Retention dashboards
The Retention section in Explore covers the customer side of growth — who comes back, when they return, and how much they spend over their lifetime — across seven dashboards powered by your ecommerce datasource (Shopify, BigCommerce, or WooCommerce) and, for messaging, Klaviyo.
Acquisition tells the story of getting customers in; Retention tells what happens after. The section unifies messaging performance (Klaviyo) with order-level customer behavior so the two can be read against each other. For DTC brands — where repeat-purchase rate and LTV often decide whether paid acquisition is even profitable — this is where the durable-growth questions get answered.
Reading order and required datasources
Section titled “Reading order and required datasources”| Dashboard | Requires | Best for |
|---|---|---|
| Email & SMS | Klaviyo connected | Campaign / flow performance, send-to-revenue attribution |
| Customers | Ecommerce datasource | Customer-level table — recency, total orders, lifetime revenue |
| RFM Segments | Ecommerce datasource | Recency / Frequency / Monetary segmentation, segment-size shifts |
| Repurchase Rates | Ecommerce datasource | What share of customers come back within 30/60/90 days |
| Cohort Analysis | Ecommerce datasource | Retention curves by acquisition cohort (month, quarter, channel) |
| LTV Trends | Ecommerce datasource | Lifetime value over time, by cohort or segment |
| Customer Segments | Ecommerce + optional Klaviyo | Custom-segment analysis (loyalty tiers, VIPs, lapsed) |
A dashboard appears only when its required datasource is connected — Email & SMS stays hidden until Klaviyo is connected, and the cohort and LTV dashboards need enough history (typically 60+ days post-first-sync) to render meaningfully.
Most workspaces read the section in roughly this order: Email & SMS and Customers for the daily, current-state view; Repurchase Rates and Cohort Analysis for medium-horizon retention curves; LTV Trends and RFM Segments for quarter-over-quarter shifts in customer value. Email & SMS and Customers are daily-read surfaces; the cohort, LTV, and RFM dashboards are quarterly-read — they don’t move meaningfully day over day.
Email & SMS dashboard
Section titled “Email & SMS dashboard”Messaging performance from Klaviyo, in the standard four-part Explore layout.
Header metrics: Total Sends, Open Rate, Click-Through Rate, Click-to-Open Rate, Attributed Revenue.
What it answers: which campaigns and flows drive revenue. Drill down by Campaign for one-off sends or by Flow for automated sequences (welcome, abandoned cart, post-purchase).
Key note: after iOS 15, Apple Mail’s privacy-preserving prefetching inflates open rates — treat click-through as the more reliable engagement signal. Attributed Revenue also depends on Klaviyo’s attribution window; confirm it in Klaviyo’s settings before reconciling.
Customers dashboard
Section titled “Customers dashboard”A customer-level table: each row is a customer, with columns for First Order Date, Last Order Date, Recency (days since last order), Total Orders, Lifetime Revenue, and RFM Segment.
What it answers: who your VIPs are, which customers are going at-risk (high recency), and the raw material for segmentation work.
Key note: customer PII (name, email) comes from the ecommerce datasource, and access depends on workspace role — some workspaces restrict customer-level views.
RFM Segments
Section titled “RFM Segments”RFM stands for Recency, Frequency, Monetary. It segments customers on three dimensions: days since last purchase (Recency), total number of orders (Frequency), and total revenue (Monetary). Each customer gets an RFM score that buckets them into named segments such as “Champions,” “At-Risk,” and “Lost.”
What it answers: where to target marketing — re-engagement to “At-Risk,” loyalty rewards to “Champions” — and how segment sizes shift over time.
Key note: segment boundaries are workspace-wide, so a customer’s segment shifts as new orders arrive or as time passes without one. Custom thresholds are managed at the workspace level.
Repurchase Rates
Section titled “Repurchase Rates”How often customers come back to buy again, in two cuts: all customers (what share placed N+ orders) and new customers (what share of a period’s newly acquired customers placed a second order).
What it answers: program-level retention health, cohort-over-cohort comparison, and first-purchase product-market-fit signals — a high first-to-second-order rate indicates strong fit.
Key note: repurchase rate is calculated at the customer level across all orders, which is why it won’t match a Shopify “repeat customer rate” that counts orders rather than unique customers.
Cohort Analysis
Section titled “Cohort Analysis”Retention by acquisition cohort — typically acquisition month on one axis and time-since-acquisition on the other, with each cell the retention rate for that cohort at that age.
What it answers: whether retention is improving or declining cohort over cohort. A newer cohort retaining better than an older one at the same age means retention is improving; worse means investigate audience, onboarding, or product.
For example: of customers who first purchased in January, 28% made a second purchase within 30 days, and 41% within 60 days. A benchmark above 30% at 60 days generally indicates healthy retention for consumable products.
Key note: read mature cohorts (acquired ≥ 6 months ago) and treat recent ones as in-flight — a January cohort can’t have a 90-day number until April.
LTV Trends
Section titled “LTV Trends”Customer lifetime value over time, segmentable by acquisition cohort, channel, or product. LTV is calculated as cumulative revenue per customer up to a given age (LTV at 90 days, at 1 year); each cohort’s curve grows as it ages.
What it answers: whether the average customer is becoming more or less valuable, whether new cohorts are worth more or less than older ones at the same age, and which channels produce higher-LTV customers.
Key note: LTV is the integral of all customer behavior, so it lags acquisition shifts by months — a change in the LTV trend usually reflects an acquisition-mix shift two to four months earlier. The most recent cohort always shows a low LTV because it hasn’t aged.
Customer Segments
Section titled “Customer Segments”Performance broken down by customer group — RFM segment, loyalty tier, externally uploaded custom segment, or behavior band (e.g. subscribers vs one-time buyers).
Header metrics (per segment): customer count, revenue, AOV, repurchase rate, LTV.
What it answers: how value compares across segments (subscribers vs non-subscribers), how segment-targeted campaigns performed, and which segments are under- or over-served.
Key note: custom segments (uploaded customer lists) require workspace-level configuration.
Common questions
Section titled “Common questions”Why does the Cohort Analysis heatmap look empty for recent months?
Recent cohorts have had less time to show repeat purchases, so the most recent one or two months will always have sparse data in the later-age columns. This is expected — read the mature cohorts.
Why are Repurchase Rates different from the repeat purchase rate I see in Shopify?
Ignyte IQ calculates repurchase rate at the customer level across all orders; Shopify’s metric counts orders, not unique customers. The two answer different questions and won’t match.