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Discovery & Research · master area · gap

Quantitative Research ​

Surveys, analytics, usage data as Discovery input — alongside, not instead of, observation. Numbers tell you how much and how often; only observation tells you what is happening when the number says so. Currently a gap in the corpus.

This is a gap area

The chain has a clear position on observation. It does not yet have a considered position on quantitative methods at Discovery time. The risk is symmetric: a team that uses only observation misses the scale signal; a team that uses only quantitative misses the meaning. Filling this is part of the corpus's job.

Owners: PO, Data Phase it would live in: Before We Build (Volume II)

What the practice would look like ​

Quantitative methods, applied to Discovery, with the chain's observation-first reflex preserved:

  1. Usage data as anchor selection. Before deciding who to observe, the team reads the usage data to identify outliers, segments, common paths. "60% of graders finish in under 30 min; 15% take over an hour. Let's observe one from each group." Quant chooses who; observation tells us what.
  2. Surveys for breadth, observation for depth. A survey can answer "how many graders use the workaround?" — but only observation answers "what does the workaround actually look like?" Quant scales the question; observation scales the answer.
  3. Cohort analysis as Discovery input. "Are new graders behaving differently than experienced graders?" If the data says yes, the next observation session has a sharper anchor.
  4. A/B tests after launch, not before. A/B tests answer "which variation performed better." They do not tell you what either variation meant to the person. They are a verification tool, not a discovery tool.

Why this is a gap ​

The chain's witness-first principle is uncompromising — and right. But a team that only observes ends up generalising from N=5 to a population of 5,000. Without quant, the team cannot answer "is what we witnessed representative?"

The reconciliation: quant provides the question, observation provides the answer. Done in that order, the methods don't compete — they layer.

What it would NOT be ​

  • Discovery by dashboard. "The funnel shows a 30% drop at step 3." That's a phenomenon, not an understanding. You still have to go watch.
  • Survey as Voice of Customer. "82% of users want feature X." That's a wish-list, not a model of the moment.
  • A/B test as discovery. "We tested two designs; the one with bigger buttons won." That tells you nothing about why. The wallet bug shipped a design that A/B tested positively — until the negative-balance case surfaced.

200apps · How We Work · NWIRE

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