analytics
Time-based analytics
An Article by Ryan SingerAnalytics apps don't tell you much about usage behavior. You might be able to see how many users performed an event, or how many times they did it. But none of the analytics packages out there are good at showing you how often people do things. Are they using to-dos once a week? Every day? Only signing into the app once a month but happily paying for years?
Time matters. You can't understand usage without time.
Collaborative Information Architecture at Scale
An Article by Brandon DornHere I describe an approach for defining new information architectures for large organizational websites managed by many stakeholder groups.
Broadly speaking, there are four general phases to the approach:
- Auditing. Begin by immersing yourself in existing content and encourage stakeholders to adopt a critical, audience-minded perspective of their content.
- Diagramming. Work with stakeholders to develop new conceptual categories that better serve audiences and organizational direction.
- Elaborating. Think through content in detail and test new categories against specific instances and edge cases.
- Producing. Prepare content teams for production using a shared database of new sitemap pages and editorial considerations that you’ve developed incrementally.
Software that nobody wants
Finding value is the result of enabling individual and group-level discovery attempts. It's not the result of everyone following one leader's gut.
What just happened is a new software product/feature was created that no customer wanted. This happens way too often. In fact, most hyper important software projects that must be done by date certain or else, have deep flaws that cause some variation of this phenomenon, flaws that include:
- Not wanted - Company specified a solution to a problem that customers don't actually have
- No Rarity - Company is pursuing an iKnockoff of existing products. The market already has two scaled competitors with working solutions, customers naturally spend budget on products that are already successful to avoid risk
- Incorrect Packaging - Customers need a website, but the company created an iOS app instead
- Incorrect Pricing - Customers need SaaS pricing, but the company created a shrink wrapped, on-premise solution with CapEx and maintenance agreements instead
The 'date scrum' anti-pattern
Date Scrum is an R&D pattern where developers are asked to estimate software project requirements upfront for the entirety of the project. After the project is green lighted and the budget is set based on the final estimates, the team then holds daily scrums to status and manage risk as they “iterate” the solution toward the release date. To some, this approach is described as doing Waterfall in sprints.
The fundamental problem with Date Scrum is that the team is de-focused from discovering the best solution. Instead they are heavily focused on delivering Something™ by the Date™. Engineers are problem solvers, and if the primary problem becomes delivering Something™ that will pass QA by the Date™, they will, with enough pressure, solve that exact problem.