With fierce competition and the sale of identical products, it is extremely important for business owners to consider metrics such as LTV, the likelihood of selling products together (up-sell) and having a smart system of recommendations.
Knowing the up-sell probabilities for business is crucial because it allows you to maximize your revenue and create a personalized customer experience. By understanding which products or services are more likely to be purchased together, you can strategically market and bundle these items to increase sales.
For example, if your data shows that customers who purchase a laptop are also likely to buy a laptop bag, you can promote special offers on laptop-bag combos to entice customers into making additional purchases. This not only boosts your profits but also enhances customer satisfaction by providing them with relevant suggestions that cater to their needs.
Additionally, analyzing up-sell probabilities helps identify potential gaps in product offerings or areas for improvement. Investing time and effort into exploring this information can give your business a competitive edge and foster long-term customer loyalty.
With its powerful libraries like Pandas and NumPy, calculating up-sell probabilities and building a simple recommendation system is a piece of cake. If you have customer transaction data, you can use Python to dive into the fascinating world of market basket analysis.
In this post I will show you an example of code in Python and Pandas that allows to calculate the probabilities of upselling products and an example of a simple recommendation system on this data.