The question sounds simple: when do people decide to visit Costa Rica? The answer is not a single moment but a distribution — a spread of human behavior across weeks, months, and years of planning that has been shifting steadily for two decades and accelerated dramatically after the pandemic.

This report draws on proprietary booking data from a Costa Rica-based travel agency to answer that question with precision. Rather than relying on surveys or industry estimates, the analysis is grounded in the actual behavior of 96,533 verified leads across 2024 and 2025, and 82,000 completed itineraries going back to 2006. The results challenge several assumptions about last-minute travel, reveal which customer segments drive the most revenue, and offer a clear picture of where the Costa Rica travel market is heading.

The 162-Day Traveler

The most important single number in this report is 162 days. That is the revenue-weighted average booking window for Costa Rica travel in 2024–2025: the point, measured backwards from arrival, at which the average revenue dollar was generated. In plain terms, the typical trip to Costa Rica — weighted by what it contributes to the business — begins with an inquiry made roughly five and a half months before departure.

This figure matters because it is not the same as the median booking window, which sits closer to 112 days for initial inquiries. The gap between the two reveals something important: the travelers who book furthest in advance also spend the most. The 180+ day segment generates an average booking value of $12,026 — nearly 47% higher than the 0–30 day segment’s $5,806. Planning further ahead and spending more are not coincidental behaviors; they reflect a distinct traveler profile.

“The average revenue dollar from Costa Rica travel comes from an inquiry made 162 days before arrival — not 30 days, not 60 days. Five and a half months out.” — Costa Rica Travel Agency Research Team, February 2026
Figure 1
Total Revenue and Average Booking Value by Booking Window Tranche
96,533 leads, Jan 2024 – Dec 2025 · Revenue bars (left axis) · Average value line (right axis)
Source: Costa Rica Travel Agency CRM, 2024–2025. Missing-date records excluded.

The Six Booking Windows: A Tranche-by-Tranche Analysis

Table 1 · 24-Month Combined Tranche Performance · Jan 2024 – Dec 2025
Booking WindowInquiriesBookingsConv RateQuote OpenAvg Booking ValueRevenue Share
0 – 30 days13,5971,0938.0%45.0%$5,8066.9%
30 – 60 days15,6251,61810.4%54.6%$6,68311.7%
60 – 90 days13,0581,55511.9%58.9%$7,66312.9%
90 – 120 days10,8091,27211.8%60.9%$8,88812.2%
120 – 180 days16,0991,93012.0%62.8%$9,64420.1%
180+ days27,3452,78110.2%62.3%$12,02636.2%
TOTAL / AVERAGE96,53310,24910.6%58.1%$9,019100%
Figure 2
Conversion Rate and Cancellation Rate by Booking Window
120–180d maximises conversion; cancellations rise monotonically with lead time
Source: Costa Rica Travel Agency CRM, 2024–2025 cleaned dataset.
Figure 3
Demand Volume by Tranche
Inquiries, quotes, and bookings showing funnel depth
Source: CRM 2024–2025.
Figure 4
Share of Bookings vs Share of Revenue
180+d over-indexes on revenue relative to booking share
Source: CRM 2024–2025.

The Last-Minute Traveler (0–30 Days)

The shortest-window segment is smaller and weaker than it appears. After removing records where inquiry, arrival, and departure dates were all identical — a data artifact representing non-website inquiries with missing date fields — the 0–30 day tranche holds 13,597 genuine last-minute inquiries. Its conversion rate of 8.0% is the lowest of any window and its average booking value of $5,806 sits well below the portfolio average.

The Sweet Spot: 120–180 Days

The most commercially efficient segment in the entire dataset is the 120 to 180 day window. Inquiries arriving here convert at 12.0% — the highest rate of any tranche in both 2024 and 2025 — and carry a quote open rate of 62.8%. Average booking value reaches $9,644, and the segment’s revenue share (20.1%) exceeds its inquiry share (16.7%).

The Long-Range Planner (180+ Days)

Travelers who inquire more than six months before arrival produce the highest average booking value ($12,026) and the highest total revenue ($33.4 million over 24 months). The trade-off is cancellation risk: at 1.44%, the cancellation rate is four times that of the 0–30 day segment, and each cancellation carries an average loss of approximately $12,000.

2024 vs. 2025: Year-on-Year Comparison

Comparing the two most recent full years reveals a quality-over-quantity shift. Overall inquiry volume declined 7.3% from 50,096 to 46,437, and bookings fell from 5,345 to 4,904. But average booking value rose from $8,811 to $9,246 (+5.0%), and quote open rates increased in every single tranche in 2025. The 2025 pipeline appears to be attracting better-qualified leads even as total volume softened.

Figure 5
Closed Won · 2024 vs 2025
30–60d grew YoY; 0–30d and 180+d compressed
Source: CRM 2024–2025.
Figure 6
Win Rate % · 2024 vs 2025
120–180d leads both years; 0–30d weakest after cleaning
Source: CRM 2024–2025.
Figure 7
Loss Rate % · 2024 vs 2025
0–30d has highest loss rate — lowest intent tranche post-cleaning
Source: CRM 2024–2025.
Figure 8
Cancellation Rate % · 2024 vs 2025
Cancellations scale with window length; 2025 worsened in mid/long windows
Source: CRM 2024–2025.
Figure 9
Quote Open Rate % · 2024 vs 2025
2025 quote engagement higher in every single tranche
Source: CRM 2024–2025.
Figure 10
Average Booking Value · 2024 vs 2025
Values up across all tranches in 2025
Source: CRM 2024–2025.
Figure 11
Funnel Outcome Distribution by Tranche · 24-Month Combined
Closed Won (green) is the thin sliver; closed lost dominates every window at ~88%
Source: CRM 2024–2025. Closed Lost = all non-Sold, non-Cancelled stages.

Part II · Five-Year Lead Time Analysis (2021–2025)

Zooming out to the five-year window from 2021 through 2025 reveals the post-COVID recovery arc in full detail. The median quote-to-travel window compressed to just 85 days in 2021 as the industry reopened, then climbed steeply to a peak of 121 days in 2023 before a moderate pullback to 110 days in 2025. The 5-year pooled median lands at 112 days quote-to-travel and 98 days sale-to-travel — a stable 14-day gap that reflects a consistent consideration window between first contact and commitment.

Figure 12
Annual Median Lead Times · 2021–2025
Post-COVID recovery drove sharp booking window expansion 2021→2023, now stabilising
Source: CRM, 36,786 itineraries with arrivals 2021–2025. Medians reported.
Figure 13
Quote-to-Travel by Arrival Month · All 5 Years
2021 compressed across all months; 2023–24 peak before 2025 pullback
Source: CRM 2021–2025.
Figure 14
Sale-to-Travel by Arrival Month · All 5 Years
Mirror pattern to Qt2 · Sep–Oct compression in 2025 most notable
Source: CRM 2021–2025.
Figure 15
Structural Shift: 2025 vs 2021 — Days Gained per Arrival Month
January arrivals gained the most (+63d qt2); August barely moved (+8d)
Source: CRM 2021 and 2025 cohorts compared.

Part III · Full 20-Year Historical Analysis (2006–2025)

Twenty Years of Shifting Forward

The 20-year view places the current market in its full historical context. Analysis of 82,391 completed itineraries from 2006 through 2025 reveals that the median quote-to-travel window grew from 69 days in 2006 to 110 days in 2025 — a 59% increase. The data reveals four distinct behavioural eras, each driven by different macro forces and each creating a new baseline that the next era would build upon.

+41 days

That is how much the median Costa Rica trip inquiry lead time has grown between 2006 and 2025 — from 69 days to 110 days. The structural shift reflects a fundamental change in how travelers research, plan, and commit to international travel, accelerated by digital discovery channels and post-pandemic demand patterns.

Figure 16
Median Quote-to-Travel and Sale-to-Travel Windows, 2006–2025
82,391 itineraries · Four behavioural eras · COVID dip (2020–21) followed by record expansion
Source: CRM 2006–2025. Outliers above 730 days excluded.
Figure 17
Pooled Seasonality by Arrival Month · All Years
Dec/Nov highest, Aug lowest — pattern consistent across all eras
Source: CRM 2006–2025 pooled.
Figure 18
Seasonality by Era · Quote-to-Travel
The seasonal shape is preserved across eras — only the absolute level shifts up
Source: CRM era cohorts: 2006–2012, 2013–2019, 2020–2021, 2022–2025.
Figure 19
Long-Run Structural Shift: 2025 vs 2006 — Days Gained per Arrival Month
Jan (+68d qt2) and May (+54d qt2) show the greatest 20-year transformation
Source: CRM 2006 and 2025 cohorts compared.

Seasonality: When to Book, Month by Month

December arrivals carry the longest lead times of any month: a median of 137 days from inquiry to arrival, pooled across 2021–2025. November follows at 128 days. Both months align with Costa Rica’s peak dry-season period, when demand is highest and availability in preferred lodges is most constrained. August arrivals, by contrast, sit at just 94 days — and this pattern has barely changed over 20 years, gaining only eight additional planning days between 2006 and 2025.

January has seen the most dramatic transformation. In 2021, the median lead time for January arrivals was just 54 days. By 2025 it had grown to 117 days — a 63-day shift, the largest structural change of any month in the dataset. Early-year travel to Costa Rica has shifted from a relatively spontaneous decision to a highly planned one.

Research Methodology

Booking window analysis: 119,762 raw CRM records with inquiry dates in 2024–2025. Records where inquiry date = arrival date = departure date excluded (22,745 records, 19% of raw). Clean dataset: 96,533 records with booking windows 0–730 days.

Conversion definitions: Closed Won = Lead Stage “6. Sold”. Cancelled = Lead Stage “7. Cancelled”. All other stages = Closed Lost. Revenue from Invoice.Amount for Closed Won leads only.

Historical analysis: 82,391 itineraries with arrivals 2006–2025 (negatives and >730d outliers excluded). Quote-to-Travel = datecreated to arrivaldate; Sale-to-Travel = datesold to arrivaldate. All figures reported as medians.

Data source: Proprietary CRM data from a Costa Rica-based travel agency. All data is aggregated; no personally identifiable information is included.