Causal Funnel

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Causal Funnel refers to adding causal analysis to any sales and marketing customer funnels, like Purchase funnel, Conversion funnel, AIDA (marketing) funnel.

Causal funnel focuses on adding signals to each step in the funnel, providing reasons for the what made customer to proceed to next step (conversion) and what made them not to proceed to the next step (drop-off).

This analysis could be done for the signals in various stages of the funnel

  • Traffic sources
  • Top of the funnel (TOFU), awareness stage
  • Middle of the funnel (MOFU), consideration stage
  • Bottom of the funnel (BOFU), conversion stage
  • Re-engagement or repeat customers

Causal funnel vs typical funnel analysis

Causal Funnel analysis differs from typical Funnel analysis. Typical funnel analysis uses a well-structured manner of data collection and then using statistic modeling on this data. [1] It simply provides the conversion and drop-off numbers and high level information in some cases about the customer's attributes.

The causal funnel on the other hand uses data science with artificial intelligence and machine learning to identify the signals in a particular step of the conversion funnel that makes a consumer proceed to the next step in the funnel.[2] Causal Funnel is based on a large amount of data collection(ref) without any strict structure and it continuously optimizes itself as and when the consumer behavior for conversion evolves with new signals.[3] The (Causal) Impact of Coupons on the Visit-to-Purchase Funnel is well established[4] and there are several other ways in which the visitor could be nudged along the funnel.

Purpose of causal funnel

Causal funnel is useful for any company that offers their product and services online with a clear conversion path. Causal funnel helps online businesses understand and improve their conversion and engagement. It identifies what works and what does not work to help visitors progress from one step to another in the funnel.

It lets the businesses know:

  • Gaps in the services offered
  • Information gaps
  • Technical issues
  • Product offering issues

It provides them with:

  • suggestions for changes
  • Fixing technical issues
  • Expanding product or service offerings
  • Focus on signals that causes drop-off

See also

  • Purchase funnel
  • Conversion funnel
  • AIDA (marketing)
  • Causal analysis


  1. "Funnel analysis - Analytics Help". Retrieved 2021-01-01.
  2. "Causal Funnel - Bringing causality to sales & marketing funnels with AI and Machine Learning". Retrieved 2021-01-02.
  3. "Causal Data Collection and Summary (Customer Analytics) - Module 2: Defining the Problem". Coursera. Retrieved 2021-01-01.
  4. Gopalakrishnan, Arun; Park, Young-Hoon (2020-10-05). "The Impact of Coupons on the Visit-to-Purchase Funnel". Marketing Science. doi:10.1287/mksc.2020.1232. ISSN 0732-2399.

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