Applied Data Analytics for Impact

 Introduction Many analytics workflows work well once, then quietly fail over time. Data is extracted manually. Scripts are run “when needed.” Fixes are applied reactively after dashboards break. This approach doesn’t scale. As data volume and dependency chains grow, analytics teams need orchestrated pipelines , not isolated scripts. The challenge is not writing Python code. It’s designing an automated, reliable ETL flow that runs without human intervention . Why this automation of ETL is required  When ETL processes are not orchestrated: data arrives late or inconsistently quality checks are skipped under time pressure downstream dashboards lose trust analysts become operators instead of problem solvers Automation shifts analytics from reactive execution to controlled delivery . Even simple scheduling introduces: predictability accountability observability These are governance concepts, not just engineering conveniences. ETL as a system, n...

Introduction Many KPIs look precise but fail to reflect real customer behaviour. Customers are labelled “active” even if they interacted once months ago. High value customers are grouped with low engagement ones because totals look similar. Engagement scores increase even when behaviour is clearly declining. The issue isn’t calculation accuracy. It’s that KPI logic often doesn’t match how people actually behave . Why this designing matters KPIs shape decisions. They influence: who gets targeted where budget is allocated how performance is judged which customers are prioritised If KPI logic is misaligned with behaviour, teams optimise for the wrong outcomes. RFM and engagement models help anchor metrics in observable patterns , not abstract thresholds. Thinking in behaviour, not labels Behavioural KPIs work best when they answer simple questions: How recently did someone engage? How often do they engage? How meaningful is that engagement? RFM is ef...

 Introduction Topic modelling can surface recurring themes in customer text, but on its own it often raises a bigger question: Which customers are actually driving these topics? Without segmentation, topic analysis treats all feedback as equal. A concern raised by a small, high value segment and a one off comment from an infrequent user appear side by side. That makes prioritisation difficult and insight shallow. The challenge is combining what customers say with who they are . Why this combination matters Customer segmentation already helps analysts understand differences in behaviour, value, and engagement. NLP adds context by explaining why those differences might exist. When topics and segments are analysed together: issues can be prioritised by segment importance messaging can be tailored more accurately engagement strategies become evidence based rather than anecdotal This combination turns text analysis into a decision support tool rather than a descrip...

Introduction Identifying topics in customer text is useful, but it’s only half the story. What most analysts really need to understand is how those topics change over time . A theme appearing once is interesting. A theme growing steadily over several months is actionable. Without tracking trends, topic modelling risks becoming a one off exercise rather than a decision support tool. Customer feedback is dynamic. Concerns, expectations, and language evolve as products, services, and external conditions change. If topic analysis is static: emerging issues are spotted too late resolved problems continue to receive attention teams struggle to prioritise what matters now Tracking topic trends introduces a temporal lens that allows analysts to distinguish between noise and meaningful change. Adding time to text analysis Once topics have been assigned to text records, the problem becomes familiar to most analysts. You now have: a topic label a timestamp optional ...

 Introduction  CRM systems don’t just store numbers. They store words. Open ended survey responses, feedback comments, support notes, and free text fields often contain the most honest signals about customer experience. Yet these fields are usually underused because they feel messy, subjective, and hard to analyse at scale. Many analysts either ignore text entirely or rely on manual tagging, which doesn’t scale and introduces bias. The challenge is turning unstructured customer text into structured, analysable insight . Problem explanation When customer text is left untouched: important issues remain hidden in long comment fields patterns are detected too late or anecdotally decision making relies on summaries instead of evidence Basic NLP techniques allow analysts to surface themes, track changes in sentiment or concerns over time, and complement quantitative metrics with qualitative context. This doesn’t require advanced machine learning. It requires disci...

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...

Introduction Many dashboards fail quietly because the transformation logic is scattered, manual, or undocumented. Analysts often clean data “just enough” to make visuals work, without designing transformations that are structured, repeatable, and auditable. In BI environments, this leads to fragile reports. A small schema change breaks refreshes. A new column introduces inconsistencies. Over time, confidence in the numbers erodes. Power Query sits exactly at this fault line between raw data and analytics. Power Query is not just a prep tool. It is an ETL layer embedded inside BI workflows . When transformations are well designed: data refreshes become predictable logic is transparent and reviewable models remain stable as data evolves downstream DAX stays simple When they are not, analysts compensate with complex measures and manual fixes, increasing technical debt. Structured transformations reduce that debt. Intermediate technical explanation: how to think abou...