount of time it would take to experiment with all these variables and extract meaningful data in a timely fashion is overwhelming. That’s why, at some point, digital leaders start to realize that they have outgrown traditional testing capability. And why companies such as Verizon started to look for advancements in technology that could help them scale. They shared in more details their journey and rationale in our Think CX, Part 6 webinar.
They wanted to be able to respond faster and more accurately to the voice of the customer (VOC). Because listening to the VOC would enable them to optimize the customer experience—which in turn could drive growth.
AI, with its ability to handle the many variables and complexity of a dynamic marketplace, is a natural choice when looking to scale testing and experimentation efforts. It enables digital leaders to hone in on specific primary and secondary metrics. And because it’s extremely efficient at processing vast volumes of data, AI is ideally suited to handling the complexity of a highly dynamic marketplace with high and ever-changing customer expectations. It takes the proven testing principles of A/B and multivariate testing (MVT) and gives them the advantage of being able to factor in the VOC, strategize for specific metrics and implement ideation at AI scale. It’s taking the water temperature and being able to make sure it’s just right for each individual customer, every time, every day, and be able to adapt to changes. It’s taking the strategy of customer obsession and connecting the dots to achieve meaningful business results that impact the bottom line.
