AtData Email Intelligence Integration for Demographic Insights

1 Introduction
2 Prerequisites
3 Configuration Steps
4 Analyzing AtData Results
- 4.1 1. Analyze with BigQuery
  - 4.1.1 Example SQL Queries
- 4.2 2. Explore with AI Report Builder
5 Privacy and Data Handling Considerations
6 Sample Dashboard Design: Demographic Insights Dashboard
7 FAQ
8 Next Steps

Introduction

The AtData Email Intelligence Integration enables UltraCart merchants to enrich customer profiles with valuable demographic data such as age, gender, and income range.
This enrichment supports smarter segmentation, more personalized upsells, and deeper customer analytics across UltraCart’s ecosystem.

When paired with the Data Warehouse (BigQuery) and AI-Powered Report Builder, AtData insights can be visualized in dashboards that reveal patterns in customer demographics, behavior, and purchasing trends.

Tip: Use AtData demographic enrichment to identify who your most profitable customers are, tailor offers to their preferences, and optimize marketing ROI.

Prerequisites

AtData InstantData account (Sign up here)
API Key from AtData
UltraCart merchant or admin access
(Optional) BigQuery Data Warehouse and AI Report Builder enabled

Configuration Steps

1. Create an AtData Account and Generate an API Key

Go to InstantData.
Create or log in to your AtData account.
Generate an API Key.
Choose which demographic fields you want (e.g., Age, Gender, Income).

Warning: AtData operates on a per-lookup pricing model. Configure only the attributes you plan to use to manage costs.

2. Connect AtData to UltraCart

Log in to your UltraCart Merchant Account.
Navigate to:
Configuration → Integrations → AtData Email Intelligence
Paste your API Key into the field.
Select the demographic attributes to query.
Click Save.

UltraCart will automatically enrich customer records as new orders are created or customers are added.

3. Data Refresh Cycle

UltraCart queries AtData once per year per unique email address.
Returning customers are automatically re-queried after 12 months.
Retrieved data is stored in your BigQuery dataset towerdata_email_intelligence.

Note: Only the demographic attributes you select will appear in the dataset.

4. Using Demographic Data in StoreFront Upsells

You can use demographic triggers to target upsell offers.

Go to StoreFront → Upsells.
Create or edit an Upsell Path.
In Advanced Conditions, set filters such as:
- age > 25 AND gender = "Female"
- age BETWEEN 35 AND 54 AND gender = "Male"

Examples:

Offer “Luxury Skin Care Kits” to females aged 25–40.
Suggest “Performance Nutrition Packs” to males aged 30–45.

Analyzing AtData Results

1. Analyze with BigQuery

Data from AtData populates the towerdata_email_intelligence table, which can be joined with uc_orders and uc_customers tables for insight-rich reporting.

Example SQL Queries

Average Order Value (AOV) by Gender

SELECT 
  td.gender,
  ROUND(AVG(o.summary.total.value), 2) AS avg_order_value
FROM `my-project.my_dataset.uc_orders` o
JOIN `my-project.my_dataset.towerdata_email_intelligence` td
  ON o.billing.email_hash = td.email_hash
WHERE o.payment.payment_dts IS NOT NULL
GROUP BY td.gender
ORDER BY avg_order_value DESC;

Customer Distribution by Age Range

SELECT
  CASE 
    WHEN td.age BETWEEN 18 AND 24 THEN '18–24'
    WHEN td.age BETWEEN 25 AND 34 THEN '25–34'
    WHEN td.age BETWEEN 35 AND 44 THEN '35–44'
    WHEN td.age BETWEEN 45 AND 54 THEN '45–54'
    ELSE '55+'
  END AS age_range,
  COUNT(DISTINCT o.billing.email_hash) AS customer_count
FROM `my-project.my_dataset.towerdata_email_intelligence` td
JOIN `my-project.my_dataset.uc_orders` o
  ON o.billing.email_hash = td.email_hash
WHERE o.payment.payment_dts IS NOT NULL
GROUP BY age_range
ORDER BY customer_count DESC;

Revenue by Gender and Age Group

SELECT
  td.gender,
  CASE 
    WHEN td.age BETWEEN 18 AND 34 THEN '18–34'
    WHEN td.age BETWEEN 35 AND 54 THEN '35–54'
    ELSE '55+'
  END AS age_group,
  SUM(o.summary.total.value) AS total_revenue
FROM `my-project.my_dataset.towerdata_email_intelligence` td
JOIN `my-project.my_dataset.uc_orders` o
  ON o.billing.email_hash = td.email_hash
GROUP BY td.gender, age_group
ORDER BY total_revenue DESC;

2. Explore with AI Report Builder

The AI Report Builder allows you to explore demographic data in natural language.

Objective	Example AI Prompt

Objective	Example AI Prompt
Compare spend by gender	“Show average order value by gender.”
Identify top-performing age groups	“Which age group generates the highest revenue?”
Track upsell success	“List top 10 upsells purchased by females aged 25–40.”
Examine repeat purchase patterns	“Compare repeat purchase rate by gender.”
Combine demographics with campaigns	“Show average order value by age and ad source.”

Tip: Export AI-generated charts to Dashboards for ongoing monitoring.
See AI-Powered Report Builder.

Privacy and Data Handling Considerations

UltraCart’s integration with AtData follows strict data protection standards.

Customer Consent: Only emails collected through legitimate opt-in processes are sent to AtData.
Data Minimization: Only selected demographic fields are queried.
Secure Storage: Data is stored in Google BigQuery, protected by encryption and access control.
Access Control: Only authorized users can view or query demographic data.
Compliance: Integration aligns with GDPR, CCPA, and CAN-SPAM standards.

Note: Merchants should ensure their own privacy policies include demographic enrichment disclosures.

Sample Dashboard Design: Demographic Insights Dashboard

UltraCart’s AI-Powered Report Dashboards allow you to visualize and share AtData-driven KPIs across your organization.

Key Metrics to Include

KPI	Description	Suggested Chart

KPI	Description	Suggested Chart
Customer Distribution by Gender	Share of customers by gender	Donut Chart
Revenue by Age Group	Total sales by age bracket	Horizontal Bar Chart
Average Order Value (AOV) by Gender	Comparison of spending by gender	Vertical Bar Chart
Top Categories by Gender	Product category preferences segmented by gender	Stacked Bar Chart
Repeat Purchase Rate by Age Range	Loyalty across demographics	Line Chart
Demographic Mix Over Time	Age/gender composition over months	Multi-Series Line Chart

Example Dashboard Layout Diagram

Recommended layout for the Demographic Insights Dashboard (4x2 grid):

 ------------------------------------------------------------
| [1] Customer Distribution by Gender | [2] Revenue by Age Group          |
| (Donut Chart)                        | (Horizontal Bar)                  |
 ------------------------------------------------------------
| [3] AOV by Gender                    | [4] Repeat Purchase Rate by Age   |
| (Vertical Bar)                       | (Line Chart)                      |
 ------------------------------------------------------------
| [5] Top Categories by Gender (Stacked Bar, Wide Tile)                      |
 ------------------------------------------------------------
| [6] Demographic Mix Over Time (Multi-Series Line Chart, Full Width)        |
 ------------------------------------------------------------

Placement Guidance:

Row 1: High-level demographic composition
Row 2: Key behavioral KPIs
Row 3: Category and trend analysis

Tip: Maintain color consistency (e.g., blue for Male, pink for Female, gray for Unknown) for quick visual scanning.
Align your date range (e.g., “Last 30 Days” or “Quarter to Date”) across all tiles for accurate comparisons.

For complete setup steps, see AI-Powered Report Dashboards.

KPI Calculation Reference Table

Use this table to standardize how your demographic dashboards calculate key metrics in BigQuery or AI Report Builder.

KPI	Formula (SQL Expression)	Description / Notes

KPI	Formula (SQL Expression)	Description / Notes
Average Order Value (AOV)	`SUM(o.summary.total.value) / COUNT(DISTINCT o.order_id)`	Measures average spend per order.
Revenue per Customer	`SUM(o.summary.total.value) / COUNT(DISTINCT o.billing.email_hash)`	Indicates total revenue divided by unique customers.
Repeat Purchase Rate	`(COUNT(DISTINCT CASE WHEN order_count > 1 THEN o.billing.email_hash END) / COUNT(DISTINCT o.billing.email_hash)) * 100`	Percentage of customers who made more than one purchase.
New vs Returning Customers	`CASE WHEN order_rank = 1 THEN 'New' ELSE 'Returning' END`	Categorizes customers by purchase sequence.
Revenue by Age Group	`SUM(o.summary.total.value)` grouped by `td.age` ranges	Links revenue to demographic segments.
Customer Distribution by Gender	`COUNT(DISTINCT td.email_hash)` grouped by `td.gender`	Proportion of customer base by gender.
LTV (Customer Lifetime Value)	`SUM(o.summary.total.value)` per `td.email_hash`	Aggregates total lifetime revenue per customer.
Conversion Rate by Gender	`(COUNT(DISTINCT o.order_id) / COUNT(DISTINCT s.session_id)) * 100` grouped by `td.gender`	Measures conversion from session to order per gender.
AOV Growth (MoM)	`(current_month_aov - previous_month_aov) / previous_month_aov * 100`	Month-over-month growth rate for AOV.

Tip: Save standardized SQL expressions in your report templates to maintain consistency across all dashboards.

FAQ

Q: How often does UltraCart query AtData?
A: Once per year per email. The system re-queries automatically when a customer repurchases after 12 months.

Q: Can I backfill data for existing customers?
A: Yes. Purchase bulk lookup credits from AtData and contact UltraCart Support.

Q: Where is demographic data stored?
A: In your BigQuery dataset (towerdata_email_intelligence).

Q: Can I use demographics in StoreFront Communications?
A: Yes, demographic fields can be used for audience segmentation and conditional messaging.

Q: Can dashboards be shared automatically?
A: Yes. Schedule dashboards for automatic delivery via email in PDF format.

Next Steps

Review Data Warehouse (BigQuery) for data modeling and joins.
Build custom reports with AI-Powered Report Builder.
Assemble visual dashboards using AI-Powered Report Dashboards.
Apply demographic insights to your StoreFront Upsells and Communications strategy.

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