Customer-Insights-and-Marketing-ROI-Analysis-Using-SQL-Server-and-Power-BI/Analysis Notes.txt at main · MNitin-Reddy/Customer-Insights-and-Marketing-ROI-Analysis-Using-SQL-Server-and-Power-BI · GitHub

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### **1. Data Cleaning and Preprocessing**
Before diving into the analysis, you'll want to ensure the data is clean and ready for deeper insights.

#### **Questions to Answer:**
- **Are there any missing values in the dataset?**
  - Which columns have null values? How will you handle them? (e.g., imputation, removal)

- **Are there any duplicates in the dataset?**
  - Are there duplicate rows that should be removed to ensure the integrity of the analysis?

- **Are there any outliers or anomalies in key columns?**
  - Which values are outside the expected range (e.g., negative quantities or negative revenue)?

- **Are there any inconsistencies in categorical variables (e.g., spelling errors, mixed case)?**
  - How will you standardize the text data in columns like `name` or `channel`?

- **Are the data types for each column appropriate (e.g., date columns formatted as dates)?**
  - Do we need to convert columns like `occurred_at` to a proper datetime format?

- **What are the statistics for numeric columns (e.g., mean, standard deviation, min/max)?**
  - This will help identify any unexpected or inconsistent values.

---

### **2. Customer Analysis**
This segment focuses on gaining insights about the accounts or customers in the dataset.

#### **Questions to Answer:**
- **What are the key customer demographics (e.g., company size, location)?**
  - Are there any observable patterns in customer locations (e.g., geographic clustering)?

- **Which accounts are generating the most revenue (both in terms of number of orders and total spend)?**
  - Can you identify top-tier customers based on total revenue?

- **Which accounts have the highest number of orders or the largest quantities ordered?**
  - Are there any accounts with frequent purchases but relatively low spend? What might this indicate?

- **How diverse is the customer base in terms of the number of unique companies or industries?**
  - What proportion of revenue comes from a small subset of customers versus a broad group?

- **Are there specific accounts or industries that tend to order more gloss/standard/poster paper?**
  - Can you identify patterns in product type preferences among accounts?

- **What is the retention rate of customers?**
  - Are there returning customers, and what is the frequency of their orders?

---

### **3. Sales Performance**
This section focuses on the performance of sales, both at the individual order level and at the account level.

#### **Questions to Answer:**
- **What is the total revenue generated by orders over time?**
  - What are the trends in revenue over days, months, or years?

- **Which product types (e.g., standard, gloss, poster) are contributing the most to revenue?**
  - How does revenue break down between these categories?

- **What is the average order size for different types of customers (e.g., by region, company size)?**
  - Are there differences in order values based on customer characteristics?

- **Which sales representatives or regions are bringing in the most revenue?**
  - Is there significant variance in sales performance based on location or individual sales reps?

- **What is the distribution of total order amounts?**
  - Do you see a skewed distribution, or is it relatively uniform?

- **How does the revenue distribution look across various order types?**
  - What proportion of sales come from standard paper vs. gloss or poster paper?

- **Are there any sales performance trends (seasonality, spikes, or dips)?**
  - Do certain months, seasons, or days of the week see higher sales?

---

### **4. Marketing Effectiveness**
Here, you’ll explore how marketing campaigns and channels are performing in terms of customer interaction.

#### **Questions to Answer:**
- **Which marketing channels (e.g., organic, adwords) are generating the most customer interactions?**
  - How do different channels compare in terms of engagement?

- **What is the conversion rate from marketing touchpoints (e.g., how many web events lead to actual orders)?**
  - Can you track which marketing channels lead to actual revenue generation?

- **How do customers behave after being contacted via marketing channels (e.g., purchase frequency, order size)?**
  - Does a contact via organic search lead to larger or more frequent orders?

- **What is the retention or churn rate for customers contacted via marketing channels?**
  - Are marketing channels leading to long-term customer relationships or just one-time purchases?

- **Which types of companies (or regions) are more likely to be influenced by a specific marketing channel?**
  - Do you notice that certain channels are more effective for particular customer segments or industries?

---

### **5. Geographic Insight**
This section focuses on analyzing sales and customer behavior based on geographic factors.

#### **Questions to Answer:**
- **Where are the highest-revenue-generating customers located?**
  - Are there regions or cities where sales are particularly high?

- **What is the geographic distribution of customer orders (e.g., are certain regions more active than others)?**
  - Can you visualize or identify trends in customer orders based on region?

- **How do regional sales compare to other metrics like product type preferences or sales rep performance?**
  - Are some regions ordering more of one product type than others? Is this due to geographic preferences?

- **Are there any underperforming regions that require further attention from sales reps or marketing teams?**
  - Can you identify regions with low sales performance but high potential based on customer density?

- **How does customer retention vary by location?**
  - Are some regions seeing higher retention rates than others?

- **Is there any significant geographic variation in pricing or discounts offered?**
  - Do sales in certain regions show more variability in order sizes or types?

---

### **6. Bonus Insights**
These are additional questions that cut across different dimensions of the data and offer deeper insights.

#### **Questions to Answer:**
- **How do customer order patterns change over time?**
  - Do you notice any seasonal trends, growth over time, or any long-term shifts?

- **What are the most common product combinations ordered?**
  - Are there specific combinations of standard, gloss, and poster paper that are frequently ordered together?

- **What factors seem to influence high-value or low-value orders?**
  - Do certain factors (e.g., product types, customer types, marketing channels) correlate with larger or smaller orders?

- **Can we predict customer order behavior based on historical data?**
  - Can we build a predictive model to estimate future orders based on past trends?

---

### **Key Deliverables for the Project:**
- **Data Cleaning Report**: A detailed explanation of how missing values, duplicates, and inconsistencies were handled.
- **Customer Insights Dashboard**: Visualizations showing key metrics like customer distribution, revenue per account, and order types.
- **Sales Trends**: Line charts, bar charts, or histograms showing sales over time, top products, and order sizes.
- **Marketing Effectiveness Dashboard**: Metrics like conversion rates, channel performance, and customer engagement post-marketing touch.
- **Geographic Analysis**: Heatmaps or geographic visualizations of sales by region.
- **Predictive Model (optional)**: A model predicting future sales or customer behavior based on historical data.