Customer Retention Prescriptive Data Analysis

@EightVeer

Hi there, We will deliver a prescriptive customer retention analysis that turns CRM, support ticket, usage log, and marketing touchpoint data into ranked actions you can act on quickly. We will clean and document the dataset, build and validate churn modeling with feature importance and what-if scenarios, then translate the findings into a concise report or slide deck tied to churn rate and CLV. We bring public Freelancer review history covering AI adoption, data management and analytical engagements. Would you like the optional dashboard included in this scope? For this bid, we would deliver campaign and market diagnostic with prioritised recommendations for Customer Retention Prescriptive Data Analysis. Any later implementation would remain a separate Freelancer scope. Best Regards, 8veer

@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

I'll start by merging CRM, support ticket, and usage log data into a unified customer view, engineering features around engagement trends, support friction, and touchpoint timing. From there, a churn classification model (likely XGBoost, benchmarked against logistic regression for interpretability) identifies the strongest predictive drivers, validated through cross-validation and reported honestly with accuracy, precision, and recall — not inflated numbers. The prescriptive layer is where this goes further: using the model's feature importance and SHAP values, I'll run what-if simulations testing how changes like earlier upsell timing, targeted loyalty offers, or workflow adjustments shift predicted churn probability for at-risk segments. Each resulting recommendation gets ranked by projected CLV/churn-rate uplift against realistic implementation effort, so priorities are obvious. Preferred tools: Python (pandas, scikit-learn, SHAP) for modeling, with a Power BI or Tableau dashboard visualizing key retention drivers if you'd like the ongoing monitoring layer. Tentative timeline: data consolidation and EDA (week 1), modeling and validation (week 2), simulation and recommendations deck (week 3) — adjustable based on data volume and complexity once I see it.

@robertb297

Hi there What stood out to me in your description is that you explicitly separated descriptive analysis from prescriptive, saying the work goes beyond describing what happened. A lot of churn projects quietly stop at "here's a model that predicts who's leaving," but you're asking for the next layer, the what-if simulation and prioritized action list ranked by projected uplift versus implementation effort. That's a fundamentally different deliverable, because it means every insight has to be traced all the way through to a testable business action, not just a feature importance chart that looks compelling but doesn't tell you what to actually do on Monday morning. My approach would start with the descriptive groundwork done properly, cleaning and merging the CRM, support ticket, and usage log data into one unified customer view, since retention drivers usually hide in the interaction between data sources rather than any single one, someone with rising support tickets and dropping usage tells a very different story than either signal alone. From there I'd build a churn model, likely a gradient boosted approach for the accuracy plus interpretable feature importance, validated with proper holdout testing so the accuracy metrics you asked for are genuinely trustworthy rather than optimistic. The prescriptive layer is where I'd spend the real effort, running scenario simulations around specific interventions, timing an upsell offer earlier, adjusting messaging cadence, and tying each recommendation back to a measurable KPI like churn rate or CLV so you're never left with an insight you can't act on or measure afterward. I'd package everything into the notebook or SQL scripts, the model code with feature explanations, and a concise slide deck ranking the recommendations, with the dashboard as an optional add-on if you want ongoing visibility into the drivers rather than a one-time snapshot. Quick question before I finalize a timeline, roughly how much historical data are we talking about in terms of time span and customer volume, since that affects whether a few days is realistic for rigorous validation or whether we should scope this as a phased engagement?

@durgesh8527

Combining my deep understanding of statistical modeling and machine-learning techniques with my versatile skill set in SQL and Data analytics tools, I am uniquely suited to provide you with the insightful analyses and prescriptive recommendations you need for your customer retention project. Having worked extensively on projects like this in the past, I know that merely knowing what has transpired is not enough - understanding 'why' and 'what next' is key. I am confident in my abilities to unravel deep insights as to why customers churn, simulate different scenarios and provide you with clear, actionable steps you can take immediately. My preferred methodology includes a meticulous process- from cleaning the dataset, to employing rigorous statistical modeling techniques and interpreting the results unraveled by the models. The latter will allow me to prioritize recommendations based on projected impact and implementation effort. As a capstone, I will deliver a concise slide deck or report, outlined KPI-driven actionable next steps and even an optional dashboard if that aligns with your requirements. Lastly, apart from being technically sound, I place great emphasis on effective communication throughout each stage of the project, a quality that ensures project success and client satisfaction. I believe this combined with my skill-set makes me the suitable candidate for Data Science Solution on your project to boost customer retention.

@sayarsinghseo

In the era of data-driven decision-making, I understand the paramount importance of using robust statistical modeling techniques and machine-learning tools to mine actionable insights from a complex set of customer data like yours. Being adept at SQL, I will streamline and document your data, transforming it into a clean, organized dataset for our analysis. My overarching goal will be to unravel the 'why' behind customer churn, and then use this understanding to create concrete, prioritized retention strategies for your firm. Throughout the process, I'd leverage my expertise in data visualization (Tableau) to help you track these strategies and ensure they are continuously tied to measurable KPIs - an acceptance criterion that is non-negotiable. Having successfully helped numerous businesses improve their visibility and revenue by making strategic sense of their digital analytics, my track record demonstrates my ability to deliver exactly what you need—results-oriented insights and actionable tactics that drive impact. Let's create a comprehensive data-analysis roadmap together, in alignment with your desired timeline and expectations.

@fatema0013

I can help turn your CRM, support, usage, and marketing data into actionable retention strategies—not just a churn prediction model. My methodology: • Clean and integrate customer, CRM, support, usage, and marketing data using Python/SQL. • Perform EDA and statistical analysis to identify significant churn drivers. • Engineer behavioral features such as recency, frequency, engagement, support activity, plan changes, and campaign interactions. • Build and compare Logistic Regression, Random Forest, and XGBoost models. • Validate using cross-validation, ROC-AUC, PR-AUC, precision, recall, F1, and confusion matrix. • Use feature importance/SHAP to explain why customers churn. • Segment customers by risk and identify targeted actions such as loyalty offers, personalized messaging, upselling, and customer-success interventions. • Conduct what-if/uplift analysis where the data supports it and rank recommendations by expected impact vs. implementation effort. Tools: Python, Pandas, NumPy, Scikit-learn, XGBoost, Statsmodels, SHAP, SQL, Power BI/Tableau, and Jupyter. Timeline: 1–2 days data preparation, 2–3 days modeling/driver analysis, 2 days prescriptive analysis, and 1 day final report/dashboard. I’ll deliver clean documented data, reproducible code, validated models, key churn drivers, and KPI-focused recommendations tied to churn, retention, CLV, and projected uplift. Best regards, Fatema

@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.

@rodiongusev1607

I will deliver a prescriptive analysis to identify key drivers of churn and provide actionable strategies to improve retention. I will use SQL for data cleaning, Python for modeling, and Tableau for visualization. Next, I will build a predictive model to understand why customers leave and simulate what-if scenarios. Then, I will create a prioritized list of retention tactics with expected impact on churn rate and CLV. Could you share the data structure and sample size? What are the specific KPIs you want to track? How are the marketing touchpoints labeled in the dataset?

@ashrafa275

Hi there, I can deliver a complete data-driven customer retention and churn analysis for your dataset using Python (Pandas, Scikit-Learn) and SQL. What I will provide: 1. Cleaned Dataset & Code: Fully documented Jupyter Notebook / SQL scripts showing data cleaning, EDA, and feature importance. 2. Machine Learning / Predictive Model: Statistical modeling to pinpoint exact churn drivers and simulate what-if scenarios. 3. Executive Presentation/Report: Clear, prioritized retention strategies tied to KPIs like CLV and Churn Rate. 4. Optional Dashboard: Clean visual summary of key drivers using Python (Matplotlib/Seaborn) or Power BI. I have a strong background in Machine Learning, Python, and Data Analysis. Ready to start as soon as you share the dataset!

@elijahhmonreal

Hi there, I just read your posting. It sounds like you need a prescriptive churn analysis that goes beyond identifying retention problems and turns customer behavior into prioritized actions with measurable impact. I am an AI/ML Engineer with 10+ years of experience in Python, SQL, machine learning, statistical analysis, customer segmentation, and predictive modeling. I can combine CRM, support, usage, and marketing data to identify churn drivers, build and validate retention models, and explain feature importance clearly. I can deliver cleaned data and reproducible notebooks, churn/CLV modeling, what-if simulations, and recommendations ranked by projected uplift and implementation effort. I typically use Python, Pandas, Scikit-learn/XGBoost, SQL, SHAP, and Power BI/Tableau, with work structured from data audit through modeling, validation, and final recommendations. Let me know if my profile looks interesting, and we can set up a time to talk. Best regards, Elijah M.

@hardikt89

I have a good expertise in AI &ML projects as I did my graduation in this and currently contributing with the same skills.

@edwinewing

1. Clean & merge CRM, support ticket, and usage log data into a customer-level dataset (documented notebook/SQL). 2. EDA: churn distribution, segmentation, initial feature correlations. 3. Predictive modeling: Logistic Regression baseline + XGBoost/Random Forest, validated via cross-validation (AUC-ROC, F1). SHAP for feature importance. 4. Prescriptive layer: uplift/scenario simulation to estimate impact of interventions (loyalty offers, upsell timing, messaging). Recommendations prioritized by impact vs. effort. 5. Deliverable: concise slide deck with top ranked recommendations tied to churn rate/CLV, plus optional Power BI/Tableau dashboard. Tools: Python (pandas, scikit-learn, XGBoost, SHAP), SQL, Power BI/Tableau. Timeline: 4-5 working days depending on data size. Happy to start once the anonymized dataset is shared — could you share the data size/format to refine the estimate?

@bhutrakichu

I am Neha, a data analyst with a passion for turning complex information into actionable insights. Having worked on similar projects in the past, I understand the importance of thorough data analysis in driving business decisions and boosting customer retention. My skills in SQL will help me clean, analyze, and extract the most valuable insights from your CRM, support tickets, and usage logs. As mentioned in my profile, I am proficient in transforming data into engaging visualizations using Power BI—a tool that can provide you with an optional dashboard illustrating key retention drivers. This visualization will not only help you understand why customers leave but also enable you to simulate what-if scenarios and implement effective strategies to boost retention. Indeed, my deliverables won't just stop at reports and slides—they'll be tied to measurable KPIs and supported by model accuracy metrics—a key factor for success in this project. Regarding methodology, I propose using statistical modeling and machine-learning techniques, complemented with your anonymized customer records, churn labels, engagement metrics, and marketing touchpoints. This approach will allow me to give you prioritized recommendations—such as loyalty offers, upsell timing, personalized messaging or workflow changes—that are specific to your company's needs And when it comes to timeline—I understand that time is money—I assure you of delivering on schedule without compromising on quality.

@ruchitas11

I have a strong background in Python, SQL, Data Analysis, Statistical Analysis, Predictive Analysis and Power BI and your customer retention project is a great match for my skills. I can analyze your CRM, support, usage and marketing data to identify the key factors driving customer churn and translate those findings into practical, measurable retention strategies. I will use Python (Pandas, NumPy, Scikit-learn), SQL and Power BI/ Tableau to clean data, perform exploratory analysis, build and validate predictive models and identify the most important churn drivers. I will deliver: - Clean and documented customer dataset - Reproducible Python notebook and SQL scripts - Exploratory analysis of customer behaviour and engagement - Customer segmentation and risk analysis where applicable - Clear visualizations and an optional Power BI dashboard Concise report or presentation summarising insights and recommended actions My approach will go beyond simply predicting who is likely to churn. I will focus on understanding why customer leave which customer groups require attention and what actions could potentially improve retention. I will ensure the analysis is statistically sound, the model is properly validated and all recommendations are supported by measurable evidence. I cam start immediately and provide the provide the initial data analysis and methodology withing the first stage of the project.

@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.

@tanishav38

I’m a strong fit for this project because I have completed my B.Sc. in Statistics with Data Science and currently work as a Data Analyst Intern. I have hands-on experience with Python, SQL, Excel, Power BI, statistical analysis, machine learning, predictive modeling, and data visualization. I have also worked on multiple predictive modeling projects, including data preprocessing, feature engineering, model evaluation, and extracting meaningful insights from data. My statistics background helps me understand customer behavior, identify important churn factors, and turn analytical results into practical, data-driven recommendations.

@hadeera78

Hello, I can turn your customer, CRM, support, usage, and marketing data into actionable retention strategies—not just descriptive insights. My approach will include: • Cleaning, validating, and integrating the datasets using Python/SQL. • Exploratory and statistical analysis to identify key churn drivers. • Building and validating churn prediction models using Scikit-learn. • Evaluating performance with ROC-AUC, Precision, Recall, F1-score, and other relevant metrics. • Analyzing feature importance to understand why customers churn. • Segmenting customers by churn risk and potential value. • Developing prioritized retention actions such as targeted offers, messaging, and upsell timing. • Ranking recommendations by expected impact and implementation effort. • Measuring potential impact using KPIs such as churn rate, retention rate, and CLV. • Optionally creating a Power BI dashboard to monitor retention drivers. Tools: Python, Pandas, NumPy, Scikit-learn, SQL, Matplotlib/Seaborn, and Power BI. I will provide clean documented data, reproducible code, model results, and a concise report translating the analysis into practical actions your team can implement. I’m ready to review the anonymized data and churn definition and start immediately.

@exdeveloper75

Hello, I’m interested in turning your CRM, support, usage, and marketing data into an actionable customer-retention strategy—not just a churn report. My methodology: • Clean and validate the datasets, handle missing values/outliers, and engineer customer-level features from engagement, usage, support, tenure, and marketing activity. • Build and compare churn models using Logistic Regression, Random Forest, and XGBoost. Validate with ROC-AUC, PR-AUC, precision, recall, F1, cross-validation, and calibration. • Use SHAP/feature importance to identify the strongest churn drivers. • Perform prescriptive and what-if analysis to identify high-risk customers, optimal intervention timing, and potential impact of retention actions. • Prioritize recommendations by expected uplift, confidence, ROI, and implementation effort, tied to KPIs such as churn rate, retention, CLV, and revenue at risk. Deliverables: cleaned/documented data, reproducible Python/SQL code, validated model, explainability analysis, actionable recommendations, and executive report. I can also provide a Power BI/Tableau dashboard. Tools: Python, SQL, Pandas, Scikit-learn, XGBoost/LightGBM, SHAP, and Power BI. Estimated timeline: 10–12 days. I focus on evidence-based insights that can be translated directly into measurable business results.