knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) options(tibble.print_min = 5, tibble.print_max = 5)
library(dplyr) library(ggplot2) library(zoo) load(file = "./data/product_data.rda")
As a product owner, you want to understand how users are interacting with your product, in order to improve adoption and engagement, but also align the product’s success with business objectives.
To measure how improvements impact users individually and the business as a whole, you’ve set up a strategy and a tracking plan that defines what are the most important events in your user’s journey.
RProductAnalytics provides utilities to analyse those interactions, how your experiments and introduction of features are impacting key metrics, product-market fit and overall alignement with business objectives.
install.packages("devtools") library(devtools) install_github("lantrns-analytics/RProductAnalytics") library(RProductAnalytics)
Let's take this initial product data from our newsletter as an example.
You first isolate the metric that you want to work with and specify a windows which will define the baseline to compare with.
product_metric <- RProductAnalytics::prepare_metric( product_data = select(product_data, id, send_date, metric = total_recipients), baseline_window_width = 3 ) tail(product_metric)
With that new data frame, you can do some analysis of how that metric evolved through time
ggplot(product_metric, aes(send_date, metric)) + geom_line()
ggplot(product_metric, aes(send_date, metric_baseline)) + geom_line()
ggplot(product_metric, aes(send_date, metric_baseline_diff)) + geom_line()
ggplot(product_metric, aes(send_date, metric_baseline_diff_perc)) + geom_line()
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