Leveraging Customer Lifecycle Analytics: A Launchpad for Aspiring Analysts
Posted 18 Jun 2024
Understanding the intricacies of customer lifecycle analytics not only empowers businesses but also provides a fertile ground for young professionals, students, and career shifters to build a robust portfolio in the field of analytics. By mastering various analytical methods and associated statistical or machine learning techniques, aspiring analysts can showcase their skills and make significant contributions to any organization. Below, we explore how each analytical method within the customer lifecycle can be leveraged as a learning and portfolio-building opportunity.
What is Customer Lifecycle Analytics?
Customer lifecycle analytics entails analyzing and interpreting data at different levels of a customer’s interaction with a business. It accurately maps the journey from awareness to long-term loyalty, a process that is not only documented but influenced by insightful evidence-based strategies. Fundamental to this analytics is the understanding of the distinct but interconnected stages: acquisition, onboarding, retention, cross-selling, winback, and loyalty, each representing a strategic opportunity for the firm to forge deeper connections with its clientele.
Simultaneously, from the viewpoint of the customers themselves, their journey unfolds through the stages of Discover, Explore, Buy, Use, Ask, and Engage. Each of these customer-centric phases mirrors a specific business milestone: Discovery aligns with acquisition as customers become aware of the brand; the Explore phase dovetails with onboarding, where experimentation leads to familiarity; Buying signals conversion, turning interest into action; Use equates to retention through repeated interaction; Asking is an opportunity for feedback and cross-selling; and Engaging cements loyalty, inviting advocacy and lasting bonds.
We construct a holistic vision of the customer journey by syncing the internal focus on growth and profitability with the external customer experience. Bridging analytical insights between organizational objectives and customer experiences enables the crafting of strategies that resonate with customers, anticipate their needs, and build advocacy, thus, seamlessly integrating business and consumer narratives into one shared story of mutual growth and satisfaction.
Analytics Across the Customer Lifecycle
Understanding the intricacies of customer lifecycle analytics not only empowers businesses but also provides a fertile ground for young professionals, students, and career shifters to build a robust portfolio in the field of analytics. By mastering various analytical methods and associated statistical or machine learning techniques, aspiring analysts can showcase their skills and make significant contributions to any organization. Below, we explore how each analytical method within the customer lifecycle can be leveraged as a learning and portfolio-building opportunity.
Discover: Profiling and Targeting Prospects
1. Segmentation
- • Technique: Clustering (e.g., K-means, Hierarchical Clustering)
- • Application: Segmenting customers based on demographic, behavioral, or psychographic data helps in understanding distinct customer groups. Analysts can use clustering algorithms to create meaningful customer segments, showcasing their ability to handle and interpret large datasets.
2. Lead Scoring
- • Technique: Classification (e.g., Logistic Regression, Decision Trees, Random Forest)
- • Application: Predicting the likelihood of prospects converting into customers involves assigning scores based on various attributes. Using classification techniques to develop lead scoring models demonstrates proficiency in predictive analytics.
3. Customer Look-Alike Targeting
- • Technique: Similarity Measures (e.g., Cosine Similarity, Euclidean Distance)
- • Application: Identifying new prospects who resemble existing high-value customers can be done using similarity measures. Look-alike modeling involves analyzing data from first-party sources to identify patterns among high-value customers and find similar prospects. This approach improves audience precision and increases conversion rates by targeting those more likely to engage with the brand.
Explore: Analyzing Marketing Responses
1. Offer/Contact Optimization
- • Technique: A/B Testing and Multivariate Testing
- • Application: Testing different marketing strategies and their impacts involves designing and analyzing experiments. Proficiency in experimental design and hypothesis testing is critical here.
2. Marketing Mix Modeling
- • Technique: Regression Analysis (e.g., Multiple Linear Regression)
- • Application: Understanding the effectiveness of various marketing channels can be achieved through regression analysis, providing insights into resource allocation and ROI.
3. A/B and Multivariate Testing
- • Technique: Experimental Design and ANOVA
- • Application: Conducting A/B and multivariate tests to optimize marketing inputs helps in learning about the impacts of different variables. Analyzing these tests using ANOVA or other statistical methods can highlight analytical rigor.
Buy: Driving Conversion and Increasing Share-of-Wallet
1. Propensity Models
- • Technique: Logistic Regression, Gradient Boosting Machines
- • Application: Predicting the likelihood of a customer taking a specific action (e.g., purchasing) involves propensity modeling. Using these techniques showcases the ability to work with classification models and interpret their results.
2. Cross-Sell/Upsell
- • Technique: Association Rule Learning (e.g., Apriori Algorithm)
- • Application: Identifying products that customers are likely to buy together helps in developing cross-sell and upsell strategies. Association rule learning techniques can be used to uncover these patterns.
3. In-Market Timing Models
- • Technique: Time Series Analysis (e.g., ARIMA, Exponential Smoothing)
- • Application: Determining the best time to target customers can be done using time series analysis, which is crucial for timing marketing efforts accurately.
Use: Driving Product Utilization
1. Product and Recommendation Analysis
- • Technique: Collaborative Filtering, Matrix Factorization
- • Application: Recommending products based on user behavior involves techniques like collaborative filtering. This demonstrates the ability to handle recommendation systems and personalize user experiences.
2. Sentiment Analysis
- • Technique: Natural Language Processing (NLP) (e.g., Text Classification, Sentiment Analysis Models)
- • Application: Analyzing customer feedback to gauge sentiment involves NLP techniques. Building sentiment analysis models showcases skills in text analytics and machine learning.
Ask: Learning About Engagement
1. Engagement Analysis
- • Technique: Survival Analysis, Cohort Analysis
- • Application: Understanding customer engagement over time can be done using survival and cohort analyses. These techniques highlight the ability to analyze customer behavior longitudinally.
2. Voice of the Customer Analysis
- • Technique: Text Mining, Topic Modeling (e.g., Latent Dirichlet Allocation)
- • Application: Extracting insights from customer feedback involves text mining and topic modeling. These techniques demonstrate proficiency in handling unstructured data.
Engage: Fostering Long-Term Relationships
1. Churn Models
- • Technique: Classification, Survival Analysis
- • Application: Predicting customer churn and understanding its drivers can be achieved using survival analysis and classification techniques. Building churn models showcases predictive analytics skills.
2. Next-Best-Action Models
- • Technique: Markov Chains, Reinforcement Learning
- • Application: Determining the next best action for engaging customers involves sequential decision-making techniques. This highlights advanced analytical capabilities.
3. Lifetime Value Models
- • Technique: Survival Analysis, Regression Analysis, Monte Carlo Simulations
- • Application: Estimating the lifetime value of customers involves understanding the duration and value of customer relationships. Survival analysis is particularly useful for modeling the time until churn or other critical events, while regression models and simulations can help forecast long-term customer value. Using these techniques demonstrates the ability to handle both temporal and financial aspects of customer analytics.
4. Customer Location Analysis
- • Technique: Geospatial Analysis (e.g., GIS Mapping)
- • Application: Understanding the geographical context of customer behavior involves geospatial analysis techniques, showcasing the ability to work with location-based data.
5. Loyalty Models
- • Technique: Logistic Regression, Clustering
- • Application: Building models to understand and enhance customer loyalty involves classification and clustering techniques, demonstrating the ability to analyze customer retention.
Conclusion
1. Offer/Contact Optimization
- • Technique: A/B Testing and Multivariate Testing
- • Application: Testing different marketing strategies and their impacts involves designing and analyzing experiments. Proficiency in experimental design and hypothesis testing is critical here.
2. Marketing Mix Modeling
- • Technique: Regression Analysis (e.g., Multiple Linear Regression)
- • Application: Understanding the effectiveness of various marketing channels can be achieved through regression analysis, providing insights into resource allocation and ROI.
3. A/B and Multivariate Testing
- • Technique: Experimental Design and ANOVA
- • Application: Conducting A/B and multivariate tests to optimize marketing inputs helps in learning about the impacts of different variables. Analyzing these tests using ANOVA or other statistical methods can highlight analytical rigor.
Buy: Driving Conversion and Increasing Share-of-Wallet
1. Propensity Models
- • Technique: Logistic Regression, Gradient Boosting Machines
- • Application: Predicting the likelihood of a customer taking a specific action (e.g., purchasing) involves propensity modeling. Using these techniques showcases the ability to work with classification models and interpret their results.
2. Cross-Sell/Upsell
- • Technique: Association Rule Learning (e.g., Apriori Algorithm)
- • Application: Identifying products that customers are likely to buy together helps in developing cross-sell and upsell strategies. Association rule learning techniques can be used to uncover these patterns.
3. In-Market Timing Models
- • Technique: Time Series Analysis (e.g., ARIMA, Exponential Smoothing)
- • Application: Determining the best time to target customers can be done using time series analysis, which is crucial for timing marketing efforts accurately.
Use: Driving Product Utilization
1. Product and Recommendation Analysis
- • Technique: Collaborative Filtering, Matrix Factorization
- • Application: Recommending products based on user behavior involves techniques like collaborative filtering. This demonstrates the ability to handle recommendation systems and personalize user experiences.
2. Sentiment Analysis
- • Technique: Natural Language Processing (NLP) (e.g., Text Classification, Sentiment Analysis Models)
- • Application: Analyzing customer feedback to gauge sentiment involves NLP techniques. Building sentiment analysis models showcases skills in text analytics and machine learning.
Ask: Learning About Engagement
1. Engagement Analysis
- • Technique: Survival Analysis, Cohort Analysis
- • Application: Understanding customer engagement over time can be done using survival and cohort analyses. These techniques highlight the ability to analyze customer behavior longitudinally.
2. Voice of the Customer Analysis
- • Technique: Text Mining, Topic Modeling (e.g., Latent Dirichlet Allocation)
- • Application: Extracting insights from customer feedback involves text mining and topic modeling. These techniques demonstrate proficiency in handling unstructured data.
Engage: Fostering Long-Term Relationships
1. Churn Models
- • Technique: Classification, Survival Analysis
- • Application: Predicting customer churn and understanding its drivers can be achieved using survival analysis and classification techniques. Building churn models showcases predictive analytics skills.
2. Next-Best-Action Models
- • Technique: Markov Chains, Reinforcement Learning
- • Application: Determining the next best action for engaging customers involves sequential decision-making techniques. This highlights advanced analytical capabilities.
3. Lifetime Value Models
- • Technique: Survival Analysis, Regression Analysis, Monte Carlo Simulations
- • Application: Estimating the lifetime value of customers involves understanding the duration and value of customer relationships. Survival analysis is particularly useful for modeling the time until churn or other critical events, while regression models and simulations can help forecast long-term customer value. Using these techniques demonstrates the ability to handle both temporal and financial aspects of customer analytics.
4. Customer Location Analysis
- • Technique: Geospatial Analysis (e.g., GIS Mapping)
- • Application: Understanding the geographical context of customer behavior involves geospatial analysis techniques, showcasing the ability to work with location-based data.
5. Loyalty Models
- • Technique: Logistic Regression, Clustering
- • Application: Building models to understand and enhance customer loyalty involves classification and clustering techniques, demonstrating the ability to analyze customer retention.
Conclusion
1. Product and Recommendation Analysis
- • Technique: Collaborative Filtering, Matrix Factorization
- • Application: Recommending products based on user behavior involves techniques like collaborative filtering. This demonstrates the ability to handle recommendation systems and personalize user experiences.
2. Sentiment Analysis
- • Technique: Natural Language Processing (NLP) (e.g., Text Classification, Sentiment Analysis Models)
- • Application: Analyzing customer feedback to gauge sentiment involves NLP techniques. Building sentiment analysis models showcases skills in text analytics and machine learning.
Ask: Learning About Engagement
1. Engagement Analysis
- • Technique: Survival Analysis, Cohort Analysis
- • Application: Understanding customer engagement over time can be done using survival and cohort analyses. These techniques highlight the ability to analyze customer behavior longitudinally.
2. Voice of the Customer Analysis
- • Technique: Text Mining, Topic Modeling (e.g., Latent Dirichlet Allocation)
- • Application: Extracting insights from customer feedback involves text mining and topic modeling. These techniques demonstrate proficiency in handling unstructured data.
Engage: Fostering Long-Term Relationships
1. Churn Models
- • Technique: Classification, Survival Analysis
- • Application: Predicting customer churn and understanding its drivers can be achieved using survival analysis and classification techniques. Building churn models showcases predictive analytics skills.
2. Next-Best-Action Models
- • Technique: Markov Chains, Reinforcement Learning
- • Application: Determining the next best action for engaging customers involves sequential decision-making techniques. This highlights advanced analytical capabilities.
3. Lifetime Value Models
- • Technique: Survival Analysis, Regression Analysis, Monte Carlo Simulations
- • Application: Estimating the lifetime value of customers involves understanding the duration and value of customer relationships. Survival analysis is particularly useful for modeling the time until churn or other critical events, while regression models and simulations can help forecast long-term customer value. Using these techniques demonstrates the ability to handle both temporal and financial aspects of customer analytics.
4. Customer Location Analysis
- • Technique: Geospatial Analysis (e.g., GIS Mapping)
- • Application: Understanding the geographical context of customer behavior involves geospatial analysis techniques, showcasing the ability to work with location-based data.
5. Loyalty Models
- • Technique: Logistic Regression, Clustering
- • Application: Building models to understand and enhance customer loyalty involves classification and clustering techniques, demonstrating the ability to analyze customer retention.
Conclusion
1. Churn Models
- • Technique: Classification, Survival Analysis
- • Application: Predicting customer churn and understanding its drivers can be achieved using survival analysis and classification techniques. Building churn models showcases predictive analytics skills.
2. Next-Best-Action Models
- • Technique: Markov Chains, Reinforcement Learning
- • Application: Determining the next best action for engaging customers involves sequential decision-making techniques. This highlights advanced analytical capabilities.
3. Lifetime Value Models
- • Technique: Survival Analysis, Regression Analysis, Monte Carlo Simulations
- • Application: Estimating the lifetime value of customers involves understanding the duration and value of customer relationships. Survival analysis is particularly useful for modeling the time until churn or other critical events, while regression models and simulations can help forecast long-term customer value. Using these techniques demonstrates the ability to handle both temporal and financial aspects of customer analytics.
4. Customer Location Analysis
- • Technique: Geospatial Analysis (e.g., GIS Mapping)
- • Application: Understanding the geographical context of customer behavior involves geospatial analysis techniques, showcasing the ability to work with location-based data.
5. Loyalty Models
- • Technique: Logistic Regression, Clustering
- • Application: Building models to understand and enhance customer loyalty involves classification and clustering techniques, demonstrating the ability to analyze customer retention.
Conclusion
For young professionals, students, and career shifters, mastering these analytical methods and techniques offers a comprehensive pathway to building a strong portfolio in analytics. By applying these methods to real-world business scenarios, aspiring analysts can demonstrate their capability to drive strategic decisions, optimize marketing efforts, and enhance customer experiences. Embracing customer lifecycle analytics as a starting point not only provides practical experience but also opens doors to various opportunities in the analytics domain, paving the way for a successful career in this ever-evolving field.
References:
Sridharan, S. & Purcell, B. (2015). How Analytics Drives Customer Life-Cycle Management. Vision: The Customer Analytics Playbook. Forrester.
Baesens, B. (2014). Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications. John Wiley & Sons, Inc., Hoboken, New Jersey.
Grigsby, M. (2015). Marketing Analytics: A practical guide to real marketing science. Kogan Page.
Keywords:
customer relationship management, crm, customer lifecycle, data, analytics, practical use-cases, applied analytics, regression, classification, association rules, survival analysis, analytics portfolio
Back to top
Other Posts
Creating Dynamic Maps in Excel: A Step-by-Step Tutorial
Struggling to make your data presentations visually impactful and easily understandable? Traditional charts often fall short in illustrating geographic trends clearly, leaving your audience confused and disengaged. My Excel dynamic maps tutorial simplifies complex data visualization, enabling you to create engaging, clear maps that captivate and inform your audience effortlessly.
06 Jun 2024
The Power of Niche: How to Stand Out in AI and Analytics as a Young Professional or Non-Techie
Navigating a career in AI and Analytics can often feel overwhelming due to its complexity and rapid pace of change. Without a focused approach, many professionals find themselves lost, unable to capitalize on the right opportunities or advance in their careers. This article introduces a clear pathway to discovering your niche in AI and Analytics, helping you to build deep expertise, stand out in a crowded market, and significantly enhance your career trajectory.
03 Jun 2024
Discover the Power of Data with UNDP's "Data Thursdays" Free Webinars
Advocates for social change often struggle to effectively use complex data for Sustainable Development Goals (SDGs). Without strong data skills, making a real impact can feel out of reach. Empower your advocacy with "Data Thursdays" by UNDP on SparkBlue—free webinars to boost your data literacy.
20 May 2024