Having access to all your data in one place is only the first step. We know you have business questions that can lead to valuable optimizations once the learnings are uncovered. We've put together a list of the most common (and some not so common) questions that you want to answer in order to make actionable changes.
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- Reporting
Retention & Subscriptions
When are customers typically churning out, and what are the key influencers?
- Navigation
- Report: Subscription > Retention
- Metrics: Starting Subscriptions, Overall Retention Rates
- Filters: Charge Frequency, Acquisition Date
- Time Frame: Last 12 Months - Past 30 days
- How to Analyze
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- Review the cycle to cycle retention rates to determine where you are seeing the greatest dips overall and by cohort
- Drill down into granular filters to understand how factors like Product, Promo Code or Acquisition Source impact retention
- Review skip rates to understand how they impact retention and CLV. Someone could remain “active,” but never drive you a subsequent dollar if they keep skipping renewals.
- Review active vs. passive churn separately to determine which customer are being lost due to credit card declines versus active cancellations and drive retention strategies separately.
- What's Next
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- Once you have a clear understanding of your churn, figure out WHY customers are actively churning out by leveraging the Subscriptions > Cancellations report
- Implement retention tactics to address reasons for active churn and dunning strategies to reduce passive churn.
- Keep an eye on quick cancels (churn in the first 72 hours) and first renewal retention rates in our First Renewal report to help normalize early retention data. Setup alerts to proactively optimize low quality traffic that is churning out quickly.
Which types of subscribers stay the longest?
- Navigation
- Report: Subscription > Retention
- Metrics: Renewal 1 Overall Retention Rate, Renewal 2 Overall Retention Rate + , Average Renewal Cycle
- Filters: Offer Name
- Time Frame: Previous 12 Months (-30 days)
- How to Analyze
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- Customize columns, prioritizing "Overall Retention Rate." This correlates to your renewal cycles. You will see your retention rate decline as your subscribers either opt out (active churn) or have declined credit cards (passive churn).
- The Average Renewal cycle metric will tell you an average of how long the subscribers stick around within the Offer Name cohort
- What's Next
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- Drill down into more metrics! Do you want to see the acquisition source? Layer on your UTM parameters as a second metric.
Why are my customers churning?
- Navigation
- Report: Subscription > Cancellations
- Metrics: Active Cancels, Passive Cancels
- Filters: Cancel Reason
- Time Frame: Previous 12 Months (-30 days)
- How to Analyze
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- Active Cancels reflect customers who have cancelled from in a customer portal, called in or otherwise have been the initiator of the cancel
- Passive Cancels indicate to look into your Dunning Schedule. These are subscriptions that have been canceled after a payment has failed to process.
- What's Next
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- Drill down into more metrics! Layer on Item Name, Discount Code, Shipping Frequency, UTM parameters etc to identify customer trends.
Who are my most profitable customers?
- Navigation
- Report: Customers > Customer Lifetime Value
- Metrics: Charges Per Customer, Average Order Value, Customer Lifetime Value
- Filters: Initial Offer Name, Initial Discount Code, Acquired Week
- Time Frame: Previous 12 Months (-30 days)
- How to Analyze
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- Work through the filters that are most applicable to the types of promotions or changes that you have made.
- If there was a promotional week, were those customers driving higher value?
- Do customers from one code yield higher CLV than other codes?
- What's Next
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- Drill down into more metrics! Layer on first vs last click attribution, current offer or current item, UTM parameters etc to identify customer trends.
How do I find a count of all of these orders with active shipments?
- Navigation
- Report: Customers > Customer Lifetime Value
- Metrics: Charges Per Customer, Average Order Value, Customer Lifetime Value
- Filters: Initial Offer Name, Initial Discount Code, Acquired Week
- Time Frame: Previous 12 Months (-30 days)
- How to Analyze
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- Work through the filters that are most applicable to the types of promotions or changes that you have made.
- If there was a promotional week, were those customers driving higher value?
- Do customers from one code yield higher CLV than other codes?
- What's Next
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- Drill down into more metrics! Layer on first vs last click attribution, current offer or current item, UTM parameters etc to identify customer trends.
Marketing
We are having issues where deep discount promo codes are leaking to deal sites and I want to keep track of who is responsible for actually driving the sale.
- Navigation
- Report: Finance > Sales
- Metrics: Charges, Discount, Discount Rate, Total Revenue
- Filters: Discount Code, Discount Name, Referrer Base Domain
- Time Frame: Custom: Promo code Launch - present
- How to Analyze
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- Review the Discount Code in question and drill into the Referrer Base Domain to understand which website source was responsible for driving the sale according to last click attribution
- What's Next
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- If you are paying an affiliate for the sale based upon discount code usage, you can now provide a clear audit of which sales they were directly responsible for driving.
How Successful is my Customer Service Team in upselling customers?
- Navigation
- Report: Subscription > Retention
- Metrics: Initial Customer and Offer details, Upsell Metrics
- Filters: Created User
- Time Frame: 30 days
- How to Analyze
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- Understand the initial starting point per customer service rep with regard to order volume processed. If you only offer upsells on certain products, filter out those particular products.
- Review the impact of upsells by considering the upsell rate and value of upsells driven by each customer rep. This will also tie into the average customer value metric to provide a holistic view of how upsell strategies drive aggregate customer level value.
- Add Drill by filters to view by Customer Path to understand which products and associated upsells are driving the greatest performance.
- Other interesting filter views:
- Is New Customer: does upsell performance differ across new vs. existing customers?
- Initial Discount Name: are customers more likely to accept an upsell if they use a discount on their initial order?
- Custom Parameters: is there a higher upsell rate for customers acquired from certain sources?
- What's Next
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- Optimize upsell strategies based upon the most successful upsell paths and tactics leveraged by your most successful reps.
- Create visibility across your Customer Service team to keep an eye on performance per rep. Leverage user permissions and groups to define what platform-level visibility to provide.
- Leverage insights to drive online in-cart and post-cart upsell strategies.
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