Report feature: Registration-Level retention

What you’ll learn in this article:

• How a Registration-Level retention report is different from a ‘Contact-Level Retention’ report
• How to pull retention information at the Registration-Level
• Different ways to customize a Registration-Level retention report, such as discovering participants who participated in a virtual Sub Event previously and have not been retained
• Questions to consider at the Registration-Level
• How to analyze new participants to your event with registration metrics, especially those who are supporting your brand in 2020

How is a Registration-Level report different from a Contact-Level report?

Retention is an important metric for event organizers to monitor. We have outlined some ways to analyze retention of your contacts with a Contact-Level Retention report template, and now we are going to dive deeper by looking at a Registration-Level report feature. As a reminder, When viewing a Contact-Level Retention report each line or record in the report is a unique contact, whereas in a Registration-Level retention report each line or record in the report is a registration. Keep in mind that when analyzing at a Registration-Level a contact can have more than one registration.

Tip: Start by analyzing your retention data at a Contact-Level.

What is the purpose of this report feature? Learn how to analyze and customize the retention of your participants at a Registration-Level using different metrics. This Registration-Level report type will provide a deeper look into your participants who are new or have not yet been retained. This report will allow you to analyze registration data such as Event, Sub Event, Date Registered, etc. Become confident in pulling information that matters to your organization and/or event.

This report type is at the Registration-Level, focusing on the Registration object, so there will be more use for field filters. The Registration-Level field filters will be related to what data you want to view in your report. You will, however, still require the use of cross-filters to identify the unretained year or years and sub-filters that you’re interested in. For example, for a 2019-2020 Event-specific not retained report you would be using field filters to view only 2019 registrations of that Event, then you would add a cross-filter of "Contacts without Registrations" for the 2020 Event. The end result will pull 2019 registrations of the Event of participants who haven't yet signed up for the 2020 specific Event. We will cover how to build and customize this kind of report below.

How to build a Registration-Level retention report:
A Registration-Level retention report is not available as a report template, but instead you can build and customize your own report depending on the results you’d like to analyze. First, select the report type of “CRM - Registrations with/without Results”. Filters you’ll want to add for a Registration-Level retention report include:

• A field filter for “Contact: Testing” equals False, to ensure no demo or test data is used.
• A field filter for “Registration: Is Active” equals True, pulling active registrations and no inactive (transferred, deferred, etc.) registrations.
• A cross-filter for "Contacts without Registrations" with the sub-filter "Event Year" for the current year, such as 2020.
• Add any other Registration-Level field filters and or sub-filters under your cross-filter, we’ll review customization ideas below.

It’s important that you’re comfortable with reporting and report filters before diving in. Our CRM reporting guides are a great resource to work with when customizing your own reports. Start with the basic reporting guide and level up to the intermediate reporting guide that covers terminology that you’ll see below.

An overview of a Registration-Level retention report:

What are some ways to customize a Registration-Level retention report?

Analyze by Event:

• Add a field filter “Registration: Event Name” contains “event 1” such as “forest city” which will narrow results. As well as a sub-filter for Event contains “event 1” such as “forest city” to the cross-filter to further narrow results to analyze retention by the specified Event.

Note: Use contains as the expression for the Event Name filters to ensure all applicable records are pulled. If however, you’re referencing an exact Event Name such as “Forest City 2018” utilizing the expression equals will pull the desired results.

Tip: To analyze by event, you can also utilize “Event: Account Name” and “Event: EID” fields.

This data view will show all participants who had an active registration for the Forest City event in 2019 who don’t yet have a registration in 2020 for the Forest City event.

Analyze by Sub Event:

• A powerful way to analyze Registration-Level retention data is to dive into Sub Event metrics. Add field filters for “Registration: Event Name”,  “Registration: Is Active”, “Registration: Event Year” and “Registration: Sub Event” and specify the values you’re interested in. Add a cross-filter for "Contacts without Registrations" with sub-filters for any of the details you’re interested in such as “Event”, “Is Active” and “Event Year”.

This data view will show all participants who had an active registration for any 2020 virtual Forest City Sub Events who have not yet been retained for the Forest City 2021 event.

Analyze by Sub Event combinations:

• Analyze high value Sub Events to see who has not yet been retained for the upcoming year. Add a field filter for “Registration: Sub Event” and a sub-filter under the cross-filter to outline the Sub Event’s you’re interested in. Use contains as the expression for the Sub Event filters to ensure all applicable records are pulled.

This data view will show all participants who had an active registration for any 2020 Sub Event that contains half marathon or marathon who have not yet been retained for any 2021 Sub Event containing half marathon or marathon.

Analyze participants who’ve not been retained since a specific year:

• Look at participants who haven’t been engaged with a certain Event since a specific year. Add a field filter for the last Event a participant participated in. We used the expression Event equals including the year in the field “Registration: Event Name” as we are pulling the exact Event Name, such as “Forest City 2017”. A field filter for “Registration: Is Active” is used to ensure that no inactive registrations are analyzed in this report. Lastly, add a cross-filter for "Contacts without Registrations" including sub-filters for the Event and Event Year you’re analyzing. 

Note: The expression ‘greater than’ is used in the Event Year sub-filter so we can analyze those who haven’t ran for the past 3 years (2018, 2019 and 2020) rather than maxing out the number of cross-filters and using one for each year (max 3 cross-filters).

This data view will show all participants who had an active registration for the Forest City 2017 event who have not participated in the Forest City event since. 

Analyze by Date Registered:

• Customize the Date fields by using the “Registration: Date Registered” field to further analyze behavioural habits or timeframes of participants. Select a custom range and specify the timeframe you’re interested in. Add a field filter for Event to pull data for a specific event; we used the expression Event equals including the year in the field “Registration: Event Name” as we pulled the exact Event Name, such as “Forest City 2020”. Then add a cross-filter for "Contacts without Registrations" including sub-filters for the specific Event you’re analyzing. 

This data view will show all participants who had an active registration for the Forest City 2020 Event who registered for the event between 01/10/2019 and 31/01/2020 who don’t yet have a registration for the Forest City 2021 Event.

Analyze based on loyalty metrics:

• Segment even further to identify loyal customers from those who have not been retained. Learn more about loyalty metrics and what each field means here. Depending on what you want to analyze, filter unretained loyal customers by utilizing the following as a field filter:
• Total Number of Races
• Total Number of Registrations
• Number of Years Participated

Note: Loyalty metrics are all-time for the organization; loyalty metrics do NOT exist at the event level.

This data view will show active 2020 registrations for Forest City of contacts who have 4 or more active registrations for the event organization as a whole* who have not yet been retained to the Forest City event in 2021.

*Note: the metric “Total Number of Registrations” is calculated at the organizational level, so in this example, the 4 or greater registrations could be from multiple registrations from 1 event, or multiple registrations in 1 year.

Some questions to ask as you’re analyzing retention data at a Registration-Level:

• Who has not been retained for these specific Sub Event(s)?
• Who has not been retained for this specific Sub Event in the last 3 years? 
• Who participated in a virtual Sub Event last year and has not yet registered for the current year?
• Who has not been retained in an Event since participating in a specific year?
• Who has not been retained that registered in this timeframe in the previous year?
• Who has not been retained for my event who registered previously in a specific timeframe?
• Who has not been retained that answered a specific custom registration question?

Consider contact-specific questions such as gender, geographical location, reference the Contact-Level retention report.

Analyze New Participants at the Registration-Level

Alternatively, this report can be used to analyze participants who are new to your organization and or event. Depending on your goals, it may be beneficial to know who, and how many participants are new to a specific event or Sub Event. All of the examples that were outlined above to see who hasn’t been retained can be used to discover new participants by using an alternate view of the report. Let’s review how-to below:

Analyze New Participants

• To analyze new participants update the "Contacts without Registrations" sub-filter for “Event Year” to be less than 2020, or the current year you’re analyzing. As well, you will need to make the “Registration: Event Year” field filter equal to the current year you’re analyzing.

This data view shows all participants who are new to your organization as of the current year, 2020. The participants have an active 2020 registration and no registrations for any previous year.

Analyze New Participants to your Event

• Add a field-filter for “Registration: Event Name” to pull data for an Event such as marathon weekend. Add a sub-filter to the "Contacts without Registrations" cross-filter for Event ‘contains’ marathon weekend. 

Note: The expression ‘contains’ to ensure all data is pulled.

This data view will show all participants who are new to the specified event, Marathon Weekend, for the year 2020.

The information which can be pulled at a Registration-Level to understand different retention metrics are very powerful to your organization and event marketing.

If you have any questions or would like Race Roster to review your Registration-Level retention report don’t hesitate to contact your Customer Success Manager.