Consumer Loyalty – building attribution models and digital consumer paths

In today’s digital age, understanding the complexities of consumer purchasing journeys is crucial for businesses trying to succeed in competitive markets. Especially to gain and improve consumer loyalty. Data, and association rule learnings, plays an important role in this effort and offers invaluable insights into consumer behaviour, preferences and trends across various digital platforms. By leveraging data analytics to understand consumer behaviour, enhance user experiences, and drive strategic decision-making, businesses can cultivate deeper connections with their audience, foster brand loyalty, forecast future demand, anticipate market shifts… and ultimately drive sustainable growth. 

In a Direct-to-Consumer (DTC) platform operating in a digital interaction hub, a business was accumulating significant amounts of data on consumer purchasing journeys. Initially, this data was used only to validate existing strategies and decisions through data and analytics. The business struggled to dig into this data and uncover hidden insights to improve consumer loyalty. This resulted in a need of improvement on consumers’ purchasing journeys on digital platforms.

We are using Python libraries – including libraries with AI components – to conduct Apriori algorithm experiments to mine and uncover association rule learnings*. We are visualizing these, building attribution models and intelligent journey paths.​ This involves generating in-depth visualizations and statistical solutions to gain insights into complex data relationships. 

* Association rule learning is a machine learning method for discovering interesting relations between variables in large databases