Zoho Analytics Data Pipeline makes it easy to build a complete Customer 360 view, even when your data is scattered across multiple tables and formats. Imagine you’re the data analyst at a retail company—your team wants a “Customer 360” dashboard where you can track sales, loyalty points, and payment behaviors in one place. But the raw data lives everywhere:
Sales data comes in two separate tables (H1 and H2).
Customer details sit in another table.
Loyalty points are tracked separately.
Quarterly sales are spread across four columns (Q1–Q4).
Manually cleaning, combining, and restructuring this data would take hours. With Zoho Analytics Data Pipeline, you can streamline these tasks step by step and generate a unified Customer 360 dataset effortlessly.
Step 1: Append Sales Data
You start by merging the H1 and H2 sales tables into one annual sales dataset.
In the pipeline, drag in Sales Data H1.
Use Append to stack Sales Data H2.
Result: one unified table showing sales for the entire year.
Step 2: Join Customer Info and Loyalty Points
Next, you need to connect your customer profiles with their loyalty points.
Drag in the Customer Information table.
Use Join with the Customer Loyalty Points table.
Apply an Outer Join to keep all customers (even if some don’t have points yet).
Rename duplicate columns if needed.
Result: a richer customer dataset that combines personal info and loyalty behavior.
Step 3: Unpivot Quarterly Sales
The product sales table lists Q1–Q4 in separate columns, which makes it hard to filter or compare.
Select the Product Sales table.
Use Unpivot on Q1, Q2, Q3, and Q4 columns.
Create two new columns: “Quarter” and “Sales.”
Result: sales are normalized into rows, making it easy to calculate totals or trends per quarter.
Step 4: Pivot to Analyse Payment Methods
Finally, you want to see spending patterns by payment type.
Take your unified Customer Orders table.
Apply Pivot with:
Columns → Payment Method
Rows → Customer ID
Data → Total Amount
Result: a table showing exactly how much each customer spends by credit card, cash, wallet, etc.
Step 5: Finalize and Run the Pipeline
Right-click the final stage → Create as Output Table.
Mark the pipeline as Ready.
Run it manually to test, then set up a Schedule to refresh automatically (e.g., daily at midnight).
Every run creates a Job, so you can monitor row counts, duration, and status in the Overview tab.
Conclusion
With this pipeline in place, you’ve transformed scattered raw data into a single, trusted Customer 360 dataset. From there, you can easily build dashboards showing:
Annual sales performance.
Loyalty engagement levels.
Quarterly product trends.
Payment behavior patterns.
