Likelihood to Purchase is an e-commerce metric predicting the probability of prospective customers buying a product.
The term 'Likelihood to Purchase' revolves around predictive analytics in e-commerce. It refers to the estimated probability that a potential consumer is likely to buy a specific product, given their browsing or purchasing history, marketing interactions, and other predefined conditions. This metric is fundamentally concerned with predicting consumer behavior.
There isn't a universal formula for calculating the 'Likelihood to Purchase'. It is largely influenced by the business’s unique algorithms and analytics which are built through behavioral data, demographic details, buying patterns, purchase history, engagement on digital platforms, and consumer responses to marketing activities.
Let's say an online fashion retail store observes that a returning customer frequently searches for designer t-shirts and often purchases during sales. Using these insights, the platform can gauge his likelihood to purchase and propose a personalized marketing strategy. They can send him an email promotion during an upcoming sale, highlighting the latest designer t-shirts collection.
Understanding a customer's purchasing probability is vital to timely and relevant marketing. It assists in narrowing down the potential consumers, helps identify high-value customers, reduces wastage of marketing resources, and boosts conversion rates. Moreover, it helps businesses identify and nurture leads that are more likely to convert.
Likelihood to Purchase is directly linked with metrics like Customer Lifetime Value (CLTV), Customer Acquisition Cost (CAC), and Churn Rate. A high Likelihood to Purchase tends to increase CLTV, decrease CAC, and reduce the Churn Rate.