Today I’d like to share one application for using big data in a local church. This post will describe how I determine the Crowd, Connected, Core, Leadership layers (where do these terms come from?) of our church (Greenwood) with big data. You might notice that the software I’m using is Planning Center People. You can run similar queries in any church database software.

Using Big Data in a Local Church to Determine Connections

Previously I shared a post that described the theory behind Big Data in a local church. Simply put, you can use data on attendance, giving, and other things that you record to show how committed people are at your church. The discussion below will demonstrate how I use Big Data in a local church to look at how people are committed to our vision/purpose.

At Greenwood, we are constantly telling people that we hope they will attend worship, be a part of a small group, serve somewhere and give financially. So, I’ve devised my criteria based on looking for people who do what we ask.

Using Big Data in a Local Church to Determine the Community and the Crowd

By definition, the community circle would basically be any contact entry that we have in our database. So, in our database we have 5,612 entries as of 3/3/2018. That number represents our “community”.

big data in a local church community

Now, to determine our “crowd” I’ve decided to take out any entry that has been inactive since 1/1/2017. In other words if the person or company has not given, attended, or served in the past 13 months, then I’ve eliminated them from the “crowd”. When I ran that query in our database, I saw that we had 5595 people in this category.

Using Big Data in a Local Church to Determine the Connected

Based on the previous post that discussed the theory behind determining this category, I had to find a line in commitment that was about the average of everyone in our “crowd”. For this exercise I used the following criteria: Member/Regular Attender and either 1) gave in the past year, 2) attended a small group in the past year, or 3) registered for an event in the past year. I chose these criteria because they felt like the lowest bar for people who have joined us in our mission of attending worship (member/regular attenders) attending a small group (attending at least once in the past year) serving (we don’t have good data on this point) and giving (gave at least once in the past year).

Based upon the criteria above, the query produced 2064 results. So, using big data in a local church, we can see we have a little less than half of the entries in our database who fit this level of commitment.

big data in a local church congregation

Using Big Data in a Local Church to Determine the Committed

Using our mission statement, I upped the frequency of involvement in the categories we looked at above. So in this case, I decided to look for people who are members AND either gave in the past 4 months, or checked a child in the past 4 months, or attended a small group meeting in the past 4 months. Again, I’ve noticed a gap in our database for determining who has served. Also, we have a gap in people who have attended worship, but did not check in a child to the Sunday children’s area (In other words about ⅓ of our adults, since we are a young church).

Even with these deficiencies the number who fit this category was 384 adults and 370 children, so a total of 754, which is about the number of our average weekend attendance.

Using Big Data in a Local Church to Determine the Core

At this level I have really noticed a problem in our data. How do we really quantify who our leaders are? Often times it isn’t through something that shows up in a database. For example, a database will not show the difference between someone who does everything we ask and leads a group vs. someone who does everything we ask and doesn’t lead a group. The only real way to differentiate this top level of committed-ness is to create a subjective box to check in the database. I’ve been working hard at this and put in our Small Group Leaders and those who lead in the AVL ministry, plus staff families. My preliminary number is 42 adults. I say this is preliminary because I have not included those who are in leadership in another area. I plan to update this post quickly with those numbers.

big data in a local church committed

Conclusions about Using Big Data in a Local Church

At this point I’ve given you the raw numbers, but it is always nice to be able to visualize that data in a helpful way. So I’ve created the graph below to do just that.

big data in a local church graph

Based upon this application of using big data in a local church, you can see that the congregation group at Greenwood is about 37% of our total database entries. That means that a little more than a third of the people we come into contact with so far are buying into our vision/mission in some way. Then about 13% of the people that we come into contact with are committed to what we’ve asked. As a percentage that sounds pretty low, but that’s actually about the raw number of our average attendance on a Sunday. So, really it just shows that we have a pretty large database.

The real usefulness of these visualizations will be next year when we can see how effective we’ve been in moving people to the next step in commitment. For example, if we’ve seen a sizable increase in the amount of people who are committed next year, then we will know we’ve been doing our job well. If we do not, then we will know what to work on.

Now, let me end by saying that I do not believe numbers are the ultimate goal at church. However, I do believe these datapoints give us a tool to quantify things rather than functioning with subjective observations. What I mean is that I really don’t think it is helpful for us to say, “I really feel like our church grew spiritually this year so it was a good year.” Was there any increase in small group attendance, giving, or service? If not, then we should rethink that subjective statement.

If you enjoyed this discussion of using the church management system at your church to leverage big data, you might also enjoy my post about Planning Center Workflows.

 

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