The analytics cycle described by Gartner depicts moving beyond descriptive analytics towards analytics that answer questions of “why did it happen?”, “what will happen?”, and “how can we make it happen?” Value and difficulty both increase throughout this natural progression.


R is easier and faster to use than traditional programming languages like C++ and Java.  R is open source and was created in 1993 specifically for statistical computing.  One reason it is efficient and easy to use is because of its well-documented functions which don’t require complicated programming code such as iterative loops. Analysts can access and test functions one at a time on the fly using tools such as RStudio and also have the ability to develop a series of functions to save and execute as a statistical application.  In other words think of the SQL Query Tool which allows you to write SQL and test your results interactively, vs. a SQL stored procedure which may be called once to execute a series of statements.

In technospeak, R leverages data structures based on vectors and matrices which are key to the underlying mathematics used for data mining, optimization, and machine learning.

Why is this important for associations?  R was recently ranked as the 6th most popular programming language by an annual IEEE study, and was also the biggest mover of the top 10 languages.  Knowledge of R is rapidly increasing because it is heavily used in a variety academic programs such as social sciences.  As new talent enters the association workforce, applicants with this skill set will be in high demand.

Popular analytics tools commonly used by associations are integrating R into their core products. Tableau provides easy access to analytics using R functions with calculated fields. Microsoft recently acquired Revolution Analytics, a leading commercial provider of software and services based on R, and announced plans to integrate R into the core SQL Server product. These and other analytics applications can already leverage the power of R through integration with extract, transform, and load (ETL) scripting to incorporate timely advanced analytics into a comprehensive data layer.

Capabilities of R can help answer common association business questions:

  • What other products tend to be purchased with memberships?
    Association rules, commonly known as Market Basket Analysis, provide measures of such products using easily understood statistics
  • What characteristics are related to overall membership revenue and how much will individual members likely spend?
    A variety of regression models estimate both yes/no outcomes and dollar amounts along with the most impactful characteristics.
  • How can we categorize members and prospects by level of engagement?
    Classification algorithms such as decision trees identify unique characteristics that define different segments of individuals and assign them into groups such as low, medium, and high.
  • Did that website change or marketing campaign have an impact?
    “A/B testing” using hypothesis testing informs if changes in behaviors as a result of efforts such as website changes and marketing campaigns exposed to different groups are significant.
  • What do groups of our members have in common?
    Undirected data mining such as clustering identifies characteristics of similar member groups that potentially contribute to different individual behavior.
  • What are common and related words in blog posts, social interaction, and publishing content?
    Text mining identifies words, phrases, and concepts that describe content along with those likely to be found together to help understand interests to guide association product offerings.

Predictive analytics using R can help you understand the story in your data and help association staff make decisions with confidence!