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In response to a slowdown in summer occupancy, a Las Vegas luxury resort partnered with Net Conversion to implement a dynamic, data-driven growth model.
Nestled amidst the vibrant energy of the Las Vegas Strip, a premier luxury resort channeling the allure and grandeur of Italy offers guests an unparalleled experience with its world-class amenities and captivating ambiance. However, like many high-end establishments in the region, the resort grapples with a recurring challenge: a significant drop in occupancy during the sweltering summer months. This seasonal slowdown, exacerbated by the desert heat, presents a formidable obstacle to maintaining consistent revenue streams.
Recognizing this cyclical pattern and its impact on the resort’s bottom line, Net Conversion, the resort’s analytics and media agency, stepped in to address this challenge head-on. The agency’s strategic vision extended beyond merely mitigating the summer slump; the primary goal was to develop a data-driven growth model to effectively allocate media budget across various tactics and channels, thereby countering the seasonal dip and driving revenue growth even during the off-season.
Strategic Goals
- Predictive Media Allocation: Leverage key performance indicators and macro variables to anticipate fluctuations in demand and proactively adjust media spend.
- Channel Optimization: Identify the optimal mix of marketing channels to achieve channel-specific goals and uncover new growth opportunities.
- Performance-Based Budget Management: Implement a dynamic budget allocation system that responds to real-time performance data and adjusts spending accordingly.
Our overall goal was to increase bookings by +5% during the summer months.
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Implementation
A Dual-Model Approach to Dynamic Media Allocation
Net Conversion’s strategic solution for the resort’s seasonal challenges was a sophisticated, two-pronged model. This data-driven system enabled intelligent, real-time media allocation, responding to the ever-changing market dynamics. It optimized media investments by considering a multitude of factors, including macro-level indicators, brand-specific metrics, and granular performance data. The model’s power lay in translating this data into actionable insights through logic statements and conditional triggers. These statements dictated how media spend should be adjusted in response to specific market conditions or performance thresholds.
- Slow/Cut Spend Triggers: When KPIs dipped or macro trends signaled a downturn, the model triggered a reduction or cessation of spending on certain channels.
- Shift Spend Triggers: Conversely, when the model detected a surge in demand or high-potential opportunities, it triggered a shift in media allocation towards proven tactics or channels.
Proactive Reallocation Based on Predictive Insights
The first model functioned as an early warning system, identifying opportune moments to reallocate media budget across different stages of the marketing funnel. It leveraged a combination of key performance indicators to anticipate shifts in demand and trigger proactive adjustments in media spending.
- Key Performance Indicators: Metrics like search demand, expected occupancy rates provided by the resort, and historical booking patterns formed the bedrock of the model’s predictive capabilities. By monitoring these KPIs, the model could identify emerging trends and potential inflection points, allowing for timely adjustments in media strategy.
- Real-Time Trigger Mechanism: The model was designed to operate in real-time, constantly monitoring the influx of data and triggering shifts in media allocation whenever predetermined thresholds or conditions were met. This agility enabled the resort to respond swiftly to changes in the market and capitalize on emerging opportunities.
Optimizing Budget Distribution Across Channels
Once the initial model identified the need for reallocation, the primary function of the second model was to determine the optimal distribution of the newly available budget across various marketing channels, ensuring that every dollar was invested for maximum impact.
- Historical Performance Data: The model drew heavily on historical performance data, analyzing past campaigns and channel-specific results to identify the most effective channels for reaching the target audience and driving conversions.
- Channel-Specific Goals: The model also considered the unique objectives of each channel, ensuring that media spending aligned with both overall marketing goals and channel-specific KPIs. This granular approach ensured that each channel contributed meaningfully to the overall marketing strategy.
- Performance-Based Adjustment: To further enhance the model’s adaptability, a performance-based adjustment mechanism was incorporated. This allowed for the average 7-day cost to be adjusted linearly (within a range of +15% to -15%) based on real-time performance data, ensuring that budget allocation remained responsive to actual results.
By incorporating diverse variables and logic statements, the model became a dynamic, self-learning system capable of adapting to the market’s realities. It moved beyond static budget allocation, embracing a fluid, responsive approach that allowed the resort to stay ahead and make informed decisions in real-time. This agility was crucial in navigating the complexities of the hospitality industry, where fluctuations and unforeseen events can significantly impact demand and profitability.
Results
The model was implemented in June to combat the summer slowdown. Prior to fully implementing the model, we ran a series of pre-tests to ensure accuracy. These tests revealed a recommendation to shift media dollars into awareness tactics. Historically, online video had not performed well for our partner. However, given the promising results from the model and the rising popularity of Connected TV (CTV), we opted to test it as a performance channel despite its typical classification as a “reach” channel.
In conjunction with this strategic shift towards CTV, we fully implemented the model and observed remarkable results within an 8-week test period (June and July)
Voices Featured in this Case Study:
Net Conversion Team: Nivas Patel, Zac Douglas, & Alex Holt
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