The rise of data-driven decision making has coincided with a resurgence of design thinking. Both processes are now seen as critical to delivering the ideal customer experience. To further understand the area we spoke to Joe Cincotta, director of Thinking Group.
Just how important is data in design thinking for the customer experience?
It’s essential. The problem with not using data is our own psychological biases affecting the decision-making process. Primarily “availability bias” and “false consensus” are two key problems.
Humans typically use mental shorthand that ignores data and looks for readily available examples. The problem with this is it ignores actual customers in the customer experience design process.
The same can be said for “false consensus,” where teams of people make the same mistake of not paying attention to the data.
How are leaders leveraging data to improve the customer experience?
Great leaders are using data to constantly inform the experience design process — not just once, but in cycles, where they use experiments to test new ideas that are informed by data and then measure those tests against control groups to understand the impact of their experiments.
This happens in different ways through the product design process, from prototyping to systems in production.
How can data be sourced for data-driven decisions?
There are a few ways of doing this based on where you are in the product development cycle and the business model you are operating within.
Immersion — A G Lafley, former legendary CEO of Procter and Gamble, who was responsible for doubling the company through the 2000s, forced his people from everywhere in the organisation to spend time with actual customers.
As an example of how serious he was about the idea that “The Customer is the Boss”: when P&G launched a product in India, he flew product managers from the United States to live with families in India to deeply understand their context.
Engagement — Spending time with customers to use prototypes and observing their behaviour is a foundation of design thinking, but it’s also a foundation to good prototype design principles.
Analytics and telemetry data — Bottom line, measure everything you do and understand what your objectives are when you go in to measure. What is important: speed of providing results, or number of page views?
Each measure would make a huge difference in the way you design to yield a “great result” versus actually helping the customer.
Get super clear on the customer and then the metrics should cascade from that. Then measure the shit out of it. Live and die by it.
Is data use about influencing decisions, validating decisions, or both?
Both. We want data to inform inspiration for our experiments. We also want data to validate the success or failure of our experiments. A great way of looking at this is captured in a chapter in the book by Charles Duhigg called Smarter Faster Better: the concept of “disfluency”. It’s the idea of forcing us to interact with data to actually understand it.
The real value of data comes from interacting with it, not the passive observation of it. This is true for leadership as well as front-line teams.