Customer retention refers to the activities and actions companies and organizations take to reduce the number of customer defections. The goal of customer retention programs is to help companies retain as many customers as possible, often through customer loyalty and brand loyalty initiatives. It is important to remember that customer retention begins with the first contact a customer has with a company and continues throughout the entire lifetime of the relationship. While most companies traditionally spend more money on customer acquisition because they view it as a quick and effective way of increasing revenue, customer retention often is faster and, on average, costs up to seven times less than customer acquisition. Selling to customers with whom you already have a relationship is often a more effective way of growing revenue because companies don’t need to attract, educate, and convert new ones. Companies that shift their focus to customer retention often find it to be a more efficient process because they are marketing to customers who already have expressed an interest in the products and are engaged with the brand, making it easier to capitalize on their experiences with the company. In fact, retention is a more sustainable business model that is a key to sustainable growth. The proof is in the numbers: according to studies done by Bain & Company, increasing customer retention by 5% can lead to an increase in profits of 25% – 95%, and the likelihood of converting an existing customer into a repeat customer is 60% – 70%, while the probability of converting a new lead is 5% – 20%, at best. Set customer expectations – Set customer expectations early and a little lower than you can provide to eliminate uncertainty about the level of your service and ensure you always deliver on your promises. Become the customers’ trusted advisor – You need to be the expert in your particular field, so that you can gain customers’ trust and build customer loyalty. Use relationships to build trust – Build relationships with customers in a way that fosters trust. Do this through shared values and fostering customer relationships. Take a proactive approach to customer service – Implement anticipatory service so that you can eliminate problems before they occur. Use social media to build relationships – Use LinkedIn, Twitter, and Facebook to connect and communicate with customers and give them a space for sharing experiences with your company, so they can become brand ambassadors. Go the extra mile – Going above and beyond will build strong relationships with customers and build long-term loyalty by paying attention to their needs and issues. Make it personal – Personalized service improves customer experience and is something customers are expecting and demanding. Make their experience personal to strengthen the bond with your brand. Rather than try to manage customer retention with a mishmash of customer retention strategies, many companies use customer retention software systems and targeted customer retention plans to improve customer retention. Some companies offer customer experience management solutions that enhance customer retention rates. Measuring Customer Retention and Key Metrics Attrition rate compliments retention rate. For example, if a company has a 20% attrition rate, it has an 80% retention rate. Companies’ attrition rates can be defined by the percentage of customers the company has lost over a given period. Specifically, companies can determine retention rate by using a simple customer retention rate formula: Retention rate = ((CE-CN)/CS))100. CE = number of customers at end of period, CN = number of new customers acquired during period, and CS = number of customers at start of period. At first glance, the formula may look complicated, but it’s not too difficult once you start using it. For example, if you start the given period with 200 customers and lose 20 customers but gained 40 customers, at the end of the period you have 220 customers. 220-40 = 180. 180/200 = 0.9, and 0.9 x 100 = 90. The retention rate for the given period was 90%. It is beneficial to track retention rates so companies can put their customer retention metrics into perspective and measure results over time.
Predictive analytics involves extracting data from existing data sets with the goal of identifying trends and patterns. These trends and patterns are then used to predict future outcomes and trends. While it’s not an absolute science, predictive analytics does provide companies with the ability to reliably forecast future trends and behaviors. Gartner offers a predictive analytics definition describing the concept as any approach to data mining that contains the following key elements: Emphasizing prediction, rather than description, classification, or clustering Rapid analysis, with measurements in hours or days, rather than the traditional approach to data mining Emphasizing business relevance of the resulting insights Ease of use, making data and tools easily accessible by business users Predictive analytics emerged from a desire to turn raw data into informative insights that can be used not merely to understand past patterns and trends, but provide a model for accurately predicting future outcomes. How Predictive Analytics Differs from Other Analytics Models Gartner visualizes the various types of analytics as being on a spectrum, with each more advanced method of analysis being more difficult, but offering increased value. Descriptive analytics are at the low end of the spectrum, with a primary focus on information. Diagnostic analytics is the next level of analysis, providing insights on the motivations and causes driving trends and behaviors. Diagnostic analytics is followed by predictive analytics, or the ability to forecast what is likely to happen. At the top of the spectrum is prescriptive analytics, providing foresight and the knowledge required to create desired outcomes. Predictive Analytics Methods Predictive analytics is primarily concerned with analyzing data and manipulating variables in order to glean forecasting capabilities from existing data. Predictive analytics techniques rely on measurable variables, manipulating metrics to predict future behavior or outcomes given various measurable approaches. Predictive analytics models combine multiple predictors, or measurable variables, into a predictive model. This approach allows for the collection of data and subsequent formulation of a statistical model, to which additional data can be added as it becomes available. The addition of higher volumes of data as it becomes available creates a smart predictive model, relying on larger and larger data sets which produces more reliable predictions based on the volume of data analyzed. Additionally, relying on real-time data to fuel predictive analytics models results in greater accuracy of forecasting.