Measuring Retention as Tied to a Specific Tag (Tag History?)

Hello,

I was curious to see what you might think about this scenario. So, let’s say we have a tag to measure when someone is a customer, when someone is not a customer, and when they are a user of a particular section of our product. Let’s call these tags:

Tag: Current Customer
Tag: Former Customer
Tag: Feature User

We want to measure retention as it relates to the Feature User tag, e.g. are people who have that tag more likely to stay? Our current process is like this:

Customer joins, Current Customer tag is applied.
Customer opts into feature, Feature User tag is applied.
Customer opts out of feature, Feature User tag is removed.
Customer quits, Current Customer tag is removed, and Former Customer tag is applied.

Sometimes, people opt out of the feature without quitting as a customer. So, then, we’d like to measure what percentage of customers quit, that have the Feature User tag at the time they quit, e.g. at the time the Former Customer tag is applied. And what percentage of costumers don’t have that tag when they quit, as well.

I realize this is a little complicated, but hopefully I have explained it in a way that isn’t a total mess. I don’t think it’s possible, but I wanted to check, and to see if anyone had any suggestions of a better way to establish a data set that measures this - and then how to measure it.

I’d love to hear your suggestions! Thank you for taking the time to read this.

Patrick

Hi,

You could create a 4th and 5th tag or custom field and change the automation slightly.

When the Former Customer Tag is applied, check for the Feature User Tag and based on that, apply one of 2 tags:

Former Customer : Feature User Present
Former Customer : Feature User Not Present

Or

Set a custom field called say Cancellation Details: to ‘Feature User Present’ vs. ‘Feature User Not Present’

Hope it helps!

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Thank you for the quick reply, @Mark_Price! Much appreciated.

Just wrapping my head around this, what do you think would be the quickest/easiest way to generate those reports, showing the percentages of people who fit into each category, e.g. Former Customer Tag + Feature User Present vs. Former Customer Tag + Feature User Not Present?

Thanks so much for your advice!

Using Tags:

Create some saved searches that use the tags. Then add the ‘custom statistics’ widget to your dashboard. Within there you can set it up to show you the COUNT of the records.

It won’t show necessarily a percentage, but will show the totals and from there it would be easy to get the percentage.

Using custom fields:
You can add the custom field to the search and see at a glance or sort by the column but to do any sort of data manipulation or reporting would probably need excel.

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Thanks again, @Mark_Price! Grateful for your time. Related to this, is there any way to search based on when a tag was applied?

e.g. customers who had the Current Customer tag removed before a certain date? Like if I wanted to get a picture of 12 month retention, it would be members who joined after January 1, but left by December 31. Or something along those lines.

Happy to help with this tricky problem!

To see when a tag was applied, there’s the ‘Tag Applications’ report under Marketing in the system but it has some limitations.

You could store the sign-up date / cancel dates in custom fields and report that way.

But now there is a new issue in that you would have to set the cancel date field to ‘Todays Date’ when the cancel tag is applied and the same goes for the Current Customer tag. This requires an HTTP POST + simple API script :sleepy:

There are a few 3rd party tools that would do the date trick, but it’s a pretty basic script.

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Much appreciated, @Mark_Price! Off the top of your head, do you know any of those 3rd party tools? Thanks!

This would be exactly what opportunities are for. You can treat each step as a stage in an opportunity and then the crm->reports will tell you length of time and when each happened.

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maybe plusthis or novak solutions add-ons.

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Thanks so much, @John_Borelli and @Mark_Price. You’ve given me a bunch to think about. I am going to do so, and I might be back. :slight_smile: Much appreciated.