Customer Retention Prescriptive Data Analysis -- 2

@EightVeer

Hi there, We will deliver a prescriptive customer retention analysis that turns your CRM, support ticket, usage, and marketing touchpoint data into ranked actions tied to churn and CLV. We will clean and document the dataset, build and validate a model, explain feature importance, and translate the findings into a concise report or slide deck with implementation priorities. Our public Freelancer review history covers AI adoption, data management, and analytical engagements. This first workstream is limited to campaign and market diagnostic with prioritised recommendations for Customer Retention Prescriptive Data Analysis -- 2. Broader implementation would be scoped separately on Freelancer. Best Regards, 8veer

@mobimubasir

Hello there, I can deliver a complete prescriptive churn and retention analysis using Python/SQL, combining data cleaning, statistical analysis, churn modeling, feature importance, and validated what-if simulations to identify the strongest drivers of customer loss. I will translate the findings into prioritized retention actions tied to measurable KPIs such as churn rate, CLV, projected uplift, and implementation effort, with model metrics, documented code, and an executive-ready report/slide deck plus an optional Power BI/Tableau dashboard. My workflow covers data preparation, exploratory analysis, model development and validation, scenario testing, and final recommendations in clear milestones, allowing actionable findings to be delivered quickly while keeping the entire analysis reproducible and business-focused.

@maxscender

I can help you turn your churn data into a clear retention playbook, not just a report. I’ll use Python (pandas, scikit-learn, SHAP) to clean and model your CRM, support, and usage data. The approach: - Build a validated churn model (gradient boosting or logistic regression) with accuracy/ROC metrics. - Extract feature importance to explain *why* customers leave. - Simulate what-if scenarios for loyalty offers, upsell timing, and messaging changes — tied directly to churn rate and CLV impact. - Deliver a prioritized action list ranked by projected uplift and implementation effort, plus a short slide deck and optional Power BI dashboard. Deliverables include documented code, cleaned dataset, and a concise executive summary you can act on immediately.

@nrajuu07

I'm a data scientist with experience building end-to-end churn prediction and prescriptive analytics pipelines using Python, Scikit-learn, and XGBoost over CRM and behavioural datasets. I'll clean and document your customer data, build a churn model with rigorous validation and feature importance analysis to explain exactly why customers leave, run what-if scenario simulations to quantify the impact of potential interventions, and translate everything into a prioritised report with concrete retention strategies tied to measurable KPIs like churn rate and CLV. Deliverables include cleaned data with SQL scripts, model code, a ranked recommendations report with projected uplift and implementation effort, and an optional Power BI dashboard. Proposed timeline is 7 to 10 days from data access. Ready to start immediately.

@DevDataAnalyst

Hi there! ? I’ve spent the last 10+ years helping businesses make sense of their data — turning numbers and reports into clear, visual stories that drive smarter decisions. My expertise includes Power BI, Tableau (Desktop & Server), R, SQL Server, and KNIME for data prep and automation. Whether it’s building a simple KPI dashboard or a full-scale analytics solution, I focus on making your data work for you — easy to understand, easy to use, and built around your goals. If you’re looking for someone who blends technical skill with a business mindset, let’s connect and discuss how I can help bring your data to life.

@robertb297

The useful part of this project is connecting the churn model to actual retention decisions. I’d look at customer behavior across CRM, support, usage, and marketing data rather than treating churn as a single-variable prediction problem. My approach: Clean and combine the available customer datasets and build meaningful churn features. Test a few suitable models and validate them with metrics such as ROC-AUC, precision/recall, and calibration. Turn the strongest drivers into prioritized retention actions with measurable KPIs. I work with Python, pandas, scikit-learn, SQL, and data visualization, and I’ll provide the analysis code/notebook so the results can be reproduced. Do you already have the anonymized dataset and churn labels ready?

@gunjan1711

- I have hands-on experience using SQL, Python, statistical modeling, and Power BI to turn customer data into predictive and actionable business insights. - My expertise includes churn analysis, predictive analytics, customer segmentation, feature importance, statistical modeling, data visualization, and retention analytics. - I can integrate CRM, support-ticket, usage, and marketing data, clean and validate the datasets, engineer relevant features, and identify the strongest drivers of customer churn. - I have experience developing predictive models, evaluating model accuracy, interpreting feature importance, and translating analytical findings into practical business recommendations. - In a recent project, I analyzed a 196K+ sales dataset using Python and SQL, developed interactive Power BI dashboards, and generated actionable insights to support business decision-making. - My approach will combine exploratory analysis, churn modeling, driver analysis, what-if scenarios, and prioritization based on expected customer impact versus implementation effort. - Deliverables will include the cleaned dataset, reproducible SQL/Python code, model evaluation and feature-importance analysis, prioritized retention recommendations, and an optional Power BI/Tableau dashboard. - I am available to start immediately and can provide regular progress updates throughout the project. Reference work is available in my profile.

@echekwa

Dear client, I would love to offer my services in prescriptive data analysis. I am an experienced data analyst with 3 completed customer retention analysis projects. My approach: (1) clean and merge CRM, ticket, and usage data into a unified dataset; (2) exploratory analysis to surface churn drivers; (3) build a classification model (Random Forest/XGBoost) with SHAP-based feature importance for interpretability; (4) validate via cross-validation, precision/recall, and AUC; (5) simulate what-if scenarios (e.g., proactive outreach, discount timing) to estimate uplift; (6) deliver a prioritized action plan tied to churn rate/CLV impact, plus an optional Power BI dashboard. Tools: Python (pandas, scikit-learn), SQL, Power BI. Estimated timeline: 3-5 days. Regards, Chijioke

@elijahhmonreal

Hi there, I just read your posting. It sounds like you need a data scientist who can move beyond churn reporting and build a prescriptive retention framework that explains why customers leave, predicts risk, and identifies the highest-impact actions to improve retention and CLV. I am a Data Scientist and AI Engineer with 10+ years of experience in Python, SQL, machine learning, predictive modeling, and customer analytics. I can clean and unify CRM, support, usage, and marketing data, build and validate churn models, analyze feature importance, and run what-if scenarios. I can deliver documented notebooks/SQL, model code with accuracy metrics, prioritized retention strategies ranked by projected uplift and effort, and a concise report or dashboard. I typically use Python, pandas, scikit-learn/XGBoost, SQL, SHAP, and Power BI/Tableau. Let me know if my profile looks interesting, and we can set up a time to talk. Best regards, Elijah M.

@Algorista92

Hi, nice to greet you. This is Matías speaking from Córdoba, Argentina. I am the CEO and founder of MJE Data Consulting, a consultancy specialized in data science, statistical analysis, artificial intelligence, and data-driven solutions. I can help you develop an end-to-end prescriptive analysis focused on customer retention, starting with data cleaning and exploratory analysis and progressing toward churn modeling, feature importance analysis, what-if scenarios, and actionable retention recommendations. I can combine CRM data, support interactions, usage behavior, engagement metrics, and marketing touchpoints to identify the factors most strongly associated with churn and translate them into measurable business actions. My preferred stack is Python, Pandas, Scikit-learn, SQL, and Power BI. I can develop and validate predictive models using appropriate metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and churn-related KPIs, while also quantifying the potential impact of different retention strategies. Recommendations can then be prioritized according to expected business impact and implementation effort. I would like to review the anonymized dataset, available variables, churn definition, and current retention KPIs to understand the business context and define the appropriate methodology, modeling approach, deliverables, and timeline. Best regards, Matías

@Huzaifa366

Hi, I can help turn your CRM, support, usage, and marketing data into actionable customer-retention strategies, going beyond descriptive analytics to identify churn drivers and simulate what-if interventions. I have experience with Python, Pandas, NumPy, Scikit-learn, statistical modeling, ML, feature engineering, and Power BI. My approach: • Clean, validate and document the customer dataset • Engineer behavioral, engagement and support features • Compare churn models such as Logistic Regression, Random Forest and Gradient Boosting • Validate using cross-validation, ROC-AUC, Precision, Recall and F1 • Use feature importance/SHAP to explain key churn drivers • Segment customers by churn risk and value • Perform what-if analysis for offers, messaging, upsells and workflow changes • Rank recommendations by expected impact vs. implementation effort • Connect recommendations to KPIs such as churn rate, retention and CLV Deliverables: reproducible Python notebook/scripts, cleaned dataset, trained model with explainability, actionable report/slide deck, and optionally a Power BI retention dashboard. Timeline: 10–12 days, depending on data complexity. I can start immediately. Best regards, Muhammad Huzaifa

@bojans9

Hi - Bojan here from Serbia "CUSTOMER RETENTION PRESCRIPTIVE ANALYSIS" — you need to know why users leave and what actions will keep them longer. I would combine data cleaning, churn prediction models, and feature importance analysis to identify the behaviors linked to customer loss. Then I would translate those findings into practical steps like targeted offers, messaging changes, and workflow improvements. I would also validate the model results with clear metrics and create an understandable report so your team can act on the insights instead of only viewing charts. Which customer data sources are available first, and how many months of history do you have for churn analysis? Looking forward to working with you.

@ashrafa275

Hi there, I am a Systems Engineer with strong expertise in Data Analysis, Machine Learning, and Python. I can deliver a full prescriptive churn and retention analysis for your dataset. Proposed Methodology & Tools: 1. Data Cleaning & Feature Engineering (Python/Pandas/SQL): Clean anonymized records, handle missing values, and engineer engagement/frequency metrics. 2. Predictive Modeling & Root Cause Analysis (Scikit-Learn/XGBoost): Train classification models (Logistic Regression, Random Forest, XGBoost) to predict churn and extract SHAP/Feature Importance values to understand *why* customers leave. 3. Prescriptive Strategy & What-If Scenarios: Simulate loyalty offer impacts, upsell timing, and workflow tweaks mapped to CLV and Churn Rate KPIs. 4. Report & Dashboard: Deliver a concise executive slide deck ranking actions by uplift vs. effort, along with an interactive Power BI / Matplotlib summary dashboard. Sample Timeline (2 Days): • Day 1: Data cleaning, EDA, feature engineering, and model training/validation. • Day 2: Feature importance analysis, what-if simulations, slide deck creation, and dashboard finalization. Deliverables: Cleaned dataset, commented Jupyter Notebook/SQL scripts, validated model code, slide deck report, and dashboard visuals. Ready to start as soon as you share the dataset! Best regards, Achraf

@Ace5th

Hi, I'm interested to work on your projects as this is something I often do, I can turn this around in a few days once I have data access. My approach: 1. Consolidate CRM, ticket, and usage data into a clean, documented dataset. 2. Build a churn model eg. logistic regression with proper validation. 3. Simulate what-if scenarios (offer timing, targeting, messaging) and estimate impact on churn rate 4. Deliver a prioritized action list ranked by uplift vs. effort Deliverables: documented notebook + scripts, model code with validation metrics, a concise slide deck with ranked recommendations, and an optional Power BI/Tableau dashboard. Timeline: 3–5 days Day 1: Data access, cleaning, consolidation, exploratory analysis Day 2: Model build + validation Day 3: Feature importance, scenario simulation, recommendation ranking Day 4–5: Deck/dashboard build, review, handoff This assumes data is reasonably clean and available on day one, I'd confirm scope and volume on a quick call first so the timeline holds. Best, Ajie

@pradeeps139

Hi, I’d be happy to help you analyze your customer data and turn it into practical retention strategies. I can handle the complete workflow, including data cleaning, EDA, customer segmentation, churn-driver analysis, statistical analysis, and machine-learning churn prediction. I’ll focus not only on identifying which customers are likely to leave, but also on understanding why they churn and what actions can reduce churn. Deliverables: Cleaned and validated customer dataset Churn and customer-behavior analysis Identification of key churn drivers Customer segmentation and high-risk customer identification ML/statistical churn model with proper validation What-if scenario analysis Clear visualizations and insights Prioritized retention recommendations such as loyalty offers, upselling timing, personalized messaging, and workflow improvements Final report explaining findings in business-friendly language I have experience with Python, Pandas, NumPy, SQL, Excel, Power BI, and machine-learning techniques, and can work through the project from raw data to actionable recommendations. I’ll make sure the analysis is accurate, well-documented, and focused on measurable business impact, rather than just producing charts or model outputs. I’m ready to start immediately. Best regards, Pradeep Singh Data Analyst | Python | SQL | Power BI | Machine Learning

@devendrac88

I understand that you need more than a basic customer-retention analysis. The goal is to identify **why customers churn, predict retention risk, evaluate possible interventions, and turn the findings into measurable actions**. I can help with: • Data cleaning, preprocessing and exploratory analysis • Customer churn/retention modeling using suitable ML/statistical techniques • Feature importance and identification of key churn drivers • Customer segmentation and high-risk customer identification • What-if analysis for retention strategies • KPI-focused recommendations using churn rate, CLV and retention metrics • Clear visualizations and business insights • Clean, documented Python/SQL code and analysis notebook • Concise report/slide deck with prioritized recommendations • Power BI dashboard if required My approach would be: 1. Understand and clean the CRM, support and usage data 2. Perform EDA and identify important behavioral patterns 3. Build and validate suitable predictive models 4. Analyze feature importance and churn drivers 5. Translate model findings into practical retention actions 6. Rank recommendations by expected impact and implementation effort I work with **Python, Pandas, NumPy, SQL and data visualization**, and I focus on converting technical analysis into clear business decisions. I can start immediately and provide the analysis in a clean, reproducible format.

@tanishav38

B.Sc. Statistics with Data Science | Data Analyst Intern skilled in Advanced Python, SQL, Power BI & ML

@PTNprodigies101

I will transform your multi-source customer data—CRM, support tickets, and usage logs—into a powerful predictive and prescriptive engine designed to maximize Customer Lifetime Value (CLV) and systematically mitigate churn. Utilizing an advanced Python and SQL tech stack, I will build robust Machine Learning models (such as XGBoost or LightGBM) paired with SHAP (SHapley Additive exPlanations) to uncover not just if a customer will leave, but exactly why. My approach goes beyond standard diagnostics; I will construct a predictive simulation framework to model what-if scenarios, allowing your team to quantify the impact of proactive retention strategies before deployment. You will receive a clean, fully documented data pipeline, production-ready model code with rigorous validation metrics (ROC-AUC, Precision-Recall), and a highly polished, executive-ready slide deck that translates complex statistical features into a prioritized action roadmap ranked by projected revenue uplift and implementation effort. Let’s connect to align on data ingestion and accelerate your retention KPIs.

@AbdallahWk

Hello, I’m a Data Analyst with experience in Python, SQL, Power BI, Excel, and statistical analysis, and I can help turn your customer data into practical retention strategies. My approach would start with cleaning and validating the CRM, support, usage, and marketing data, followed by exploratory analysis to identify the main patterns associated with churn. I would then build and evaluate an appropriate classification model to predict churn, using metrics such as precision, recall, F1-score, ROC-AUC, and confusion matrix results. Feature importance and customer segments would help identify the strongest drivers behind customer loss. From there, I would translate the findings into prioritized retention actions, such as targeted offers, engagement campaigns, upsell timing, or workflow improvements. Each recommendation would be connected to measurable KPIs such as churn rate, retention rate, and customer lifetime value, with estimated impact where the available data supports it. Preferred tools: Python (Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn), SQL, and Power BI. Proposed timeline: • Day 1: Data cleaning, validation, and exploratory analysis • Day 2–3: Feature engineering and churn modeling • Day 4: Model evaluation, feature importance, and segmentation • Day 5: Recommendations, visualizations, and final report/dashboard I’ll provide clean, documented code and a clear final report so the analysis is reproducible and easy for your team to act on.