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.
Understanding Customer Lifecycle Management
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.
Mechanics of Data-Led CLM
A CLM strategy consists of four components:
- Customer Segmentation: Develop an understanding of the customers and define their lifecycle stage.
- Marketing Content: Develop a repository of marketing content with varying design and offerings based on customer lifecycle.
- Marketing Channel: Ensure availability of marketing channels for targeting customers which typically includes emails, push notification, website placements and other channels like paid search, social, etc.
- Performance Measurement: Reports with campaign performance providing ability to further dissect the data to understand campaign performance and make changes to further optimize strategy.
Customer Segmentation
There are multiple ways to segment a customer base depending on business needs:
Recency, Frequency and Monitory based segmentation (RFM)
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.
K-Means based segmentation
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.
Decision Tree based segmentation
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
Marketing content have the 2 aspects:
Offering
This is a combination of products, pricing, incentive and call to action (CTA) for the targeted customer.
- Products – Products can be identified by performing product affinity analysis or using more sophisticated techniques like collaborative filtering. Both the techniques identify the products that customers are likely to buy after buying a given product. Product affinity analysis is a one-dimensional approach which only looks at the products bought in the past and future whereas collaborative filtering is a multi-dimensional approach which accounts for customer attributes as well as product attributes.
- Pricing – Once the products have been identified at customer level, pricing is a static value which is generally assigned by business owners. Pricing can also be dynamic depending on the business e.g., in banking domain credit card APR are generally dynamic based on customer’s risk profile but in e-commerce domain products have fixed pricing for all customers at a given point in time.
- Incentives – Incentives are decided based on marketing budget and varies based on customer segment e.g., a new customer may have a higher conversion likelihood with a $ incentive and an existing loyal customer may have a higher conversion likelihood with a personalized offering. Experimentation plays a key role in identifying the right incentives as it helps identifying incremental conversion and ROI across varying incentives.
- Call to Action – CTA is generally to buy the product. There can be instances where the intent of the marketing campaign is to create awareness of a product functionality and in such a case CTA will direct the customer to a web/app page that has details of the functionality.
Design
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.
- Textual components include subject line, pre headers, branding text and supporting text/disclaimer. AI tools/models like GPT-4, Claude 2 and copy.ai can be used to optimize these components.
- Visual components include hero image/video, brand logo, general color scheme, font choices, product images and white/negative spaces. AI tools/model like DALL-E 3, Khroma AI and Adobe color AI can be used to optimize these components.
- Functional components include buttons, links, forms, social media icons. AI tools like Uizard, Builder.io can be used to optimize these components.
- Technical components include size/dimensions, image format, aspect ratio or any other platform specific requirements. AI tools like Adobe Sensei and Adobe Express can be used to optimize these components.
Marketing Channel
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.
Performance Measurement
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:
- Engagement KPIs – These include sent volume, impressions, open rates, click rates and un-subscribe rate depending on the target channel. These KPIs help in understanding a customer’s engagement with the marketing content e.g. lower email open rates may indicate that the subject line is not appealing enough and lower click rates may indicate that the offering is not appealing enough.
- Financial KPIs – These include conversion rate, sales, units and profitability depending on the business. These KPIs help in understanding the financial impact of the campaign and to calculate the ROI of the campaign.
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.
Future of CLM
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.
Conclusion
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.
