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Optimising marketing strategies by customer segments and lifetime values, with A/B testing
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Abstract: Every customer has different needs and purchasing behaviour. This paper shows how data science tools such as machine learning, artificial intelligence and A/B testing enable marketers to segment their target market, identify the most loyal high-value customers and their purchasing patterns, and calculate the lifetime value of these customer segments to optimise marketing strategies and campaigns. The paper also argues that A/B testing helps marketers make unbiased data-driven decisions, making it the gold standard for identifying the best marketing strategy.
Keywords: predictive analytics; customer; segmentation; lifetime value (LTV); experimentation; A/B testing
Paromita Guha is co-founder and data scientist at Axiomatic Data, a FinTech data startup. Paro has a PhD in economics with experience in econometrics, machine learning, predictive modelling and experimentation. She has collaborated with cross-functional teams, building end-to-end FinTech data products from ideation to market launch.
Christina Echagarruga is a data scientist at Facebook. She has experience in signal processing, experimentation, machine learning, data cleaning methods and analytics to create and implement solutions with business value. Chris has a PhD in biomedical engineering from Penn State University.
Eva Qi Tian is a data scientist at Vanguard Group, responsible for customer experience analytics. She has eight years’ research and working experience in experimentation, behavioural decision research, econometric modelling and machine learning. She holds an economics PhD from Michigan State University.