Likelihood to Purchase

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?

1. Customer expectations
2. Price
3. Brand reputation
4. Product quality
5. Customer service
6. Website usability
7. 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

What is Likelihood to Purchase's relationship with other metrics?

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.