Advanced Email Analytics

Email analytics provides a wealth of data to optimize campaigns, segment audiences, and drive conversions. This in-depth guide explores advanced email analytics techniques, including custom metrics, predictive modeling, and reporting best practices. By leveraging these strategies, marketers can gain deeper insights, anticipate subscriber behavior, and maximize email ROI.

Defining Custom Email Metrics

While standard metrics like open rate, click rate, and unsubscribes are important, defining custom metrics allows for more targeted analysis. Some examples of valuable custom email metrics include:

• Conversion Rate by Segment - Measuring conversions for specific audience segments to identify high-value groups.
• Revenue per Subscriber - Calculating the average revenue generated per subscriber to assess list quality and subscriber lifetime value.
• Engagement Score - Combining opens, clicks, and website activity into a single subscriber engagement metric.

The diagram below illustrates how custom metrics can be derived from raw email and website data:

Implementing Custom Metrics in Email Analytics Platforms

Most email service providers (ESPs) and analytics platforms allow for creating custom metrics. The general process involves:

1. Identifying the data points needed for the metric (e.g. opens, clicks, purchases)
2. Using the platform's API or integration tools to import external data if required
3. Defining the metric calculation using the available data
4. Creating a widget or report to track the metric over time

// Example code for calculating Revenue per Subscriber 
// Using the SendGrid API

const SENDGRID_API_KEY = 'your_api_key';
const EMAIL_CAMPAIGN_ID = 'your_campaign_id';

const sgMail = require('@sendgrid/mail');
sgMail.setApiKey(SENDGRID_API_KEY);

async function calculateRevenuePerSubscriber() {
  try {
    // Get total revenue from campaign    
    const campaignRevenue = await getTotalCampaignRevenue(EMAIL_CAMPAIGN_ID);
    
    // Get total delivered emails for campaign
    const [response] = await sgMail.send(request);
    const deliveredEmails = response.data.stats.metrics.delivered;

    // Calculate and return revenue per subscriber
    return campaignRevenue / deliveredEmails;
  } catch(error) {
    console.error(error);
  }
}

Predictive Analytics for Email Marketing

Predictive analytics uses data mining, machine learning and statistical modeling to forecast future subscriber behaviors and outcomes. Some key applications of predictive analytics in email include:

The following diagram shows a high-level predictive analytics workflow:

Predictive Analytics Tools and Platforms

Implementing predictive analytics requires specialized tools to process large datasets and build machine learning models. Some popular options include:

Best Practices for Building Predictive Models

When developing predictive models for email marketing, consider these best practices:

• Ensure a large enough dataset to train the model, typically at least 10,000 subscribers
• Select predictor variables with strong correlations to the outcome metric
• Use a separate holdout dataset to validate model accuracy
• Retrain models periodically to capture changing subscriber behaviors over time

Cohort Analysis for Email Campaigns

Cohort analysis groups subscribers based on common characteristics or acquisition dates to compare engagement and conversion metrics over time. It helps answer questions like:

• Do subscribers acquired from certain sources have higher lifetime value?
• How do open and click rates change as subscriber tenure increases?
• Which acquisition cohorts are most likely to churn within 3 months?

This diagram visualizes typical cohort analysis output comparing revenue per subscriber across monthly cohorts:

Setting Up Cohort Analysis

To implement cohort analysis for email marketing:

1. Define the cohort parameters, such as acquisition date or subscriber attribute
2. Select the engagement metrics to compare across cohorts
3. Choose the time intervals for analysis (e.g. days, weeks, months)
4. Segment email results by cohort and aggregate engagement metrics for each time interval
5. Visualize results in a cohort chart or table

Actionable Insights from Cohort Analysis

Cohort analysis often uncovers actionable insights to optimize email strategy, such as:

Email Marketing Attribution Models

Attribution modeling allocates conversion credit to individual subscriber touchpoints leading up to a purchase or desired action. With proper attribution, marketers can quantify email's impact on conversions and revenue. Common email attribution models include:

This diagram demonstrates how conversion credit is allocated differently in first-touch vs. last-touch attribution models:

Choosing the Right Attribution Model

The ideal attribution model depends on business goals and customer journey dynamics. Consider these factors:

• Sales Cycle Length - Last-touch works better for short sales cycles, while first-touch or linear fit longer nurture sequences.
• Channel Interplay - Linear or time decay can help understand email's contribution relative to other channels like paid search or social.
• Subscriber Lifecycle - Analyze whether top-of-funnel or bottom-of-funnel emails deserve more revenue credit.

Before finalizing an attribution model:

1. Capture all relevant touchpoint data in a centralized analytics platform
2. Define rules for crediting touchpoints and distributing revenue
3. Test different models to compare results and select the best fit
4. Regularly monitor and validate the attribution model

Email Analytics Reporting Best Practices

Designing effective email analytics reports is critical to clearly communicate results and drive smarter decisions. Key principles for email reporting include:

Reporting Design Tips

• Tell a clear story with data by highlighting key takeaways up front
• Use descriptive titles and labels to clearly communicate chart content
• Select chart types that best fit the data story, such as line graphs for trends over time or bar charts for comparisons
• Incorporate benchmark data like industry averages to provide context
• Use progressive disclosure to share high-level data up front with options to drill down into details

This template shows a well-designed email campaign performance dashboard:

Advanced Email Reporting Techniques

Take email analytics reporting to the next level with these