Hotel Reservation Analysis

A property management software company needed insights into customer booking behavior to optimize revenue and improve digital services.

Key questions included:

  • Which booking rates do different customer segments prefer?
  • What drives online check-in adoption?
  • Which guest segments generate the highest revenue per occupied space?

Executive Summary

Booking Rate Preferences

  • Male guests prioritize flexibility (58% choose Fully Flexible rates)
  • Female guests are more price-conscious (higher non-refundable rate adoption)
  • Business segments show stronger patterns than demographics

Online Check-in Challenge

  • Critically low adoption at 5.92%
  • OTA guests most likely to adopt (9% rate) vs. corporate guests (2% rate)
  • Unknown guests never use online check-in (0% adoption)

Profitability Insights

  • Female Leisure travelers most profitable ($63.46 per night per capacity)
  • Unknown FIT travelers occupy significant space with minimal returns ($13.47 per capacity)

Data Schema

Rates data table

Column Data Type Description
rate_id STRING Primary key, unique identifier for each rate type
rate_name STRING Full descriptive rate name
short_rate_name STRING Abbreviated rate name for analysis
settlement_action INTEGER Rate settlement behavior indicator
settlement_trigger INTEGER Trigger condition for rate settlement
settlement_value DECIMAL Settlement calculation value
settlement_type INTEGER Type classification for settlement processing

Reservations data table

Column Data Type Description
start_utc TIMESTAMP Reservation start date/time
end_utc TIMESTAMP Reservation end date/time
created_utc TIMESTAMP Booking creation timestamp
night_count INTEGER Total nights booked
night_cost_sum DECIMAL Total revenue for stay
occupied_space_sum INTEGER Total capacity units occupied
guest_count_sum INTEGER Total number of guests
rate_id STRING Foreign key linking to rates table
gender INTEGER Guest gender (1=Male, 2=Female, 0/NULL=Unknown)
age_group INTEGER Guest age category (0=Unknown, 25=25-35, etc.)
nationality_code STRING ISO country code
business_segment STRING Distribution channel (ie "OTAs", "Leisure", "Direct Business")
is_online_checkin BOOLEAN Online check-in usage flag

Assumptions and Limitations

  • All reservation states included: Analysis includes cancelled reservations under the assumption that booking intent existed regardless of cancellation reason
  • No duplicate validation performed: Assumes dataset has been pre-cleaned by data platform/engineering team with no booking ID duplicates
  • Nationality analysis limited: Only included countries with 40+ bookings for reliability

Booking Rate Choices by Customer Segments

Gender-Based Analysis

Our hotel serves a male-dominated customer base with significant unknown demographics:

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Rate Preferences by Gender

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Male guests prioritize flexibility, while female guests show more price sensitivity. Unknown guests follow entirely different booking patterns, likely representing corporate or agent bookings rather than individual travelers.

Male Guests - Flexibility Focused

  • Fully Flexible rate dominates at 58% of male bookings
  • Clear preference for maximum booking flexibility over discounts

Female Guests - Balanced Approach

  • Fully Flexible rate leads at 47% but less dominant than males
  • Non-Refundable rates at 18% - significantly higher than males
  • More price-conscious, willing to accept restrictions for better rates

Unknown Gender - Early Planning

  • Early-60 days rate dominates at 50% - dramatically different pattern
  • Fully Flexible secondary at 27%
  • Suggests advance corporate booking or travel agent reservations

Age Group Analysis

60.78% of bookings have unknown age data, limiting the reliability of age-based insights.

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Rate Preferences by Age

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While all age groups prioritize flexibility, younger travelers show highest price sensitivity, while older travelers prefer advance planning discounts. However, the large proportion of unknown age data (60.78%) limits the reliability of these insights for business decision-making.

Consistent Flexibility Preference Across All Ages

  • All age groups prefer Fully Flexible rates (44-55% within each group)
  • Young travelers (0-25) most price-sensitive: 24.36% choose Non-Refundable rates
  • Middle-aged travelers (25-45): Balanced between flexibility and discounts

Data Reliability Limitations Age groups 55+ have insufficient sizes for reliable business insights:

  • 55-65 group: Only 65 total bookings
  • Over 65 group: Only 16 total bookings

Pattern suggestions for these groups (older travelers preferring early booking discounts) cannot be considered reliable for business decision-making.

Nationality Analysis

Our hotel attracts a diverse international clientele, though 43.82% have unknown nationality:

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Rate Preferences by Nationality

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With 44% unknown nationality data and several countries having small sample sizes (46-72 bookings), insights for smaller markets should be considered preliminary. Business decisions should focus on the larger markets (US, GB and DE).

European Guests - Flexibility Seekers

  • German guests lead flexibility preference at 71% Fully Flexible (154 bookings)
  • Czech guests at 76% (67 bookings) and Slovak guests at 64% (72 bookings) Fully Flexible
  • British guests show high flexibility demand at 65% (187 bookings)

US Guests - Balanced Value Approach

  • Fully Flexible preferred at 45% but significantly lower than Europeans
  • Higher price sensitivity: 16% choose Non-Refundable rates
  • Unique preference for Direct Booking rates at 8% (highest among all nationalities)

Business Segment Analysis

Business segments are relatively balanced across our hotel's distribution channels:

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Rate Preferences by Business Segment

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  • OTA channels drive flexibility demand - guests pay premium when unable to contact hotel directly
  • FIT travelers most price-sensitive - plan ahead for discounts
  • Corporate segments balance flexibility with advance planning
  • Leisure travelers show most diverse booking patterns

Online Check-in Analysis

Overall Adoption Challenge

Online check-in adoption is critically low at just 5.92%, indicating significant barriers to digital adoption or limited system availability:

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By Business Segment

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OTA Channels Lead Digital Adoption

  • OTAs: 9.07% online check-in rate (562 total bookings)
  • OTA Netto: 7.80% online check-in rate (551 total bookings)
  • Leisure: 8.42% online check-in rate (499 total bookings)

Traditional Channels Show Resistance

  • Direct Business: 2.20% online check-in rate (318 total bookings)
  • FIT: 0.97% online check-in rate (516 total bookings) - surprisingly lowest
  • Film: 0% online check-in rate (55 total bookings)

By Gender

There's a consistent low adoption accross genders:

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Unknown guests never use online check-in, likely representing corporate bookings or travel agent reservations where end guests handle their own check-in.

Online Check-in by Weekday

Saturday shows the highest online check-in adoption rate at 12.33%, though this is based on a small denominator of only 146 total bookings.

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The small denominator (148 total online check-ins) makes detailed analysis unreliable:

  • Saturday's high rate based on only 18 online check-ins
  • Daily variations likely represent statistical noise rather than meaningful patterns
  • Not enough data for confident business decisions

Average Night Revenue per Occupied Capacity Analysis

Methodology

Average night revenue per occupied capacity calculated as:

(night_cost_sum / night_count) / occupied_space_sum

This metric provides the average night revenue per single occupied capacity unit (bed/space), normalizing for both stay length and room capacity to enable true profitability comparison across guest segments.

By Gender

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While female guests show the highest revenue per capacity, they represent only 14.4% of total bookings (360 out of 2,501), making this the smallest size among the 3 gender segments. Male guests drive volume with 51.8% of bookings, while Unknown guests represent 33.8% with significantly lower profitability.

Cross-Segment Analysis: Gender × Business Segment

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  • Male OTAs and Male OTA Nette appear as the largest bubbles in the high-volume, high-profitability quadrant.
  • Female guests consistently outperform across all business segments despite representing smaller booking volumes.
  • Unknown segments systematically underperform, particularly in FIT and Film channels.

Most Profitable Guest Segments

  • Female Leisure travelers: $63.46 per night per capacity (72 bookings, 2.88% of total bookings, $38.7K total revenue)
  • Female OTA guests: $62.82 per night per capacity (77 bookings, 3.08% of total bookings, $41K total revenue)
  • Male OTA guests: $59.24 per night per capacity (477 bookings, 19.07% of total bookings, $282K total revenue)

Least Profitable Guest Segments

  • Unknown Film segment: $4.18 per night per capacity (16 bookings, 0.64% of total bookings, $18.3K total revenue)
  • Unknown FIT travelers: $13.47 per night per capacity (340 bookings, 13.59% of total bookings, $68.3K total revenue)

Conclusions and Recommendations

Data Quality Improvements

  • Improve demographic data collection: 34% unknown gender, 61% unknown age limits analysis reliability
  • Enhance nationality capture: 44% unknown nationality prevents market-specific strategies
  • Focus on business segment data. Most reliable and actionable for decision-making

Online Check-in Priority Investigation

  • System usability review required: 5.92% adoption suggests fundamental barriers
  • Partner with OTA platforms for promotion (highest current adoption at 9%)
  • Investigate corporate booking processes - Unknown guests represent 34% of bookings but 0% online check-in

Revenue Optimization Strategy

  • Target Female Leisure segment for premium offerings (highest profitability)
  • Reevaluate FIT channel strategy - 21% of bookings but lowest revenue per capacity
  • Optimize OTA partnerships - balance volume (Male OTAs) with profitability (Female segments)