Data Forecasting, Mining & Duplication Audit

I am pulling information from multiple systems every day—iCloud exports, purchase and revenue files, our CRM, even stray WhatsApp logs—and it is getting messy. I need a detail-oriented analyst who can turn this scattered data into clear, trustworthy insights.

Here is what I want to achieve:
• Build an accurate data forecast so I know exactly how much usable customer data we have and how long it will sustain current campaigns.
• Mine fresh data from every available source, then consolidate everything into one clean dataset.
• Audit purchase and revenue records to spot and remove any duplication, with special attention to iCloud–generated files.
• Reconcile datasets across systems so customer counts, lead statuses in the CRM, and revenue totals all match.
• Highlight the trend of losing customers—where drops occur, how big they are, and any patterns you uncover.

Deliverables I will review and sign off:
1. A master reconciled dataset (CSV or Excel) with duplicate entries flagged or removed.
2. A forecasting model (Excel, Python, or R) showing data availability projections plus clear charts.
3. A concise report explaining customer-loss trends, the cleansing steps you took, and recommended next actions.

Accuracy and clear documentation are vital; mistakes in calls, WhatsApp records, or CRM statuses must be caught before I see the final files. If you are comfortable combining SQL, Excel, Python/R, or similar tools to hit these goals, let’s talk timing and milestones.