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Lean Startup
Published in Yves Caseau, The Lean Approach to Digital Transformation, 2022
The most commonly used metrics for growth hacking are the AARRR (acquisition, activation, retention, referral, revenue) metrics, also known as pirate metrics. There is an implicit order that corresponds to the customer life cycle: Acquisition measures the number of leads that come to the product or service. Activation measures the number of customers who have become active (completed their enrollment process). Retention measures how many customers leave or stop using the service after a certain amount of time. Good practice is to do a cohort analysis (grouping together users who activated at the same time) so that the attrition rate (called churn rate) is more meaningful, based on the customer’s lifetime. Referral metrics are used to evaluate virality (i.e., the propensity of users to recommend the service or product). Finally, revenue metrics measure revenue creation. In the logic of the lean process, the metric that receives the most attention is retention, which is obviously linked to user satisfaction. The first step is usually to check that the onboarding process is working, which is measured by the activation/acquisition rate. The second step is to make sure that the customer is creating value, which is measured, among other things, by the retention rates. Only then can we work to promote the virality of the product.
Injury control: using novel analytic methods to enhance advocacy and policy response
Published in International Journal of Injury Control and Safety Promotion, 2018
Some of the modern statistical and analytical methods that have been used recently include video data analysis, age-period-cohort modelling, spatial regression modelling, geographical information systems (GIS)-based spatial analytical methods and social network analysis (Li & Baker, 2012). Video data analysis, for example, has the advantage of data collection accuracy associated with events that occur in a fraction of a second and may thus be inaccurately captured by the injured individual or an observer such as a rupture of the anterior cruciate ligament during a sporting event. Caswell, Lincoln, Almquist, Dunn, and Hinton (2012) used video data analysis of injury data to provide an objective and comprehensive identification of the mechanisms of injury as well as game characteristics associated with head injuries in girls’ high school lacrosse. Play at the goal area was found to be associated with increased head injuries at the varsity high school level, suggesting the need to review and possibly increase penalty calls during these situations. The age-period-cohort analysis is also an analytic tool that is used to partition trends into components that are associated with changes over time within a given age structure of the population, time period and birth cohort. It can be a useful analytical method to uncover hidden patterns in rates over time in order to inform targeted intervention programmes in specific demographic groups like the opioid epidemic (Huang, Keyes, & Li, 2018).