E-Commerce Conversion Tracking and Sales Performance Analysis

Course: Digital Marketing

Topic: E-Commerce Conversion Tracking and Sales Performance Analysis

Course Link: Digital Marketing Course

Demo Link: Register for a Course Demo

1. Introduction to E-Commerce Conversion Tracking

E-commerce conversion tracking is the process of measuring and recording important actions performed by visitors on an online store. These actions may include viewing products, adding items to a cart, beginning checkout, completing purchases, signing up for offers, or subscribing to a newsletter.

Sales performance analysis involves examining sales data to understand how well an online business is performing. It helps marketers identify profitable products, successful campaigns, customer purchasing patterns, and opportunities to increase revenue.

By combining conversion tracking with sales analysis, businesses can understand not only how many visitors arrive at their website but also how effectively those visitors become customers.

1.1 Why Conversion Tracking Is Important

• Measures how effectively website visitors become customers.
• Identifies marketing campaigns that generate sales.
• Helps calculate conversion rates and return on advertising spend.
• Reveals problems in product pages, shopping carts, and checkout processes.
• Supports data-driven decisions about advertising budgets.
• Helps improve customer experience and increase revenue.
• Measures the performance of different traffic sources and devices.

1.2 Example

An online clothing store receives 10,000 visitors in one month. Out of these visitors, 300 complete a purchase.

The store can use this information to compare performance across months, advertising campaigns, product categories, and customer segments.

2. Understanding E-Commerce Conversions

A conversion occurs when a visitor completes an action that supports a business objective. In e-commerce, the primary conversion is usually a completed purchase, but smaller actions can also be tracked to understand customer behavior.

2.1 Types of E-Commerce Conversions

2.2 Primary and Secondary Conversions

Primary conversion: The main action that directly supports the business goal, such as completing a purchase.

Secondary conversion: An action that indicates interest or supports a future sale, such as adding a product to a cart or subscribing to a newsletter.

Tracking both types helps businesses understand the complete customer journey rather than focusing only on final purchases.

3. Understanding the E-Commerce Customer Journey

The customer journey describes the stages a shopper moves through before and after making a purchase. Tracking these stages helps marketers identify where visitors lose interest or abandon the buying process.

3.1 Main Stages of the Customer Journey

1. Awareness: A potential customer discovers the store through search engines, social media, advertising, or referrals.
2. Product Discovery: The visitor browses product categories and individual product pages.
3. Consideration: The visitor compares prices, features, reviews, shipping charges, and alternatives.
4. Add to Cart: The visitor adds a selected product to the shopping cart.
5. Checkout: The visitor enters shipping, billing, and payment details.
6. Purchase: The transaction is completed successfully.
7. Retention: The business encourages customer satisfaction, repeat purchases, and loyalty.

3.2 Example of a Conversion Funnel

This funnel shows that only 300 of the 5,000 product-page viewers completed a purchase. The business can investigate product relevance, shipping costs, checkout complexity, payment failures, and other possible barriers.

4. Key E-Commerce Tracking Events

Tracking events records specific user interactions on an online store. In Google Analytics 4 (GA4), recommended e-commerce event names help organize shopping behavior and purchase activity consistently.

4.1 Why Event Tracking Matters

• Shows how users interact with products and store features.
• Reveals which steps have the highest abandonment rates.
• Helps compare product interest with completed sales.
• Supports campaign optimization and remarketing strategies.
• Provides data for building reports and conversion funnels.

5. Introduction to Google Analytics 4 for E-Commerce

Google Analytics 4 (GA4) is an analytics platform that measures website and app interactions through an event-based data model. It can help online retailers analyze user acquisition, product engagement, shopping behavior, purchases, and revenue.

5.1 Important GA4 Reports

5.2 Basic Setup Process

1. Create or access a GA4 property for the online store.
2. Configure a web data stream for the website.
3. Install the Google tag directly or use Google Tag Manager.
4. Implement the required e-commerce events on the store.
5. Send accurate event parameters, including product IDs, quantities, prices, and transaction IDs where applicable.
6. Test events using DebugView and Realtime reports.
7. Validate purchase data against actual order records.
8. Build reports to monitor conversions, revenue, and product performance.

Important: The exact implementation depends on the e-commerce platform and checkout architecture. A purchase event should represent a real completed transaction and should not fire repeatedly because a customer refreshes the confirmation page.

6. E-Commerce Event Parameters

Event parameters provide additional details about each interaction. Correct parameters make reports more useful and allow analysts to understand which products, quantities, and values are associated with events.

6.1 Example Purchase Event Structure

The following is an illustrative JavaScript data-layer example. Your developer must adapt it to the website's actual data layer and tracking setup.

dataLayer.push({ ecommerce: null });
dataLayer.push({
  event: "purchase",
  ecommerce: {
    transaction_id: "ORD-10025",
    currency: "INR",
    value: 2499.00,
    items: [
      {
        item_id: "SHOE-101",
        item_name: "Running Shoes",
        price: 2499.00,
        quantity: 1
      }
    ]
  }
});

This example illustrates the information a website might provide to a tag-management implementation. The data layer push alone does not send a GA4 event; a correctly configured tag or platform integration must process it.

7. Setting Up Purchase Conversion Tracking

Purchase tracking measures completed orders and their associated values. Reliable purchase tracking is essential for revenue reporting, advertising optimization, and return-on-investment calculations.

7.1 Implementation Steps

1. Identify the website's successful order confirmation or transaction-completion process.
2. Configure the purchase event to fire only after a successful order.
3. Include a unique transaction ID to help identify duplicate events.
4. Send the appropriate currency and order value.
5. Include item-level details where supported.
6. Configure the event through the website integration, Google tag, or Google Tag Manager.
7. Test the event with a test order or approved testing process.
8. Compare analytics results with the e-commerce platform's order records.

7.2 Common Purchase Tracking Problems

• Duplicate purchases: A confirmation page reload sends the same transaction again.
• Missing revenue: The purchase event is sent without the correct value or currency.
• Incorrect order totals: Tax, shipping, discounts, or refunds are treated inconsistently.
• Missing products: The items array is absent or incorrectly structured.
• Cross-domain issues: Users move between domains during checkout and session attribution is disrupted.
• Payment redirect issues: The event fires before the payment provider confirms success.

8. Conversion Rate and Its Calculation

Conversion rate measures the proportion of users or sessions that complete a defined conversion. The calculation depends on the denominator chosen and the definition of a conversion.

8.1 Formula

Conversion Rate = (Number of Conversions ÷ Total Eligible Users or Sessions) × 100

Use a consistent denominator when comparing results. GA4 reports can calculate session-based or user-based conversion metrics, and the interpretation differs between them.

8.2 Example

An online store records 8,000 sessions and 240 sessions with a purchase.

Session purchase conversion rate = (240 ÷ 8,000) × 100 = 3%

8.3 Ways to Improve Conversion Rate

• Improve page-loading speed and mobile usability.
• Use clear product images, descriptions, pricing, and availability information.
• Make shipping costs and delivery timelines visible.
• Reduce unnecessary checkout fields.
• Provide trusted and suitable payment options.
• Improve error messages and payment-failure recovery.
• Test page layouts, product information, and calls to action.
• Ensure advertising messages match the landing page.

9. Shopping Cart and Checkout Funnel Analysis

Funnel analysis evaluates how many users move through a sequence of steps. It helps identify where shoppers leave before completing a purchase.

9.1 Key Funnel Stages

1. View product.
2. Add product to cart.
3. View cart.
4. Begin checkout.
5. Enter shipping information.
6. Enter payment information.
7. Complete purchase.

9.2 Funnel Drop-Off Rate

Drop-Off Rate = ((Users at Previous Stage − Users at Next Stage) ÷ Users at Previous Stage) × 100

Suppose 1,000 users begin checkout and 700 continue to the next stage.

Drop-off rate = ((1,000 − 700) ÷ 1,000) × 100 = 30%

This means 30% of users who began checkout did not reach the next measured stage. Further analysis is needed to determine why.

9.3 Reasons for Checkout Abandonment

• Unexpected shipping or handling charges.
• Forced account creation.
• Complicated checkout forms.
• Limited payment methods.
• Payment processing errors.
• Slow pages or mobile usability problems.
• Unclear return, refund, or delivery policies.
• Technical errors or stock availability changes.

9.4 Improving Checkout Completion

Use a simple checkout flow, display the total cost early, offer guest checkout where appropriate, validate forms clearly, provide secure payment options, and monitor errors. Test changes against a baseline rather than assuming that every design change will improve sales.

10. Essential E-Commerce Sales Performance Metrics

Sales metrics help businesses measure revenue, order volume, customer spending, and operational performance. No single metric explains the entire business, so multiple indicators should be analyzed together.

11. Average Order Value (AOV)

Average Order Value measures the average amount spent per order during a selected period. It helps businesses understand whether customers are buying higher-value products or adding more items to their carts.

11.1 Formula

AOV = Total Sales Revenue ÷ Number of Orders

11.2 Example

If an online store generates ₹5,00,000 in sales revenue from 1,000 orders:

AOV = ₹5,00,000 ÷ 1,000 = ₹500

11.3 Strategies to Improve AOV

• Recommend relevant complementary products.
• Offer product bundles that provide genuine value.
• Use quantity discounts where margins support them.
• Offer free shipping above a carefully calculated order threshold.
• Display related accessories and upgrades.
• Make premium product options easy to compare.

AOV should be evaluated alongside gross margin, discounts, returns, and customer satisfaction. Increasing order value does not automatically increase profit.

12. Revenue Analysis and Profitability

Revenue analysis evaluates how much money the store generates from sales. Profitability analysis goes further by considering the costs required to generate that revenue.

12.1 Types of Revenue Measures

• Gross sales: Sales value before specified deductions, depending on the business's reporting convention.
• Net sales: Sales after relevant discounts, returns, and allowances, based on the accounting definition used.
• Collected revenue: Payments successfully received from customers.
• Gross profit: Net sales minus cost of goods sold.
• Contribution margin: Revenue remaining after relevant variable costs.

12.2 Example

This simplified example excludes other operating costs, advertising expenses, taxes, and additional accounting adjustments. Businesses should apply a consistent definition when comparing revenue reports with analytics data.

13. Customer Acquisition Cost (CAC) and Cost Per Acquisition (CPA)

Customer Acquisition Cost (CAC) estimates the cost of acquiring a new customer. Cost Per Acquisition (CPA) measures the cost of obtaining a defined conversion, which may be a purchase, lead, or another action. The terms can overlap, but the denominator and cost scope should always be specified.

13.1 CAC Formula

CAC = Relevant Acquisition Costs ÷ Number of New Customers Acquired

13.2 Example

If a store spends ₹60,000 on the acquisition activities included in its calculation and gains 300 new customers:

CAC = ₹60,000 ÷ 300 = ₹200 per new customer.

13.3 CPA Formula

CPA = Relevant Campaign Cost ÷ Number of Attributed Conversions

If a campaign costs ₹15,000 and generates 150 purchases, the purchase CPA is ₹100, provided that the cost and conversion reporting periods and attribution definitions align.

13.4 How to Use CAC and CPA

• Compare acquisition cost with customer value and profit margin.
• Review costs by channel, campaign, audience, and product category.
• Check whether reported conversions represent new customers or existing customers.
• Account for attribution windows and reporting delays.
• Avoid increasing budgets solely because a campaign has a low CPA.

14. Return on Ad Spend (ROAS) and Marketing ROI

ROAS measures attributed advertising revenue relative to advertising cost. Marketing ROI evaluates the return from marketing investments using a defined profit or benefit measure and the associated costs.

14.1 ROAS Formula

ROAS = Attributed Advertising Revenue ÷ Advertising Cost

14.2 Example

A paid advertising campaign costs ₹25,000 and is attributed ₹1,00,000 in purchase revenue.

ROAS = ₹1,00,000 ÷ ₹25,000 = 4

This represents a 4:1 ROAS, or ₹4 in attributed revenue for every ₹1 spent on advertising. It does not mean that the campaign earned ₹4 in profit per rupee spent.

14.3 Marketing ROI

Marketing ROI = ((Incremental Profit Attributable to Marketing − Marketing Investment) ÷ Marketing Investment) × 100

The calculation requires a defensible estimate of incremental profit and clearly defined costs. Businesses may use other ROI conventions, so the chosen method should be documented.

14.4 ROAS vs. ROI

15. Product-Level Performance Analysis

Product-level analysis identifies the items that generate strong demand, revenue, margin, and repeat purchases. It also reveals products that attract attention but rarely sell.

15.1 Metrics to Analyze

• Product page views.
• Add-to-cart rate.
• Purchase quantity.
• Revenue per product.
• Gross margin per product.
• Product return and refund rate.
• Stock availability and inventory turnover.
• Repeat purchase behavior.

15.2 Example Product Performance Table

Revenue alone does not establish which product is most profitable. Product costs, discounts, fulfilment costs, returns, and stock availability must also be considered.

16. Traffic Source and Marketing Channel Analysis

Traffic source analysis examines how visitors arrive at the online store and which sources contribute to purchases. It can help marketers allocate budgets and improve campaign targeting.

16.1 Metrics for Channel Comparison

• Users and sessions.
• Engagement rate.
• Purchase conversion rate.
• Purchases and attributed revenue.
• Advertising cost and CPA.
• ROAS.
• New customer acquisition.
• Assisted conversions, where supported by the measurement method.

A channel with high traffic but few purchases may be attracting poorly matched visitors, sending users to weak landing pages, or experiencing measurement issues. Investigate the cause before cutting the budget.

17. UTM Parameters and Campaign Tracking

UTM parameters are tags added to campaign URLs to identify the source, medium, campaign, and other details of incoming traffic. Consistent tagging helps analysts distinguish marketing activities in analytics reports.

17.1 Common UTM Parameters

17.2 Example Tagged URL

https://www.example.com/products/shoes?utm_source=newsletter&utm_medium=email&utm_campaign=diwali_sale&utm_content=banner_a

Use a consistent naming convention, avoid unnecessary personal information in URLs, and test tagged links before distributing them. For advertising platforms that support automatic tagging, follow the platform's recommended setup and avoid conflicting tracking configurations.

18. Attribution and Conversion Credit

Attribution is the method used to assign credit for a conversion to marketing touchpoints. A customer may discover a product through social media, return through organic search, and purchase after receiving an email. Attribution models can distribute credit differently across these interactions.

18.1 Common Attribution Approaches

• Last-click: Gives credit to the final eligible click before conversion under the model's rules.
• First-click: Gives credit to the first eligible interaction.
• Linear: Distributes credit evenly across eligible touchpoints.
• Position-based: Gives greater weight to selected positions in the journey.
• Data-driven: Uses available data and model-specific methods to distribute conversion credit.

Available models and reporting behavior vary by analytics and advertising platform. Attribution is not proof that a channel caused a purchase. Where possible, experiments such as controlled tests or holdout studies can help estimate incremental impact.

19. Customer Lifetime Value and Repeat Purchases

Customer Lifetime Value (CLV or LTV) estimates the value a customer generates over the relationship with a business. Depending on the method, it may refer to revenue or profit contribution, so the definition must be stated clearly.

19.1 Simple Revenue-Based Estimate

Estimated Customer Revenue Value = Average Order Value × Purchase Frequency × Customer Relationship Duration

Suppose a customer spends an average of ₹1,000 per order, places four orders per year, and remains active for two years.

Estimated revenue value = ₹1,000 × 4 × 2 = ₹8,000.

This is a simplified revenue estimate, not profit-based CLV. A more complete model may include gross margin, retention probability, servicing costs, acquisition costs, and discounting.

19.2 Ways to Encourage Repeat Purchases

• Send relevant post-purchase email campaigns.
• Provide useful product-care and usage guidance.
• Offer loyalty benefits that support profitable behavior.
• Recommend products based on legitimate purchase history.
• Make reordering easy for frequently purchased products.
• Improve delivery, support, and returns experiences.

20. Building an E-Commerce Performance Dashboard

A dashboard presents important business metrics in one place. It should help stakeholders quickly understand sales performance, identify problems, and choose the next action.

20.1 Recommended Dashboard Sections

1. Executive overview: Revenue, orders, conversion rate, and AOV.
2. Acquisition performance: Users, sessions, channels, campaigns, and acquisition costs.
3. Conversion funnel: Product views, cart additions, checkout starts, and purchases.
4. Product performance: Best-selling products, product revenue, units sold, and margin.
5. Customer analysis: New customers, returning customers, and repeat purchase metrics.
6. Marketing efficiency: Ad spend, CPA, ROAS, and ROI under documented definitions.
7. Returns and refunds: Refund trends and their effect on net sales.

20.2 Useful Tools

Ensure that the dashboard uses consistent date ranges, currency settings, time zones, filters, and metric definitions. Different platforms may report different conversion totals because of attribution rules, consent settings, reporting windows, and data processing.

21. Data Quality, Privacy, and Consent

Accurate analysis depends on reliable data collection. Incorrect event implementation, inconsistent definitions, duplicate events, or incomplete consent handling can lead to misleading reports.

21.1 Data Quality Checklist

• Confirm that purchase events fire only for completed transactions.
• Check that transaction IDs are unique and duplicate handling is implemented.
• Validate currency, order value, quantities, and product identifiers.
• Compare analytics orders with the e-commerce platform's order records.
• Check internal traffic and test transactions where appropriate.
• Review cross-domain tracking if checkout uses another domain.
• Document the treatment of discounts, shipping, taxes, cancellations, and refunds.
• Monitor tracking after website updates and checkout changes.

21.2 Privacy Best Practices

• Follow applicable privacy laws and platform policies.
• Implement consent mechanisms where required.
• Respect user consent choices in analytics and advertising implementations.
• Do not send names, email addresses, phone numbers, payment card details, or other prohibited personal information to analytics event parameters or URLs.
• Limit access to reports and customer data to authorized personnel.
• Use aggregated reporting where possible and retain data only as needed.

22. Troubleshooting Common Tracking and Reporting Issues

23. Step-by-Step E-Commerce Sales Analysis Workflow

Follow a consistent process when evaluating the performance of an online store.

1. Define the business goal: Decide whether the analysis focuses on revenue growth, profitability, purchase conversion, customer acquisition, or retention.
2. Choose the reporting period: Select a clear date range and comparison period.
3. Validate the data: Check purchase events, revenue, currency, order IDs, and refunds.
4. Review the overall results: Analyze orders, revenue, conversion rate, AOV, and margin.
5. Segment the data: Compare channels, campaigns, products, devices, and customer groups.
6. Analyze the funnel: Identify the stages with the largest drop-offs.
7. Investigate causes: Review user experience, page speed, pricing, inventory, payment errors, and campaign relevance.
8. Recommend improvements: Prioritize actions based on potential impact, cost, and implementation effort.
9. Run controlled tests: Where feasible, compare proposed changes with a valid baseline or control group.
10. Monitor results: Evaluate outcomes after sufficient data has accumulated.
11. Document findings: Record the evidence, action taken, outcome, and next steps.

24. Practical Project: Analyze an Online Store

Project Objective: Evaluate the performance of a fictional online store and recommend actions to improve sales.

24.1 Sample Dataset

24.2 Calculate Key Metrics

Current Month Conversion Rate:

(600 ÷ 20,000) × 100 = 3%

Previous Month Conversion Rate:

(450 ÷ 18,000) × 100 = 2.5%

Current Month AOV:

₹6,00,000 ÷ 600 = ₹1,000

Previous Month AOV:

₹4,05,000 ÷ 450 = ₹900

Current Month Revenue-to-Ad-Spend Ratio:

₹6,00,000 ÷ ₹1,00,000 = 6:1

This ratio is not necessarily campaign ROAS because the sample revenue may include sales from organic, direct, email, and other channels.

Current Month Acquisition Cost per New Customer:

If the entire ₹1,00,000 advertising spend is used as the acquisition cost numerator, the simplified ratio is ₹1,00,000 ÷ 400 = ₹250 per new customer. A true CAC calculation may need to include other acquisition costs and confirm that the new customers were acquired through the relevant activities.

24.3 Interpretation

• Sessions increased from 18,000 to 20,000.
• Orders increased from 450 to 600.
• Conversion rate increased from 2.5% to 3%.
• AOV increased from ₹900 to ₹1,000.
• Revenue increased from ₹4,05,000 to ₹6,00,000.
• Advertising spend increased, so campaign-level cost and incremental profit should be investigated before assuming that the additional spend was efficient.

24.4 Recommended Actions

1. Compare conversion rates by device to identify mobile or desktop friction.
2. Analyze product and checkout funnels to locate the largest drop-off.
3. Review campaign-level CPA and attributed ROAS.
4. Compare product revenue with gross margin and refund rates.
5. Check whether revenue growth came from new customers, returning customers, or higher order values.
6. Test improvements to checkout, product pages, and relevant product recommendations.

25. Best Practices for E-Commerce Conversion Tracking

• Define conversion events before implementing tracking.
• Use recommended event names and parameters where applicable.
• Keep event naming and campaign tagging consistent.
• Test tracking before and after site releases.
• Prevent duplicate purchase events.
• Validate analytics data against reliable order records.
• Use one documented definition for each important KPI.
• Separate revenue, profit, attributed revenue, and cash collected.
• Segment reports to uncover differences between customer groups and channels.
• Use experiments to validate optimization decisions.
• Respect user privacy and applicable consent requirements.
• Record assumptions, limitations, and changes in the measurement setup.

26. Common Mistakes to Avoid

• Counting every button click as a successful conversion.
• Firing purchase events before the order is completed.
• Ignoring duplicate transaction events.
• Comparing reports with different date ranges or currencies.
• Assuming attributed revenue equals incremental revenue.
• Evaluating advertising only by clicks or impressions.
• Confusing ROAS with profit or ROI.
• Ignoring refunds, cancellations, and product costs.
• Changing campaign budgets before checking data quality.
• Collecting unnecessary personal information in analytics tools.

27. Interview Questions and Answers

Q1. What is e-commerce conversion tracking?

E-commerce conversion tracking measures actions such as product views, cart additions, checkout starts, and completed purchases to understand user behavior and sales effectiveness.

Q2. What is a purchase event in GA4?

The purchase event records a completed transaction and can include parameters such as transaction ID, currency, value, and item details.

Q3. What is conversion rate?

Conversion rate is the percentage of eligible users or sessions that complete a defined conversion. The denominator should be clearly specified.

Q4. What is an e-commerce funnel?

An e-commerce funnel represents the steps users take from product discovery to purchase. Funnel analysis helps identify where users abandon the journey.

Q5. What is Average Order Value?

AOV is the average sales value per order. It is generally calculated by dividing the defined sales revenue by the number of orders.

Q6. What is the difference between CAC and CPA?

CAC estimates the cost of acquiring a new customer, while CPA measures the cost of a defined conversion. Their scope depends on which costs and conversions are included.

Q7. What is ROAS?

ROAS is attributed advertising revenue divided by advertising cost. It measures revenue relative to ad spend, not profit by itself.

Q8. Why might GA4 revenue differ from an e-commerce platform?

Differences may arise from tracking gaps, consent settings, duplicate events, refunds, attribution models, time zones, currencies, and different revenue definitions.

Q9. What are UTM parameters?

UTM parameters are campaign tags added to URLs to help identify traffic sources, mediums, campaigns, and creative variations.

Q10. How can cart abandonment be reduced?

Businesses can investigate unexpected charges, simplify checkout, improve mobile usability, offer appropriate payment options, and resolve technical errors.

Q11. How do you validate purchase tracking?

Test completed orders, inspect event parameters, check transaction IDs, verify event firing behavior, and compare analytics data with order records.

Q12. Why is product-level analysis important?

It helps identify products that contribute to sales and profit, reveals low-performing products, and supports decisions about pricing, promotion, stock, and merchandising.

28. Quick Revision Checklist

• Understand the difference between primary and secondary conversions.
• Know the customer journey from product discovery to repeat purchase.
• Understand the purpose of GA4 e-commerce events.
• Know the key purchase parameters: transaction ID, currency, value, and items.
• Calculate conversion rate, AOV, CAC, CPA, and ROAS.
• Analyze cart and checkout funnel drop-offs.
• Compare sales performance across products, channels, and campaigns.
• Use consistent UTM parameters for campaign tracking.
• Understand attribution limitations and the difference between revenue and profit.
• Validate tracking and respect privacy requirements.
• Build a dashboard that supports clear business decisions.

29. Conclusion

E-Commerce Conversion Tracking and Sales Performance Analysis help businesses understand how visitors interact with an online store, which activities lead to purchases, and how effectively marketing investment contributes to business results. By tracking the customer journey, implementing accurate purchase events, monitoring sales metrics, and analyzing products and channels, marketers can identify opportunities to improve conversion rates and revenue.

Successful analysis depends on reliable data, consistent KPI definitions, privacy-conscious implementation, and careful interpretation. Businesses should evaluate profitability and incremental impact alongside reported revenue and conversions, then test improvements and monitor the results over time.

Learn More: Explore the JustAcademy Digital Marketing Course and register for a course demo.

Additional Learning Resources:

• Google Analytics 4 E-Commerce Measurement Documentation
• Google Analytics Help Center
• Google Tag Manager Help Center