Using only machines and software to understand customer behaviour and how people buy goods and services is irrational.

First, let us address the main question: Can machine learning help understand customer purchasing behaviour?

Yes, machine learning and software can be highly effective in predicting customer behaviour and purchase patterns. Machine learning is a type of artificial intelligence that uses algorithms to identify patterns in data, and these patterns can be used to make predictions about future behaviour. When applied to customer behaviour and purchase patterns, machine learning can analyse vast amounts of data to identify trends and patterns that would be difficult or impossible to identify through manual analysis.

Some of the ways that machine learning and software can be used to predict customer behaviour and purchase patterns include:

1. Personalised recommendations: Machine learning algorithms can analyse a customer’s past behaviour to make personalised recommendations for future purchases.
2. Predictive analytics: Machine learning can be used to identify patterns in customer behaviour that can be used to make predictions about future purchases.
3. Customer segmentation: Machine learning can be used to segment customers based on their behaviour and preferences, allowing companies to tailor their marketing and sales strategies to different customer groups.
4. Fraud detection: Machine learning can be used to identify unusual patterns in customer behaviour that may indicate fraudulent activity.

However, while machines and software can provide valuable insights into customer behaviour and predict buying patterns, relying solely on technology to understand customers and make business decisions can be irrational for a few reasons:

1. Limited understanding of human behaviour: Although machines and software can learn over time, they are designed to process and analyse data, and they cannot fully understand the complexities of human behaviour. This means that relying solely on technology to understand customer behaviour and buying patterns can lead to incomplete or inaccurate conclusions.
2. Lack of context: Understanding customer behaviour and buying patterns often require a deep understanding of the social and cultural context in which they occur. Machines and software may not have the contextual knowledge necessary to make accurate predictions about customer behaviour.
3. Inability to adapt to changing customer needs: Customer behaviour and buying patterns can change quickly in response to new trends or events. Machines and software may not be able to adapt to these changes as quickly as humans, which can lead to inaccurate predictions and missed opportunities.
4. Lack of empathy: Understanding customer behaviour often requires empathy and an ability to put oneself in the shoes of the customer. Machines and software do not have emotions or the ability to empathise, which can make it difficult for them to understand the motivations and needs of customers.

Overall, machine learning and software can be highly effective in predicting customer behaviour and purchase patterns. However, it’s important to note that these predictions are not always perfect, and it’s important to balance data-driven insights with human intuition and expertise. Additionally, as with any tool, the effectiveness of machine learning and software depends on the quality and accuracy of the data being used.