K9 Resorts Customer
Lifetime Value

The single most important finding in this analysis: 587 Champions — just 21% of the customer base — generate 65.7% of total revenue. The remaining 2,170 customers split the other 34.3%. Losing one Champion costs roughly as much as acquiring five average customers. Protecting this segment is the highest-ROI activity the business can pursue.

The Pareto curve confirms extreme revenue concentration. The top 29% of customers by revenue drive 80% of total revenue — well beyond the classic 80/20 rule. This is consistent with premium pet service businesses where a core group of frequent boarding customers accounts for a disproportionate share of annual spend.

Champion CLV variance is enormous — the IQR spans roughly $2,400 to $6,000, with outliers exceeding $15K. This means there are Champions worth 20–30× the median customer. Loyal customers show a tighter distribution centered around $1,200–$1,500, suggesting a more predictable, mid-tier booking pattern. Needs Attention, At Risk, and Lost customers cluster near zero, confirming these segments represent lapsed or single-visit relationships.

Champions cluster in the upper-right quadrant — high frequency (many transactions) and high total revenue — with the most extreme outliers reaching $40K+ in lifetime revenue. The Needs Attention (pink) and At Risk (orange) clusters concentrate in the bottom-left, with low transaction counts and low revenue. The visual separation of segments confirms that the RFM scoring is meaningfully discriminating customer behavior.

Multi-pet households (2+ animals) generate 65% more CLV ($2,257 vs. $1,364) and equivalent total revenue advantage. Interestingly, average transaction count is slightly higher for single-pet households (17.6 vs. 15.7), suggesting multi-pet households may book longer stays rather than more frequent visits — a meaningful distinction for package design. Targeting multi-pet households in acquisition and retention campaigns is a clear lever.

Tenure is dramatically right-skewed. The modal customer has tenure under 50 days — meaning the single most common customer profile is someone who visited once or twice and never returned. The mean (228 days) is pulled far above the median (95 days) by a long-tenured tail of loyal repeat customers. This is a churn problem, not a revenue problem. The revenue is there — it's concentrated in a small cohort of long-tenure customers who stayed.

The heatmap reveals two structural retention patterns. Early cohorts (Jun–Aug 2023) show unusually high month-1 retention (81% for Aug 2023 cohort) — these were founding-era customers with novelty-driven repeat visits. Later cohorts stabilize at 30–50% month-1 retention, which is closer to the structural baseline. The dark-red (low retention) dominance across all cohorts after month 3 confirms the churn cliff happens early — within the first quarter of the customer relationship.

The average retention curve shows a steep initial drop: 100% at acquisition, 42% at month 1, 33% at month 2, and plateauing around 20–23% from months 6–12. This is a classic "fast churn / stable core" pattern — the majority of customers churn within the first two visits, but those who reach month 6 tend to stay. The implication is clear: the highest-ROI retention intervention is in months 1–3, before the plateau sets in.

Acquisition peaked during the facility's growth phase in late 2023 (August 2023 saw 239 new customers — the single highest month). Since then, monthly acquisition has trended down toward a 50–90 customer baseline, consistent with a maturing local market. The seasonal pattern shows summer months driving acquisition spikes, likely from owners needing boarding for vacation travel. The Feb 2026 acquisition level of ~45/month suggests the business has largely captured its immediate catchment area and growth will require either expanded marketing reach or geographic expansion.

Boarding customers generate the highest average CLV ($2,109) and comparable tenure to daycare customers (282 vs. 287 days). Daycare customers are close behind at $1,692 CLV — and their tenure is effectively identical to boarding, suggesting daycare builds just as durable a relationship. Grooming and Other categories generate a fraction of the CLV ($433–$437) with roughly half the tenure, consistent with their role as single-service or occasional-use customers rather than relationship anchors.

Boarding dominates total revenue at $0.29M in the analysis window, with daycare contributing $0.03M. Grooming and Other categories are negligible at this scale. This confirms that boarding is the core business — the product with the highest price point, highest CLV, and highest total revenue contribution. Daycare is a meaningful relationship-builder but represents a secondary revenue stream. Any pricing, retention, or capacity strategy should be anchored in the boarding segment.

The Feb 2025 rate increase is clearly visible as a step-change across all four room types. The Luxury Suite shows the largest absolute rate increase (~$10/night) and has maintained its new pricing level consistently through the post-increase period. The Feb 2026 increase is also visible, pushing the Luxury Suite above $100/night for the first time.

This is the critical chart for pricing strategy: volume did not decline after the Feb 2025 rate increase. In fact, monthly reservations grew in the post-increase period, reaching a new peak of ~450 by mid-2025. This is strong evidence of price-inelastic demand — K9 Resorts' customers are not price-sensitive at these rate levels, likely because premium pet boarding is perceived as a necessity rather than a luxury for this customer base.

The before/after comparison across all three metrics — nightly rate, reservation volume, and revenue per stay — is uniformly positive. Every room type shows rate increases of 7–12%, volume increases of 6–44%, and stay revenue increases of 6–13%. The combination of higher prices and higher volume means revenue per stay grew on every tier. The Double Compartment's 43.5% volume increase is likely capacity-driven rather than purely price-driven, but the absence of any volume decline across any tier is the key takeaway.

Labor as % of revenue varies dramatically month to month — from a best-case 23.8% to a worst-case 96.7%. This volatility is driven by seasonality: low-revenue winter months maintain fixed staffing, compressing margins. High-revenue summer months with peak boarding demand spread the same labor base across more revenue, improving efficiency. The goal is to minimize labor% in low-season months through scheduling optimization and shift toward more variable labor structures (part-time, on-call) during trough periods.

Several customers (Courtney, Rae, Marilyn) have high predicted future CLV despite relatively low historical revenue — the model detects high-frequency, recent visit patterns that signal strong forward purchase intent. Jane has the opposite profile: high historical revenue but only 56.6% probability alive, suggesting a customer who may be drifting. These asymmetries between historical and predicted CLV are exactly what makes forward-looking modeling valuable for prioritizing retention outreach.