# List performance with overlapping lists – accurate?

**URL:** <https://community.hubspot.com/t/list-performance-with-overlapping-lists-accurate/111088>\
**Category:** Reporting & Analytics\
**Tags:** enterprise\
**Created:** [July 10, 2024, 6:10pm UTC](https://community.hubspot.com/t/list-performance-with-overlapping-lists-accurate/111088 "2024-07-10T18:10:00Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![MMiller18](https://avatars.discourse-cdn.com/v4/letter/m/ec9cab/32.png) [@MMiller18](https://community.hubspot.com/u/MMiller18)\
**Post date:** [July 10, 2024, 6:10pm UTC](https://community.hubspot.com/t/list-performance-with-overlapping-lists-accurate/111088/1 "2024-07-10T18:10:00Z")

</div>

We would like to do RFM analysis against our HubSpot marketing emails.

My question is: how can we get accurate results using RFM analysis against HubSpot lists that are overlapping (some recipients appear in both lists).

By definition, lists based on RFM are highly likely to overlap. Eg a High Frequency list (frequent buyers) would almost certainly also contain recipients in a list based on Recency (recent buyers).

- I know that HubSpot can show performance of a given email by list. But wouldn’t the results be skewed by the fact that the lists are overlapping?

---

<div class="post-metadata">

**Author:** ![karstenkoehler](https://d10pqhm3lwygmk.cloudfront.net/user_avatar/community.hubspot.com/karstenkoehler/32/68554_2.png) [@karstenkoehler](https://community.hubspot.com/u/karstenkoehler)\
**Post date:** [July 10, 2024, 7:11pm UTC](https://community.hubspot.com/t/list-performance-with-overlapping-lists-accurate/111088/2 "2024-07-10T19:11:27Z")

</div>

Hi @MMiller18,

I’m not familiar with RFM but it seems like the methodology generally allows for overlapping segments, at least according to generative AI:

> * * *
> 
> ### Overlapping Segments
> 
> 1. **Acceptance of Overlaps:**
> 
> 2. **Prioritization:**
> 
> ### Common Practices
> 
> 1. **Composite RFM Scores:**
> 
> 2. **Separate Analysis:**
> 
> 3. **Segment Overlap Analysis:**
> 
> * * *

It seems to me like you could analyze each RFM list and draw conclusions from it – being aware that one segment does indeed overlap with others. As long as this is considered in reading the findings, you can still generate insights from that. As long as the % of overlap is part of an annotation and added context, I (as a non FRM expert) would find this acceptable.

If you want to have lists without overlap, you would have to prioritize the lists, rank them and then make sure that list 2 excludes contacts from list 1, list 3 excludes contacts from 1 and 2 etc. – however, this seems to skew things more than accepting overlaps.

Hope this helps!