Customer Retention and Lifecycle Analytics

Recency, frequency, and monetary scoring is the standard way to prioritize which customers deserve proactive retention spend, but many D2C brands still rely on simple "active in 90 days" flags or email platform segments that ignore order value and rhythm. That hides at-risk high-value buyers and over-invests in one-time discount seekers.

FireAI builds rfm segmentation d2c models from your OMS and subscription data. Scores are computed per customer with configurable windows so seasonal categories do not misclassify loyal buyers. Segments such as Champions, At Risk, and Hibernating map directly to playbooks: VIP treatment, save offers, and reactivation campaigns.

What FireAI tracks for RFM:

• Recency from last paid order or successful renewal, not from email opens
• Frequency of orders or successful billing cycles in the analysis window
• Monetary value using net revenue after returns and discounts where your data supports it
• Segment migration week over week: which customers moved from Loyal to At Risk
• Overlap with acquisition channel so you do not confuse paid-traffic bargain hunters with organic loyalists

Real example: A nutrition D2C brand found that 9% of customers classified as "active" by email engagement had not purchased in 120 days. RFM rescoring moved them into Hibernating; a two-step SMS and offer test recovered 14% to a second order within 30 days.

FireAI natural language queries:

• "Show RFM distribution for the last 90 days with segment counts and revenue share"
• "Which Champions dropped in recency score in the last 14 days?"
• "List At Risk customers with lifetime value above ₹15,000"

A single retention percentage for "all customers" hides the truth. Cohort retention d2c analysis shows how each acquisition or signup cohort behaves over week 0, 1, 4, 8, and beyond: one-time buyers versus subscribers, marketplace versus D2C site, promo code versus full price.

FireAI aligns cohort definitions to your business: first purchase date, subscription start, or first successful renewal. Curves compare repeat order rate, subscription survival, and revenue retention so you can see whether a Q2 campaign brought volume that never came back.

What FireAI tracks for cohorts:

• Classic retention curves (share active or repurchasing by period) and revenue-weighted curves
• Separate curves for subscription vs non-subscription cohorts
• Breakdown by product category, channel, and offer type
• Cohort quality versus CAC payback: did cheap cohorts actually retain?

Real example: A beauty brand saw Meta Q1 cohorts at 28% month-3 repeat rate versus 41% for organic and branded search cohorts. Budget reallocation toward high-retention cohorts improved blended LTV:CAC from 2.1x to 2.7x over two quarters without raising top-of-funnel spend.

FireAI natural language queries:

• "Plot month-3 repeat rate by acquisition month for the last 4 quarters"
• "Compare subscription survival curves for annual vs monthly plans started this year"
• "Which cohorts have the steepest drop between order 1 and order 2?"

Cart abandonment rate alone does not tell you what to fix. High abandonment can mean unexpected shipping cost, slow mobile checkout, payment failures, mandatory account creation, or low-intent traffic from prospecting ads. Cart abandonment analysis in FireAI ties each abandoned session to step-level exit data, device, channel, and cart value bands.

FireAI combines OMS cart and checkout events with session data where connected, classifies abandonment by last completed step, and correlates with shipping method, discount eligibility, and payment errors. You see whether the problem is offer, UX, or audience quality.

What FireAI tracks for abandonment:

• Abandonment rate and lost revenue by step: cart, shipping, payment, OTP
• Split by device, region, and acquisition channel
• Recovery performance from email and SMS flows
• A/B test readouts when you change shipping messaging or guest checkout

Real example: A home essentials brand found 38% of abandonments occurred after shipping cost display on mobile, concentrated on orders under ₹899. Introducing a clear threshold message and a low-AOV bundle lifted completed checkout by 4.2 percentage points in four weeks.

FireAI natural language queries:

• "What share of abandonments exit at shipping vs payment this month?"
• "Compare cart abandonment rate Meta vs organic on mobile"
• "What is recovered revenue from abandoned cart emails in the last 30 days?"

Subscription brands feel churn in monthly recurring revenue weeks after the underlying behavior shifted: skipped deliveries, failed cards, support contacts about price, or lower engagement with refill reminders. Churn prediction d2c uses renewal outcomes, payment history, product consumption proxies, and support signals to rank who is likely to cancel or churn passively.

FireAI scores at-risk subscribers continuously, surfaces the drivers (price sensitivity, failed payment, product fatigue), and connects to playbooks: smart retries, save offers, plan changes, and win-back timing. The goal is to intervene while the customer is still salvageable.

What FireAI tracks for subscription churn:

• Probability scores with explainable top factors per subscriber
• Churn by plan type, tenure, and acquisition offer
• Voluntary cancel vs passive churn (failed payment, card expiry)
• Incremental lift from save offers and retry rules

Real example: A beverage subscription service saw 31% of cancels preceded by two or more skips within 60 days. FireAI prioritized that pattern in the risk model; proactive "swap flavor" and "pause instead of cancel" flows cut voluntary churn by 1.8 points in the pilot cohort.

FireAI natural language queries:

• "List subscribers with churn probability above 70% in the next 30 days"
• "What factors explain churn risk for annual plan customers?"
• "How did voluntary churn change after the July price change?"