This approach is focused on consumption patterns and associated business outcomes.
It mainly looks to past events, focusing on causal relationships and sequences.Īlso known as consumption analytics, outcome analytics gives insight into customer behavior that drives specific outcomes. Examples of diagnostic analytics include churn reason analysis and customer health score analysis. This technique is often used when trying to identify why something happened, such as looking into churn indicators and usage trends amongst customers. Examples of descriptive analytics include summary statistics, clustering and association rules used in market basket analysis. This technique provides insight into what has happened historically and will provide you with patterns and trends to be able to investigate the detail. Not always the best value results, and fairly time-consuming, it can still be useful for uncovering patterns within a certain segment of customers. This could be next best offers, churn risk and renewal risk analysis. Predictive analytics uses models to forecast what might happen in a future, specific situation. For customer retention, examples of prescriptive analytics include the next best action and next best offer analysis.
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Keen to know more? Here is a quick rundown of 5 common types of retention analytics.įacilitates focusing on answering a specific question, and can help to determine the best future solution among a variety of options, and suggest options for how to take advantage of a future opportunity or illustrate the implications of each decision to improve decision-making. One way many companies are finding a competitive advantage is through customer retention analytics.
It’s clear that retaining existing customers makes the most business sense, but doing so isn’t quite that simple. Says Bain & Company, increasing customer retention rates by 5%, can increase profits by anywhere from 25% to 95%. Keeping existing customers allows for more sustainable growth. Consider that usually, there are no huge customer acquisition costs associated with selling a new product or service to your existing customer base. It goes without saying, but your existing customers are much easier to market and sell to. Once you know why your happy customers stay and why some leave, you can take the right measures to keep the right customers. Stay best friends with your loyal customers, as they are extremely valuable. In fact, it can be five times more expensive to attract a new customer, than to keep an existing one. It’s much cheaper to keep an existing customer than it is to earn a new one. While the intention to use AI and analytics is there, according to Forrester, “only 15% of senior leaders actually use customer data consistently to inform business decisions” (“The B2B Marketers Guide to Benchmarking Customer Maturity”, Forrester, 2017).ģ benefits to improve customer retention with analytics 1. Customer professionals said their biggest barrier was the inability of translating customer insights into business operations. Seemingly, customer professionals lack proficiency in, or access to, three important data science skills: programming, mathematics, statistics. Machine learning is one of the least adopted practices in customer programs (38% of companies). Thus, there’s lots of opportunity and room for improvement.
Interestingly, according to a study by Broadway Business, only 32% of respondents are satisfied with their company’s use of analytics to create a competitive advantage.
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A McKinsey report states that “executive teams that make extensive use of customer data analytics across all business decisions see a 126% profit improvement over companies that don’t” (McKinsey, 2014).Īs much as companies talk a good game about big data, they do not seem to leverage it, or customer retention analytics, to its full extent. Why is customer retention important?Ī data-driven customer retention strategy can reap rewards in a big way, if you do it right. To enable these actions, customer retention analytics provide predictive metrics of which customers might churn - which enables them to get ahead of it. What is customer retention?Ĭustomer retention refers to the actions and strategies a business uses to try and keep existing customers. So, let’s look at ways to reduce customer churn with customer retention analytics and why it’s important in the first place. Today, it’s more about offering customers something as personalized as possible, so that they feel truly special. That was the old school way of doing things. Think customer loyalty programs are all about getting generic discounts, points and rewards? Think again. Customer retention analytics: 5 strategies to reduce churn Churn & Loyalty Feedback Analysis Resources
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