Optimize Lead Source Conversions

@shikhajatav396

I can analyze your CRM and email data, build cohorts, identify conversion drivers, and deliver actionable insights using SQL, Python, and interpretable machine-learning models. I have 5+ years experience in Python, SQL, data analysis, data visualization, and database management. I can provide reproducible notebooks, cohort comparisons, model performance metrics, feature importance, and clear data-backed recommendations. Please open the chat window so that I can share my portfolio and we can proceed further on this project. In addition to your project needs, I'll provide you with clean source code, free bug patches, and maintenance. I am awaiting your positive response. Regards Shikha

@CliveMSoftware

I've run similar funnel analyses for SaaS and e-commerce clients. Here's my approach: I'll write clean, modular SQL to build your cohort tables and aggregate conversion events by lead source, then construct a feature-rich modeling dataset with time-decay engagement metrics. I'll train a Random Forest classifier to predict first-purchase conversion, extract feature importances, and validate performance with ROC-AUC and precision-recall curves. The deliverable is a Jupyter notebook with reproducible code, annotated SQL queries, side-by-side cohort comparison charts, and a one-page executive summary with three prioritized, data-backed recommendations your growth team can execute immediately. Turnaround is 4-5 days from data hand-off.

@zuaibahmedd

Hi, I can analyze your CRM and email-marketing data to identify the lead sources, cohorts, and variables driving conversions and customer value. I’ll handle the SQL data aggregation, cohort analysis, feature engineering, Random Forest/model evaluation, and clear visualizations. I’ll then deliver a reproducible Python/SQL workflow with feature importance and 3 practical, data-backed recommendations for improving conversions. I focus on turning raw data into clear business decisions, not just producing charts. I can start by reviewing your dataset and case study, then build the analysis around your actual funnel. Ready to get started.

@ressno

Hi — I can take the CRM and email-campaign exports, build a cohort table in SQL or pandas, and run a simple Random Forest so you can see which fields line up with first purchase. You get a commented notebook, side-by-side cohort charts, model scores (accuracy / ROC-AUC), and three things to try next quarter. I will not oversell the model — it is a ranking tool on the history you already have. Send a sample of the exports after award (columns only is fine) and I will confirm the join keys before I write the rest.

@mohammadhm8361

Hi, I can help turn your CRM and email-marketing history into a clear, reproducible analysis that your growth team can actually act on. My approach would be: • Build efficient SQL pipelines to clean and aggregate CRM/email events, define acquisition and time-based cohorts, and produce a modelling-ready dataset. • Analyze conversion, engagement, and customer lifetime value across lead sources, cohorts, campaigns, and relevant behavioral segments to identify where performance differs and why. • Train and validate a Random Forest (with appropriate handling of class imbalance and leakage), using ROC-AUC and other relevant metrics to assess performance. I’ll also use feature importance / permutation importance to identify the strongest factors associated with first-time purchase conversion. • Create clear cohort-over-time visualizations and a concise notebook or slide deck showing the findings, model results, and the practical implications. Most importantly, I’ll translate the analysis into at least three prioritized, data-backed actions—for example, which lead sources to scale, which engagement behaviors to target, and where funnel improvements are most likely to increase conversion and LTV. Everything will be reproducible, well-commented, and structured so your team can rerun the analysis on future CRM exports. I’m ready to start with the data and can move quickly from raw events → cohorts → model → actionable recommendations.