Overview

Likelihood to Purchase is an e-commerce metric predicting the probability of prospective customers buying a product.

What is Likelihood to Purchase?

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.

Formula

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.

Example

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.

Why is Likelihood to Purchase important?

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.

Which factors impact Likelihood to Purchase?

Customer expectations
Price
Brand reputation
Product quality
Customer service
Website usability
Marketing strategies

How can Likelihood to Purchase be improved?

Personalized offers
Tailored product recommendations
Abandoned cart recovery emails
Re-engagement campaigns
Testing different marketing strategies