Best Practices & Use Cases
General Best Practices for Report Building
|
Best Practice |
Description |
| Use the Standard 3-Panel Build | Every report should be constructed using the three main panels: Organize Rows By, Select Table Data, and Filter By. |
| Group Data First | Always use the Organize Rows By panel to add the fields you want to group your data by; these will become your primary first columns. |
| Pick Specific Metrics | Use the Select Table Data panel to define the exact quantitative metrics you want to evaluate. |
| Always Apply Filters | Most reports require you to use Filter By to narrow the data down by a specific date range or condition to ensure relevance. |
| Preview Before Finalizing | Always click Generate Report to preview the configuration and data before completing the task. |
| Save for Automation | Click Save Report if you intend to reuse the exact layout or schedule it going forward. |
Report Use Cases & Configuration Best Practices
Sales Performance
|
Report |
What it tells you |
Organize Rows By |
Select Table Data |
Filter By |
|
Weekly Sales & Basket Trend |
How sales and average order value are moving day to day |
Sales Date |
Total Sales (After Tax), Total Bills, Average Order Value |
Sales Date = Last 7 Days |
|
City-Wise Sales Performance |
Which cities generate the most revenue and number of transactions |
Store City |
Total Sales (After Tax), Total Customers, Total Bills |
Sales Date = This Year |
|
Category-Wise Sales Contribution |
Which product categories drive sales and volume |
Product Category |
Total Sales (After Tax), Total Units Sold, Unique Items Purchased |
Sales Date = This Year |
|
Month-over-Month Sales Growth |
Whether sales are trending up or down month to month |
Sales Month |
Total Sales (After Tax), Sales Growth % (vs Previous Month) |
Sales Date = This Year |
|
Units & Value Per Customer Trend |
How basket size (UPT) and customer value (VPC) are trending |
Sales Month |
Units Per Transaction (UPT), Value Per Customer (VPC), Average Order Value |
Sales Date = This Year |
|
First-Time vs Repeat Visit Trend |
What share of footfall is new vs returning, by month |
Sales Month |
First-Time Visits, Repeat Visits, Total Visits |
Sales Date = This Year |
|
Store-Wise Returns Report |
Which stores have unusually high return volumes |
Store Name, Receipt Number |
Item Price, Quantity |
Quantity < 0; Sales Date = This Year |
Customer Insights
|
Report |
What it tells you |
Organize Rows By |
Select Table Data |
Filter By |
|
New vs Repeat Customers by Store (Monthly) |
Acquisition vs retention performance, store by store |
Store Name, Sales Month |
New Customers (Monthly), Repeat Customers (Monthly), Repeat Customer % (Monthly) |
Sales Date = This Year |
|
Top Spenders Leaderboard |
Your highest-value customers, for VIP treatment |
Customer Name, Mobile Number |
Total Sales (After Tax), Total Visits, Average Order Value |
Sales Date = This Year; sort by Total Sales, descending |
|
Dormant Customer Win-Back List |
Customers who haven't shopped in a while — good for a win-back campaign |
Customer Name, Mobile Number, Home Store, Last Purchase Date |
Total Visits, Total Sales (After Tax) |
Last Purchase Date before [your cutoff, e.g. 6 months ago] |
|
Customer Recency by Tier |
How recently each loyalty tier's customers have shopped |
Loyalty Tier, Customer Name, Mobile Number |
Days Since Last Visit, Total Visits |
Sales Date = This Year |
|
Customer Base by Location |
Where your customers are concentrated geographically |
Customer City, Customer State |
Total Customers |
Sales Date = This Year |
Loyalty Program
|
Report |
What it tells you |
Organize Rows By |
Select Table Data |
Filter By |
|
Tier-Wise Loyalty Balance & Enrolment |
Points balance and new enrolments, by tier |
Loyalty Tier |
Loyalty Points Balance, New Enrolments |
Enrolment Date = This Year |
|
Loyalty Redemption Rate by Store (Monthly) |
How actively each store's customers earn vs redeem points |
Store Name, Transaction Month |
Points Issued, Points Redeemed, Points Value Issued, Points Value Redeemed |
Transaction Date = This Year |
|
Tier Upgrade Report |
Which customers moved up a tier, and when |
Loyalty Tier, Customer Name, Mobile Number, Tier Upgrade Date |
(none needed) |
Tier Upgrade Date = This Year |
|
Points Activity by Type |
Points issued, redeemed, and reversed, broken down by type |
Transaction Type |
Points Issued, Points Redeemed, Points Reversed (Issued), Points Reversed (Redeemed) |
Transaction Date = This Month |
Campaigns & Communication
|
Report |
What it tells you |
Organize Rows By |
Select Table Data |
Filter By |
|
Campaign ROI by Channel |
Which channels (SMS/Email/WhatsApp) deliver the best return |
Channel |
Campaign Spend, Attributed Sales, ROAS, Attributed Customers |
Campaign Date = This Month |
|
Top Campaigns by Attributed Sales |
Which campaigns drove the most revenue |
Campaign Name |
Attributed Sales, Attributed Customers |
Campaign Date = This Year; sort by Attributed Sales, descending |
|
Store-Wise Campaign Attribution |
Which stores benefit most from your campaigns |
Attributed Store, Campaign Name |
Attributed Sales, Attributed Customers |
Campaign Date = This Year |
|
Click-Through Performance by Campaign |
Engagement (sent/delivered/read/clicked) across campaigns |
Campaign Name, Channel |
Sent, Delivered, Read, Clicked |
Campaign Date = This Year |
Advanced (if Customer Segmentation is enabled on your account)
|
Report |
What it tells you |
Organize Rows By |
Select Table Data |
Filter By |
|
Segment-Wise Customer List |
Your customers grouped by RFM segment (e.g. Champions, Loyal, At Risk) |
Customer Segment, Customer ID, Customer Name, Mobile Number |
Loyalty Points Balance |
(none needed) |
|
At-Risk High-Value Customers |
High-value customers your segmentation model flags as at risk of churning |
Customer ID, Customer Name, Mobile Number |
Total Sales (After Tax), Total Visits |
Customer Segment = [your "at risk" segment label]; Sales Date = This Year |