Hello!I'm hoping someone here on the brains trust can help me out with some ideas, video tutorial recommendations or even a dashboard template that I could use.I would like to do a deep dive into our subscriber analytics. I have a giant excel spreadsheet which has most of this information, but its difficult to pull learnings from and I'd love to have it within analytics for up-to-date information and for future years.
Some of the things I'd like to be able to do include:
Display a total number of subscribers for the 2022 season so far, and see a breakdown of how many of those subscribers are either new, had been previous subscribers (in 2018-2021) or single ticket buyers. I'd then like to be able to look at past subscribers who have not re-subscribed, and see if they're purchasing single tickets, and if not, look at their past purchase patterns.Ideally I'd also like to be able to see the number of subscribers who are 'long term subscribers' i.e. have subscribed for 3 of the past 5 years (or something similar) and see whether they've re-subscribed or not.
I'm hoping someone else has similar questions about their subscriber base and has more analytics expertise than me!
We have all our constituents marked with a constituency tag of the relevant subscription year (i.e 21S for 2021 subscriber, and 20T for 2020 ticket buyer), and I've also got static lists generated for our subscriber base for each year and a re-generating list for our current 2022 subscribers, but I can't work out a good layout or method for filtering these to get the information I want. I'd prefer not to filter by packages etc because then we lose a lot of the 2020 data, however our 2020 ticket and subscriber constituency tags remain.
Kind regards,Nicola
Hey Nicola,
Really interested in this as well. I'm like you and have subs and singles tagged (we go bank to 2012). I've also got new/renew/reengage each year which I've got in a custom category to break out sales over time graphs.
I like to look at scatter maps of subs v singles and also for new/reengage folks what their original source or source_no is to see what's driving them back.
I'm really keen on things like gaps-and-islands for those "every couple of years" people. I was also pulling telemarketing lists last week looking at the most engaged folks by tenure, number of seasons, and CLV. Admittedly that's also for prospecting up the customer engagement roadmap.
My BHAG is to find a cluster of data points that would predict stickiness. Eg: For dance classes its people that join in March are by far the most sticky. Weird but it's because when you start a new hobby after the holiday resolution period and your back at routine of work (or uni) you're in the best place in your life to develop a repeat pattern of behaviour. I'd love to find something like that for tickets.
Our might be something like looking at folks who churn out, or "once and done" folks, and ID the segments in there.
Thanks Heath! I'm also looking for sticky points, or trends across people who do/don't re-subscribe.I wish the constituencies was easier to use as a filter tool (like I'd like to be able to filter by S20 (subscriber in 2020) but does not have the S21 tag for subscribing this year) but I haven't found a good way to do that easily!I'll keep working on it and it might be something I bring to an analytics coffee if I have any bright ideas to share :)Cheers, N
So unpopular opinion but I am still keen on post show surveys being done through WordFly (and hence linking the data into Tessitura). That's possibly the last point in which we talk to ticket buyers as they churn out.
Yeah bring it to AC. It's a great place to ask these big questions even before you have anything. Use the brains trust