Being a data driven organization

Being a data driven organization

David Taylor, notes by Josh Levinger (Citizen Engagement Lab)

what can we learn from obama campaign?

• they learned it all from silicon valley

what do we mean by data?

• raw chunks, metrics, informed decision making
• dashboards

how do we act on data we collect?

• only produce analytics that are actionable (vs reportable?)
• vanity metrics are killing startups (clicks, hits, users)
• actionable ones key (cost per acquisition, revenue per user, dropoff rate)
• very similar to member-funded orgs (dollars raised per user, value provided to them)
• * lead generation -> conversion -> lifetime value
• pirate metrics talk

what is most central metric? just actions, dollars?

• twitter used avg engagement per user per day in their early days)
• campaigns could use virality, or growth velocity (recruitment)
• ability of people to spread your message (shares)

knowing what your goal is as organization

• what are we trying to do in the world?
• at the end of the day, what do we care about?

most of the things that are measurable are best for lead conversion

• 90% of what we can measure are vanity metrics
• define qualitative metrics / proxies for real world engagement (clicks vs higher bar asks)
• move people up the engagement ladder

fundraising culture is far behind software development in following pace of change

• rigidity (obliviousness) vs plasticity
• how do we create pressure on foundations to change?
• some orgs stuck in loop of email list growth & rapid response
  • key performance indicators should be balanced against each other
• data collection and decision making systems don't talk to each other
  • by data we mean constituents (and their interactions with the org)

How do we make this easy?

• you need a lot of data before being metrics driven makes sense
• collect everything you can, go back and design what's relevant later
• use automated tools that are built for this (kissmetrics, mixpanel)
  • google analytics gives you traffic numbers (vanity metric)
  • cohort tracking, not just spikes
• decide on one kpi per channel (+- on baseline, something that indicates health)
  • know your baseline, and the sector baseline
• start with goals, work backwards
• * but check that it fits your organization model

visualization helps connect between different staff cultures

• intelligence dashboards
• chart.io (visually build charts from many data sources)
• make everything transparent, everyone in the organization has access to every graph

design tests, knowing most of them will fail

• but with limited resources "burning" them can be scary
• phrase a hypothesis vs "fine tuning" or optimization
• when is there an end any more?