Market study notes that customer churn costs US provider a staggering $168 Billion per year and a just a 5% increase in retention can boost profitability by up to 75%. In addition, a customer’s likelihood to make another purchase increases by 2.3X after their 3rd purchase indicating the importance of frequent customer purchase.
This is where customer lifecycle management (CLM) becomes important as it provides a structural approach to manage customer relationship at every lifecycle stage. A well executed CLM strategy is critical to proactively address churn risks and driving long term growth and profitability. And data driven insights sit at the heart of CLM enabling business to understand customer behavior and craft personalized engagement strategies.
Customer lifecycle management (CLM) is a marketing strategy that involves taking a holistic look at customer’s interactions with business and is focused on building long term relationships with the customers. It involves activities like targeted campaigns and delivering personalized experiences based on the lifecycle stage of a customer with a goal to drive conversion and retention.
Traditionally, marketers design CLM strategy mostly by relying on their intuition and use very limited data. They design marketing campaigns based on basic customer segmentation e.g., prospect vs. new vs. existing customers and execute email campaigns with a static list of offering that are also picked based on business heuristics. In contrast, a data-led approach allows for a more granular segmentation and identify cadence of campaigns along with a dynamic list of offerings based on customer past behavior. Data-led CLM transforms guesswork into science empowering marketers to act with confidence and agility.
A CLM strategy consists of four components:
There are multiple ways to segment a customer base depending on business needs:
This is a most simplistic, yet powerful segmentation technique where customers are segmented based on days since last purchase (recency), # of purchased (frequency) and order value of the purchases (monitory). Each of these 3 attributes are divided in 3 (or more) groups of high, medium and low based on univariate analysis and looking at X and Y percentiles where X and Y are defined based on business requirements. Based on these groups, 27 micro segments are created which are then typically combined to form 5-10 segments based on forward looking metrics like future CLTV or retention rates.
This is a type of unsupervised learning algorithm that works by finding similarities between data and create K number of segments as defined by the user. Number of segments (K) is defined based on “elbow method” where a plot of “within-cluster sum of squares” (WCSS) against different values of K is observed. Optimum value of K is then defined by identifying the point where the curve starts to level off, resembling an elbow on the graph. Typically, these segments are created using attributes like customer spend activity, tenure, product preference, etc. Further profiling analysis is performed to understand the CLTV, retention rates and lifecycle stage for these segments.
This is a type of supervised learning algorithm that is based on a series of if-else conditions to optimize a target variable which can be attributes like spend and churn rate. The algorithm recursively partitions the input data into subsets based on value of the input features. It selects the feature that best splits the data into homogenous subsets maximizing information gain. The splitting process continues until a stopping criterion is met, such as reaching a maximum depth, achieving a minimum number of samples in a segment, or no further gain in information.
RFM based segmentation is best to use when the focus is on customer purchasing behavior and customer value. K-means segmentation is best to use when one has multiple attributes/dimensions and is looking to find natural grouping. Decision tree based segmentation is best used if there are clear rules for classification. The outcome of the segmentation exercise is a strategic grouping of customer base along with their lifecycle stage that allows the marketing team to tailor the most relevant marketing content.
Marketing content have the 2 aspects:
This is a combination of products, pricing, incentive and call to action (CTA) for the targeted customer.
This consists of textual, visual, functional and technical components. Generative AI can be very helpful to increase productivity of marketers in creating multiple designs for experimentation.
There are various marketing channels available to target customers. These include, email, push notifications, website placements, SMS, social, product listing ads (PLA), display ads, paid search, etc. Email, push notification, website placements and SMS are the key channels for CLM strategy as these allow for customer level targeting.
While other channels typically don’t allow for customer level targeting, they still play a critical role in ensuring a consistent marketing messaging and creating a unified brand experience. In addition, omni-channel communication becomes a key to drive conversions as it allows customers to interact with the brand through various touch points. Another consideration while choosing a marketing channel is its cost effectiveness, e.g. email targeting is cheaper than paid search and hence looking at customer value and channel ROI becomes important to maximize impact while balancing marketing spend.
Once the campaigns are sent out, measuring their performance is crucial to ensure that the marketing efforts are effective and are driving desired customer engagement. It also helps in making continuous improvements to the overall CLM strategy. There are 2 types for KPIs for campaign measurement:
In order to continuously optimize campaign performance, experimentation (A/B testing) plays an important role. Experimentation allows to iterate on the targeting population and marketing content to provide valuable insights into what drives success or leads to underperformance of a campaign. These insights then become a feedback loop to modify the targeting/content strategy to optimize performance.
As technology is advancing and use of AI is becoming inevitable, the possibilities of data led CLM is expanding. We will soon reach a stage where customer will expect a hyper-personalized experience. Imagine a scenario, where a customer goes to a website/app and their every interaction from product recommendation to pricing is tailored specific to their preferences and behavior.
However, as hyper-personalization will become an industry norm, data privacy and security concerns also become increasingly critical. With regulations like GDPR and CCPA, it is critical for companies to prioritize transparent data collection and usage practices. Balancing personalization with data privacy will be a key challenge and organizations that successfully navigate this landscape will build stronger customer trust and loyalty.
Data led CLM is a transformative approach that has become a basic customer expectation in today’s hyper-connected world. Organizations that embrace data driven strategies can cultivate deeper customer relationships, improve retention and unlock revenue opportunities. As companies move towards increased adoption of ML and AI, customer engagement will be shaped by intelligent automation and predictive analytics. However, success in CLM isn’t just about adopting new technology – It requires a strong commitment to ethical data practices and balancing personalization with privacy. The key to long term success lies in a customer-first approach to build a lasting brand loyalty.