The Subscription Analytics Dashboard You Should Actually Be Looking At
Monthly Recurring Revenue is the metric every subscription business tracks. It is also, on its own, one of the least useful metrics for making operational decisions. MRR tells you the current size of your subscription revenue. It does not tell you whether your business is healthy, where you are losing money, or what to do next. A subscription business growing MRR at 15% month-over-month can be in serious trouble if its churn rate is accelerating and its acquisition costs are climbing faster than lifetime value.
The analytics dashboard you need goes beyond MRR. It should tell you why your revenue is what it is, which cohorts are performing well and which are deteriorating, where your revenue leaks are, and what actions will have the highest impact. Here is what that dashboard looks like and how to build it.
Monthly Recurring Revenue
Churn Rate
Avg Subscriber LTV
LTV : CAC Ratio
MRR Movement -- March 2026
A subscription analytics dashboard should surface top-line metrics with trend indicators, backed by an MRR movement waterfall that decomposes what is driving change month to month.
Cohort Retention Curves
Cohort retention is the single most important view in your analytics dashboard. A cohort is a group of subscribers who started in the same period (typically the same month). Tracking what percentage of each cohort remains active over time reveals patterns that aggregate metrics hide entirely.
A healthy subscription business shows retention curves that flatten over time -- early churn is high (months 1-3) but survivors become increasingly stable. If your retention curves are still declining steeply at month 6, you have a product-market fit problem, not just a churn problem. If early churn is getting worse across recent cohorts while later retention holds steady, your acquisition is bringing in lower-quality subscribers.
Build your cohort table as a matrix: rows are sign-up months, columns are months since sign-up. Each cell shows the percentage of the cohort still active. Colour-code cells (green for above-target, red for below) so you can spot problems visually. This view immediately answers questions like "Did the subscribers we acquired during the Black Friday promotion retain as well as organic subscribers?" and "Is our retention getting better or worse over time?"
Month 1 Retention
78%median across subscription brands -- 22% cancel before their second order
Month 6 Retention
52%average across food, wellness, and pet verticals
Month 12 Retention
38%if your numbers fall significantly below these, cohort analysis will show you where
Retention benchmarks drawn from dozens of subscription audits across Shopify brands in food, wellness, and pet verticals.
Revenue Per Subscriber Over Time
Average revenue per subscriber is useful, but it obscures how revenue evolves over a subscriber's lifetime. A more revealing metric is revenue per subscriber by tenure -- how much does a subscriber spend in month 1, month 3, month 6, and month 12?
In a well-run subscription business, revenue per subscriber should increase over time as subscribers upgrade, add products, or respond to upsell offers. If revenue per subscriber is flat, you are leaving expansion revenue on the table. If it is declining (subscribers are downgrading), you have a value perception problem that discounting will not solve.
Track this alongside product mix data. If your subscribers are gradually shifting from premium to basic products, that tells a different story than if they are reducing frequency. The former suggests price sensitivity is increasing; the latter suggests product fatigue. Each requires a different response.
Churn Segmentation: Voluntary vs Involuntary
Not all churn is the same. Voluntary churn -- subscribers who actively cancel -- and involuntary churn -- subscribers who churn because their payment method fails -- have different causes and different solutions. Lumping them together gives you a single churn rate that is actionable by no one.
Involuntary churn typically accounts for 20-40% of total churn in subscription e-commerce. It is caused by expired credit cards, insufficient funds, bank declines, and fraud holds. The solution is dunning -- automated retry logic and customer communication to recover failed payments. This is a mechanical problem with mechanical solutions, and improving it is one of the highest-ROI activities for any subscription business.
Voluntary churn is the harder problem. Segment it by cancellation reason, subscriber tenure, and acquisition source. A subscriber who cancels in month 1 because they did not like the product is a different problem than a subscriber who cancels in month 8 because they feel the value has diminished. Your dashboard should show voluntary churn rate by tenure band (0-3 months, 3-6 months, 6-12 months, 12+ months) and by cancellation reason.
Track the ratio of voluntary to involuntary churn over time. If involuntary churn is growing as a percentage of total churn, your dunning needs attention. If voluntary churn is growing, your product or value proposition needs work. This ratio is a leading indicator that moves before your overall churn rate does.
Dunning Recovery Rates
Your dunning dashboard should track three things: the initial payment failure rate, the recovery rate (what percentage of failed payments are eventually collected), and the time to recovery (how many days and retries it takes). Each of these tells you something different about the health of your payment collection process.
A good initial failure rate for subscription e-commerce is 5-8%. If yours is above 10%, you may have issues with the payment methods you accept, the timing of your billing cycle (end-of-month billing has higher failure rates), or an unusually high proportion of prepaid cards in your subscriber base.
Basic Retry Logic
recovery rate with default platform retry settings -- every few days for two weeks
Optimised Dunning
recovery rate with smart retry timing, pre-dunning alerts, and email/SMS sequences
Break down recovery rates by failure reason. "Insufficient funds" declines have the highest recovery rates (the card is valid, it just needs funds) while "card expired" or "do not honour" codes have lower rates and require the customer to update their payment method. If your subscription platform (ReCharge, Skio -- see our comparison of the two subscription platforms) provides the decline code, use it to segment your dunning approach -- send "please update your card" emails for expired cards and silent retries for insufficient funds.
Subscriber Acquisition Cost vs Lifetime Value
The LTV:CAC ratio is the fundamental health metric for any subscription business. If your subscriber lifetime value is three times your acquisition cost (a 3:1 ratio), your unit economics are solid. Below 2:1, you are likely losing money on many subscribers. Above 5:1, you may be under-investing in growth.
The problem with LTV:CAC as a single number is that it hides enormous variation. Your Facebook-acquired subscribers might have a 4:1 ratio while your influencer-acquired subscribers have a 1.5:1 ratio. Your organic subscribers might have an 8:1 ratio that subsidises the overall average. Without segmenting by acquisition channel, you cannot make informed decisions about where to invest your marketing budget.
Build your dashboard to show LTV:CAC by acquisition source, by cohort month, and by product or plan type. Include payback period -- the number of months it takes for a subscriber's cumulative revenue to exceed their acquisition cost. A 3:1 LTV:CAC ratio with a 12-month payback period requires very different cash flow management than a 3:1 ratio with a 3-month payback period.
For LTV calculations, use observed LTV from mature cohorts rather than projected LTV from models whenever possible. Models are useful for forecasting but they tend to be optimistic because they underestimate the long tail of churn. If your oldest cohort is 18 months old, your 24-month LTV estimate is a projection, and it should be flagged as such in your dashboard.
The MRR Movement Waterfall
A waterfall chart that decomposes MRR changes from one month to the next is perhaps the most actionable single visualisation you can build. It shows: starting MRR, plus new subscriber MRR, plus expansion MRR (upgrades), minus contraction MRR (downgrades), minus voluntary churn MRR, minus involuntary churn MRR, equals ending MRR.
This view makes it immediately obvious what is driving your revenue changes. If your MRR is flat, is it because growth and churn are balanced (which is concerning) or because nothing is changing (which might be fine at a mature stage)? If MRR is growing, is it being driven by new subscribers (healthy) or by expansion of existing subscribers (also healthy but different)? If MRR is declining, is it churn accelerating (urgent) or new subscriber acquisition slowing (important but less urgent)?
Include reactivation MRR as a separate line item. This is revenue from subscribers who cancelled and later returned. Tracking this separately lets you measure the effectiveness of your win-back campaigns and understand how much of your "growth" is actually recovery versus net-new.
Building This in Practice
The data for everything described above exists in your subscription platform, but it is scattered across APIs and often not available in the pre-built analytics dashboards. To build a proper subscription analytics dashboard, you need three components: a data pipeline, a data warehouse, and a visualisation tool.
Metrics Hierarchy -- from North Star to diagnostics
North Star
Net Revenue Retention
Key Metric
MRR Growth
Key Metric
Churn Rate
Key Metric
LTV : CAC
Key Metric
Cohort Retention
Dunning Recovery
Payment Failure Rate
Cancel Reasons
Rev / Subscriber
Payback Period
Win-back Rate
Organise metrics in a hierarchy: a single north star at the top, supported by key metrics that decompose it, backed by diagnostic metrics for investigation.
Data Pipeline
Extract data from your subscription platform (ReCharge, Skio), your e-commerce platform (Shopify), your marketing tools (Klaviyo, ad platforms), and your payment processor. For ReCharge, use their API to pull subscriptions, charges, customers, and events data on a daily schedule. Fivetran and Stitch both offer pre-built ReCharge connectors. For custom extraction, a simple scheduled script that paginates through the API and writes to your warehouse works reliably.
Data Warehouse
BigQuery is my default recommendation for subscription brands. It is cost-effective at the data volumes most Shopify stores produce (gigabytes, not terabytes), it scales without management overhead, and it integrates well with most visualisation tools. For smaller operations, a PostgreSQL database is sufficient and can run on a modest cloud instance.
Build a dimensional model with fact tables for charges (successful, failed, refunded), subscription events (created, activated, cancelled, reactivated), and customer events (sign-up, login, support contact). Dimension tables cover subscribers, products, plans, and time. This structure supports all the metrics described above and makes adding new analyses straightforward.
Visualisation
Metabase is my preferred tool for subscription dashboards. It is open source, easy to deploy, and handles the cohort matrix visualisation natively. Looker is the premium option -- more powerful, better for large teams, but significantly more expensive. For teams already in the Google ecosystem, Looker Studio (formerly Data Studio) connects directly to BigQuery and is free.
Key stat: The total cost of a production-grade subscription analytics stack -- BigQuery, Fivetran (starter tier), and Metabase on a cloud instance -- starts at roughly $200-400/month. For a subscription business doing $50K+ in monthly revenue, this investment typically pays for itself within the first month through identifying and fixing a single retention or dunning issue.
Dashboard Layout
Organise your dashboard in three tiers. The top tier is the executive summary: MRR, net subscriber count, overall churn rate, and LTV:CAC ratio -- all with month-over-month trend indicators. The middle tier is the operational layer: the MRR waterfall, cohort retention matrix, and churn segmentation. The bottom tier is the diagnostic layer: dunning recovery rates, acquisition channel performance, and revenue per subscriber by tenure.
Most people look at the top tier daily, the middle tier weekly, and the bottom tier when investigating specific issues. Design the layout with this usage pattern in mind -- the top tier should answer "Is everything okay?" at a glance.
| Cadence | Metrics to Review |
|---|---|
| Daily | MRR, net subscriber count, payment failure rate, dunning recovery rate |
| Weekly | MRR waterfall, voluntary vs involuntary churn split, cancellation reasons breakdown, new subscriber volume by source |
| Monthly | Cohort retention curves, LTV:CAC by channel, revenue per subscriber by tenure, payback period, win-back conversion rate |
A review cadence ensures the right metrics are examined at the right frequency -- daily for operational health, weekly for trend detection, monthly for strategic decisions.
Building a subscription analytics dashboard is one of the highest-leverage projects a growing subscription brand can invest in. If you want help designing your data model, building the pipeline, or interpreting what the data is telling you, reach out. I have built these dashboards for brands at every stage, from $10K to $500K in monthly subscription revenue.