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Customer Loyalty. New vs Existing Bookers in Analytics

Dara Hogan
$organization

Hi,

I'm trying to measure loyalty using New vs Existing bookers in Analytics.

Has anyone got any idea's on how to do this?

I'm trying to create a formula using the Constituent > First Performance Date and the current date, but it's not working for me.

I'm trying to do it in my Year on Year dashboard so that's making it a bit more complicated.

What I'd like is something like the following (in a stacked column chart):

Year        New Bookers        Existing Booker s

2019               55%                        45%

2018               57%                        43%

2017             58&                            42%

If anyone has any suggestions or other ideas on measuring loyalty it would be much appreciated.

I'm also thinking about a measure for 'Years since first performance', so measure more in depth, but one step at a time!

thanks,

Dara

Parents
  • $organization

    Hi Dara,

    I run lists (and constituencies) for New, Renew, Reengage every year.  I then use a Custom Category to allow splitting between them.  My notes are here

    For loyalty I've been playing around in SQL with RFM analysis and Gaps and Islands, which is proving to be fun, both with Philanthropy and with Ticketing.  I gained that code from other lovely forum regulars and a little from the interwebs.  RFM is handy for measureing the quality of your relationship with a customer now.  Gaps and Islands is good to see patterns in historical data, eg: do people take gap-years from you, what's the most common length of tenure, did a particular bad year end with people switch off for a year and then come back or did you loose them altogether (perhaps because of a new business/artistic direction)

    hope that's of some use

Reply
  • $organization

    Hi Dara,

    I run lists (and constituencies) for New, Renew, Reengage every year.  I then use a Custom Category to allow splitting between them.  My notes are here

    For loyalty I've been playing around in SQL with RFM analysis and Gaps and Islands, which is proving to be fun, both with Philanthropy and with Ticketing.  I gained that code from other lovely forum regulars and a little from the interwebs.  RFM is handy for measureing the quality of your relationship with a customer now.  Gaps and Islands is good to see patterns in historical data, eg: do people take gap-years from you, what's the most common length of tenure, did a particular bad year end with people switch off for a year and then come back or did you loose them altogether (perhaps because of a new business/artistic direction)

    hope that's of some use

Children
  • $organization in reply to Heath Wilder

    Thanks Heath,

    I like it, very interesting.  I'm going to have to set some time aside to look at using lists and the custom categories.

    For a quick win I've used the 'Year of First Performance' field (See below). This works OK for a single year, but not as good for comparing behaviour year on year.

    I'm also thinking of using bucketing to put this into 'First Time Booker' and 'Existing Booker'. Stay tuned for how I get on with that.

    Thanks again, Dara