Applied Data Analytics for Impact

Introduction CRM data is rarely stored in a single, analysis ready table. Customer details, interactions, transactions, and campaigns are usually split across multiple datasets, often with inconsistent keys and repeated records. As a result, analysts frequently encounter inflated metrics, broken joins, and confusing totals. These issues are not caused by SQL itself, but by how joins, aggregations, and deduplication are applied. Getting these fundamentals right is essential for trustworthy CRM analytics. Most CRM metrics depend on combining tables correctly: counting unique customers attributing interactions to campaigns summarising behaviour over time If joins are misaligned or duplicates are not handled deliberately, metrics quietly drift. Dashboards may look correct but tell the wrong story. Intermediate SQL skills allow analysts to express clear analytical intent , not just retrieve data. Thinking before writing SQL Before writing queries, I focus on three questi...