According to PWC, 32% of all customers would quit doing business with a brand they liked after a single unsatisfactory experience. And 73% of U.S. consumers declare that customer experience is a significant factor in their purchasing decision. It is so powerful that even if they love a company or product, 59% will still walk away after several bad experiences, and 17% will walk away after just one bad experience. A Mckinsey study shows that happy customers are more likely to upgrade or add services and are less prone to canceling. Confronted with poor customer service, 20% of consumers would complain publicly via social media says NewVoiceMedia.
In a nutshell, it is of prime importance to keep customers happy and anticipate the red zone into which a customer might walk out the door and display his/her dissatisfaction publicly on social media.
We want to see whether Quaartz can help identify which customers are likely to leave soon. We use a dataset containing customer-related data, which includes the following information:
We want to estimate who is likely to leave the company. And we would like to understand the characteristics of those customers likely to leave,i.e., how to recognize them before leaving. It would give us the tools to retain them proactively.
This issue raises the following marketing question:
Can we identify with a good reliability a customer likely to leave?
We have run Quaartz on the above-described dataset asking the tool to identify customers likely to churn. Quaartz predictive models’ results reach a 73% accuracy and an 81% AUC based on this real dataset. Avoidable consumer switching costs U.S. companies $136.8 billion per year, according to CallMiner.
Quaartz has selected the following four main criteria to make its decisions out of the twenty available in the dataset:
Consider two companies: company A and company B, with both an annual revenue of 20 million. Assume they are both growing at the same rate: 50%. Company A has a yearly churn of 5%, and company B has an annual churn of 15%. After five years, company A’s revenue is 90 million, and company B’s income is 60 million. This example shows that avoiding churn has a significant impact on revenue.
Quaartz helps customer satisfaction teams improve their performance and retain more customers.
In two days, the customer satisfaction team gained a competitive edge against their competitors through improved churn prevention: