Loyalty-measures
Different approaches to measuring an ecommerce customer's loyalty.
Algorithms
- RFM - The different variations can be in the way recency, frequency and monetary value are calculated. Also can take lifetime value into consideration.
- RFM measure with returns taken into account
- Causal directed graphs
Causal Directed Graphs
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DoWhy : A python library for causal inference , supports explicit modeling and causal assumptions can be tested as well. official doc
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Estimating the effect of a member rewards program - can be done after we take advantage of loyalty or before?
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How long should an articles' headline be? length of wordcount on news articles headline
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Hotel cancellations - can this be used for order cancellations or returns? - Beyond Predictive Models: The Causal Story Behind Hotel Booking Cancellations
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Causalnex : Python library that helps infer causation instead of observing correlation.
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Causual discovery toolbox : Causal inference in graphs and in the pairwise settings. Also can do graph structure recovery and dependencies.
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Causal graphical models : Python module built on top of networkx for causal graphs and structural graphs
RFM
1. Link to the lifetimes library - [todo]
Extensions
Within the domain of ecommerce, what else can be done after loyalty has been measured?
- Introduction of loyalty rewards programs like amazon prime
- Victory laps of customers
Further Extensions
Can this be extended to measure loyalty in other cases?
- Relationships : Employer-employee, personal relationships, romantic relationships (tinder/matrimonial websites might be an appropriate use case for this)