Almost every association professional I meet with these days is trying to figure out how to take advantage of the data the organization collects. The answer is surprisingly simple even if you don’t have a data scientist on staff or a sophisticated data-mining tool.

If you have a basic understanding of Excel, you’re off to a good start. With some creativity and straightforward spreadsheet modeling, you will be able to extrapolate useful data. The real challenge is making sure you ask the right questions.

For example, let’s say you’re trying to improve retention, especially among first-year members, who often account for the majority of attrition [PDF]. One way to approach your data is to look for a correlation between member satisfaction and engagement that leads to high retention. A simple formulation for the question is: What do members with a high degree of satisfaction have in common? And of those commonalities, what do members with lower satisfaction not have?

No need for a data scientist or a business-intelligence tool to answer these questions. If you have a basic familiarity with Excel, you can find the answer. Follow these four basic steps:

If you have a basic understanding of Excel, you’re off to a good start. With some creativity and straightforward spreadsheet modeling, you will be able to extrapolate useful data.

1. Choose metrics. Pick four or five key metrics tracked in your database that you think make a difference in satisfaction and retention. These might include whether a member attended the last annual conference, recently attended an educational event, is involved in advocacy or volunteerism, or is a member of a committee.

2. Assign values. In your spreadsheet, assign an appropriate value for each metric, weighted by members’ perceived value (for example, 20 points if they attended the annual conference, five points for a webinar). Then tally them to create an individual engagement score (IES) for each member.

3. Compare data. If you have tracked member loyalty or satisfaction using the net promoter score (NPS) methodology, you can compare members’ IES against that score to determine the correlation. Depending on your point allocation, another simple approach that often provides fairly accurate results is adding the IES and NPS together. Lower scores represent the members with the lowest level of engagement and satisfaction. These are the members most at risk.

4. Assess members. If high metric tallies correlate with high satisfaction while low tallies correlate with low satisfaction, you now have some valuable insights. Although a member’s score demonstrates correlation, not causation, you can combine it with other data points to learn more. For example, if you are able to include the dollar amount each member has spent in a given year or whether a member has achieved a specific certification, this information can help you predict which members are likely not to renew.

Now, you can examine first-year members in detail to assess the likely rate of renewals among this key group. Each year that you employ these measurements, you gain insight into the accuracy of your predictions and the success of your intervention initiatives.

Mining your data using a spreadsheet is an inexpensive way to build a deeper understanding of how your members are engaging in your association’s activities, and this knowledge can help improve marketing and membership efforts. Don’t let all the talk about big data and data science distract you from figuring out exactly what types of data you really need to know to solve your problems. Often the questions, and answers, are simpler than you think.