Excellent Research
There's Research... and There's
We have so many books, authors, and models telling us similar things, especially when their model is a variation of the Scientific Method: Understand your target audiences. Research their tasks and experiences. Step away from your desk, talk to representative members of the Experience Ecosystem, and observe their realities. Discover opportunities to serve these people, solve their problems, and make their lives and work better.
- Business strategy books say that customer-centricity is key to your competitive advantage.
- Marketing books say that you should understand your target audience and the market.
- Product management books say that we must have multiple conversations with customers frequently. Understanding our customers leads us to find opportunities to create delight and innovation.
- Design thinking books say that empathy is the foundation of good design, and we can’t truly empathize with people we don’t understand well.
- Lean and startup books talk about “validated learning” and getting out of the building to talk to potential users, which sound like “research” and “research.”
- Sales books say that you should know your customers’ pain points.
- Books MBA students read treat customer research and market analysis as business fundamentals. Books and courses are filled with case studies of companies that succeeded or failed based on what they understood about their audiences, and how they chose to address that market.
And yet we’ve mostly ignored these messages. We treat research like a cuttable corner. We pretend we are “leaner” if we cut anything from our process that appears to take noticeable time. We keep the “work fast from assumptions” advice that we wanted to hear, and ignore the “do thorough discovery research before trying to solve problems you don’t understand” piece we didn’t want to hear.
When every company is fast, you win when you deliver quality and value.
It’s hard to solve problems when you don’t fully understand the problems or your Experience Ecosystem. It’s hard to find opportunities to improve or innovate when you chose Minimum Viable Research over setting teams and projects up for more efficient success.
Delta CX uses qualitative research, human-to human conversations and observations, to learn your market’s tasks, behaviors, decisions, collaborators, preferences, and realities.
We can answer the hows and whys, and deliver actionable suggestions on what to do (and not do). For an example, see our case study about a hosting company struggling with funnel abandonment and low conversion rates.
ICPs: Shift from Demographics to Behaviors
Do all 50-year-old women behave the same? Need the same things? Have the same levels of savvy, knowledge, or experience? Of course not. Yet companies still segment and group people by demographics as if it’s 1950s America, conformity is key, and you can count on every housewife having the same amount of money, 2.7 children, and identical shopping behaviors.
“Personas” got muddy, complicated, and non-actionable, so we tried renaming them… customer profiles, segments, and target audiences… but that was a poor solution for a different problem. Call these groups anything you’d like, and redefine their parameters.
Reality is diverse.
You can’t say that you understand our market or audiences if you don’t understand them. You can’t have empathy for people you avoid understanding.
Assumptions aren’t empathy. Role-playing or imagining what people might do isn’t empathy. False empathy can push us down the wrong paths. Teams that “really knew their customers” are surprised later when utilization is low, nobody cares about the new feature, or everybody who said they’d pay for it… won’t.
Delta CX uses properly planned and recruited qualitative research to help companies identify or refine their ideal customer profiles, segments, and personas. Shift away from assumptions and demographics.
Streamlining
A/B testing is normally done after products or services are live since you want to compare how A, our existing offering, performs against B, a changed or new version. We can remove false confidence by shepherding B through Delta CX’s Atomic Product-Market Fit process (or any good variation of The Scientific Method), before we build, release, or A/B test. But for many teams, B is their next guess dressed up as their new “hypothesis.”
Teams love A/B testing because it feels scientific, and the data makes the decision. But A/B testing won’t be the right tool for every job. It’s for optimization, not design, problem solving, or innovation. You can’t optimize your way out of strategic problems.
A/B testing has some downfalls, including:
- A/B testing requires you to create products or services that are available to the public. You won’t learn if something works or not until very late in the product or service development process. By then, you have invested time, money, and resources in live code, physical products, or personal services.
- A/B testing doesn’t tell us why something is better or worse. Something we’re measuring happened more or less often. Why? We claim that we A/B test to learn, but what did we learn? We don’t know more about what users need, so we don’t know what a significantly better design would look like. It’s FailureSquared™: failure to learn from failure.
- A/B testing can’t tell you what to design or build. We learned that something happened more or less frequently, but what should we change? What should remain unchanged because it’s working? A/B testing isn’t a design or problem-solving technique.
Strategic and streamlined A/B testing.
The best way to have more successful A/B tests is to have higher quality standards for B. Your customers have higher standards and expectations. Don’t disappoint them.
Evaluative research techniques can tell us long before an A/B test if B is likely to succeed. We learn all of this while B is still a concept or prototype. Failures are behind the scenes rather than being public releases competitors, investors, and the public sees… while paying customers are treated like laboratory rats.
Evaluative research tells you weeks or months before an A/B test whether B is worth building at all. That’s time, money, and dev cycles back in your pocket.
It’s also a more efficient path for innovation. You can’t A/B test your way to innovation. If you want fresh, new ideas that become competitive advantages, helping you leap over the competition, cycles of small experiments are unlikely to get us there.