Understanding Funnel Conversion Discrepancies Between A1 and A2

I have a funnel "Action A -> Purchase". My action A can be A1 or A2 (there are only those 2 options) I want to see which option has more conversion : A1 or A2.

On mobile A1 has a best conversion than A2
On desktop, A1 has a best conversion than A2
But when I remove the filtering, A2 has a best conversion than A1.

How is that possible ?

2 comments

kapa.ai
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kapa.ai
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This is an interesting scenario in Mixpanel funnel analysis! What you're describing is a statistical phenomenon known as Simpson's Paradox, where a trend appears in different groups of data but disappears or reverses when these groups are combined.In Mixpanel's funnel analysis, this can happen due to several factors: 1. Population size differences: If there are significantly more users on one platform than another, the overall conversion rate will be weighted more heavily toward the platform with more users. For example, if desktop has many more A2 conversions than mobile, even though A1 converts better on both platforms individually, A2 might show better overall conversion when platforms are combined.2. Segmentation effects: When you break down your funnel by platform (mobile vs. desktop), you're creating segments that might have different user behaviors. When combined, these differences can create seemingly contradictory results.You can investigate this further using Mixpanel's advanced funnel features: - Use the Find Interesting Segments feature to automatically identify statistically significant segments that might be driving this behavior Find Interesting Segments- Check the breakdown of your funnel by platform and action type simultaneously to see the distribution of users across these segments Funnel analysis: Find conversion problems- Examine if there are any filters that might be affecting your results differently across segments FiltersThis type of paradox is actually quite common in data analysis and highlights the importance of segmentation in understanding your conversion patterns. It's a good practice to look at both segmented and overall data to get a complete picture of user behavior.
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