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Carrying out a customer retention analysis: a simple guide for ecommerce brands

A customer retention analysis looks beyond top-line retention metrics to understand why — and when — your customers stop purchasing from you.

Such an analysis can be highly technical, often straying into the realms of data science. But here, we’re going to run through some simple, actionable tactics to analyze customer retention.

You can gather your data from surveys, sales data and analytics

There are many data sources of data you can draw on for an ecommerce customer retention analysis. Combine them for best results.

Surveys and feedback forms

Surveys and feedback forms are direct lines to your customers’ thoughts and feelings about your products, services, and overall brand experience.

Designing effective surveys: Questions should be concise, targeted, and designed to elicit clear insights. Mix quantitative questions (e.g., rating scales) with qualitative ones (open-ended questions) to gather both measurable data and personal insights.

Distribution strategies: Send out surveys post-purchase, after customer service interactions, or during other key moments in the customer journey. Make use of email, social media, and even your website to reach a broad segment of your audience.

Increasing engagement: Offering incentives such as discounts or entry into a giveaway can significantly boost participation rates.

Transaction histories and purchase data

Use your transaction data to:

Analyze purchase behavior: Look for trends in purchase frequency, average order value, and product affinity. This can help identify loyal customers, potential upsell opportunities, and products that are crucial for retention.

Segment your customers: Use transaction data to segment your customer base into meaningful groups (e.g., by purchase behavior, product preferences, or spending tiers).

Website and social media analytics

You can use analytics data from your content management system, social media platforms and third-party tools like Google Analytics to better understand your audience.

Tracking user behavior: Use analytics tools to monitor how users navigate your site, what content they engage with, and where drop-offs occur. Heatmaps, session recordings, and funnel analysis can provide deep insights into user experience and barriers to conversion.

Social media engagement: Analyze likes, shares, comments, and direct messages to understand what content resonates with your audience. Social listening tools can also help track brand mentions across platforms, offering insights into public perception and areas for improvement.

Customer support interactions

Customer support interactions are a direct reflection of your customers’ pain points and satisfaction levels. If you have access to customer chat logs, you can see the exact language customers use to express their concerns.

Identifying common issues: Categorize support tickets by issue type and urgency to pinpoint common problems. This analysis can inform product improvements, FAQ updates, and preemptive customer service strategies.

Measuring satisfaction: Post-interaction surveys and net promoter score (NPS) measurements can gauge customer satisfaction and loyalty. Trends in this data can highlight the effectiveness of your support team and areas for training or process improvement.

Email engagement

Email communications with your customers provide insights into their interests and engagement levels.

Engagement metrics: Track open rates, click-through rates, and conversion rates to understand which content and offers are most appealing to your audience. Segmenting your email list based on these metrics can improve the relevance and effectiveness of future campaigns.

A/B testing: Regularly test different subject lines, email content, and send times to optimize engagement. This data-driven approach ensures that your email marketing strategy remains aligned with customer preferences and behaviors.

There are many pieces of software that can help you gather data

CRM systems

Customer relationship management (CRM) systems store all your customer data in one place. They can also segment customers, track their interactions across multiple channels, and deliver personalized marketing communications.

Segmentation and personalization: Use CRM data to segment customers based on their purchase history, behavior, and preferences. This allows for personalized communication strategies that resonate with each segment.

Integration capabilities: The best CRM systems offer seamless integration with other tools and platforms, such as email marketing software, social media channels, and ecommerce platforms.

Analytics and reporting: Advanced CRMs track metrics like customer lifetime value (CLV), churn rate, and engagement levels. Such metrics make it much easier to track your customer retention success.

Analytics and data visualization tools

Analytics and data visualization tools tell you how users interact with your website and other apps. Popular examples include:

  • Google Analytics
  • TripleWhale

Typically, you’ll want to use this software for:

Behavioral insights: Understand how customers navigate your site, what content they engage with, and where potential friction points exist.

Visualization of data: Complex data sets can be overwhelming. Data visualization tools help in presenting this data in an easily digestible format.

Custom reporting: Tailor reports to focus on the metrics that matter most to your retention efforts. Custom dashboards can highlight changes in customer behavior, campaign performance, and more.

Email marketing platforms

Email marketing platforms help you segment, automate and track your emails. Popular examples include:

  • Klaviyo
  • Omnisend
  • DotDigital

Look for software that helps with:

Automated campaigns: Set up automated email sequences for new subscribers, post-purchase follow-ups, and re-engagement campaigns.

A/B testing: Experiment with different email elements to determine what works best. Testing subject lines, content, and call-to-actions can significantly improve open rates and conversions.

Performance analytics: Detailed reports on campaign performance help in understanding what drives engagement and sales. Use these insights to refine your email strategy and improve overall retention.

AI and machine learning tools

AI and machine learning tools are revolutionizing the way ecommerce businesses predict customer behavior and retention. Platforms like Salesforce Einstein and IBM Watson provide predictive analytics, customer segmentation, and personalized marketing recommendations based on AI-driven insights.

Predictive analytics: AI tools can analyze historical data to predict future customer behavior, identifying those at risk of churning and the factors influencing their decisions.

Personalized recommendations: Machine learning algorithms can tailor product recommendations and content for individual customers.

Customer sentiment analysis: AI-powered sentiment analysis tools can gauge customer sentiment from reviews, social media, and customer support interactions.

You can put together (and implement) a customer retention analysis in (roughly) 11 steps

Understanding customer behavior and improving retention doesn’t have to be daunting. 

Here’s how you can make the most of all that data effectively, using straightforward tools and methods.

Step 1: data preparation and cleaning

What it means: Before diving into analysis, your data needs to be accurate and organized. This step is about removing any errors or inconsistencies.

Tools to help: Use Excel or Google Sheets to organize your data. These tools have functions to help identify and correct common data issues like duplicates or blank fields.

Step 2: customer segmentation

What it means: Divide your customers into groups based on shared characteristics, such as how often they buy or what products they prefer. This helps tailor your marketing and retention strategies.

Tools to help: Platforms like Klaviyo for email marketing or Shopify’s customer analytics offer built-in segmentation features based on purchase history and customer activity.

LoyaltyLion’s customer analytics can also help you find your most valuable customers.

Step 3: trend analysis

What it means: Look for patterns over time in your sales data. Identify which products are popular during certain seasons, and adjust your approach to stock and marketing.

Tools to help: Google Analytics can track which products are viewed and bought most often. You can use Excel or Google Sheets to visualize sales trends over time, too.

Step 4: churn analysis

What it means: “Churn rate” tells you the proportion of customers who stop buying from you each month. When you figure out which customers are dropping off, ask them why.

Tools to help: Surveys to customers who haven’t purchased in a while can provide insights. Tools like SurveyMonkey or Google Forms can help.

Step 5: engagement and feedback analysis

What it means: Measure how customers interact with your emails or social media and what they say about your brand. 

Tools to help: Use the analytics in your email marketing platform and social media accounts to see which content gets the most engagement. For qualitative feedback, read through customer reviews on your site or social media.

Step 6: A/B testing

What it means: Compare two versions of something (like an email) to see which one performs better. Then refine based on the results.

Tools to help: Many email marketing platforms offer A/B testing features, so you can easily test different subject lines or content to see what resonates best with your audience.

Step 7: predictive analytics

What it means: Use your existing data to make educated guesses about future customer behavior. This can help anticipate needs and improve retention.

Tools to help: Tools like Google Analytics offer basic predictive insights, such as potential revenue from returning customers. For more advanced predictions, you could get in touch with data analysis service providers.

Step 8: actionable insights generation

What it means: Turn your data analysis into specific, actionable steps to take. This might involve changing your marketing strategy, updating your product line, or improving customer service.

Tools to help: Use dashboards in tools like Google Analytics or create your own in Excel/Google Sheets to keep track of key metrics at a glance.

Step 9: strategy development

What it means: Based on your insights, develop targeted marketing campaigns, product adjustments, or customer service improvements tailored to different customer segments.

Implementing strategies: Use email marketing, social media, and your ecommerce platform to put these strategies into action, utilizing segmentation and personalized content to engage different customer groups.

Step 10: implementation and monitoring

What it means: Apply your new strategies and keep an eye on their performance. Adjust based on what works and what doesn’t.

Monitoring tools: Lean on your ecommerce platform’s analytics and Google Analytics to monitor changes in customer behavior and sales patterns.

Step 11: iteration and optimization

What it means: Keep refining your strategies based on ongoing analysis and feedback. This cycle of improvement helps keep your business adaptable and customer-focused.

Ready to get started with loyalty marketing?

Improving customer retention by 5% can boost profits by 95%, and one of the best ways to improve retention is through a loyalty program.

With loyalty program members generating between 12 and 18% more revenue per year than the average guest shopper, it’s no wonder so many brands are investigating how to leverage loyalty to their advantage. If you want to do the same, book a demo with one of our specialists. 

About the author

Fiona Stevens

Fiona Stevens is the Head of Marketing at LoyaltyLion. Fiona has over ten years’ experience in Marketing, having worked in-house and agency side across functions including PR, SEO and content. She has specialized in loyalty for retail and ecommerce brands.

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