Predefined (static) flows change into (personalised) smart (dynamic) flows. Hence, flexibility and the ability to easily adjust the book flow are key. Even better, make your flow adaptable to relevant parameters and start using different flows per situation.
The most obvious parameters that affect the search & book flows are:
Seasonality and occupancy rate
Personalisation of content and flow
Priority management
Preference bookings
Dynamic pricing
Demand and supply play a crucial role in the hospitality industry. During the low season and when occupancy rates are low, it is preferable to focus on conversion rates. A booking flow with as few steps as possible is ideal since every additional step increases the likelihood of visitors exiting the process.
Conversely, during the high season, a high occupancy rate is expected. Additional booking flow steps to increase upsell opportunities can be a viable strategy. Visitors are more likely to proceed with and complete the booking process. This is a subtle yet effective way to increase revenue without simply raising base rental prices.
Peaks in occupancy rates often lead to busy operational times. Scheduling flows in advance eliminates the need for manual adjustments, as they will automatically be implemented as expected.
Providing relevant information and data at the right moment significantly increases conversion rates and revenue. Personalisation is an effective method to achieve this.
Use existing data to autocomplete required fields for returning visitors, ensuring a faster and smoother user experience.
Display dynamic content based on user history, such as pet-friendly accommodations for guests who previously booked pet-friendly stays.
Offer relevant upsells, such as suggesting the same accommodation as the last stay or additional activities, while ensuring recommendations are non-intrusive.
A recommendation engine can be used to suggest relevant alternatives. The aviation industry has demonstrated that showing alternative dates, prices, and extras (e.g., upgrades) can be highly effective. Maxxton helps businesses learn from scenarios and implement conditional flows. Maxxton also develops an AI-driven recommendation engine that automatically recognises patterns and presents the most relevant options to website visitors.
Within search results, priority management can be used based on parameters like target revenue, contract priority, and plan board fitting (only when occupancy is high):
Target amount – Set dynamic revenue targets per accommodation and distribute bookings accordingly throughout different seasons.
Plan board fitting – During high seasons, reservations should be optimally placed to maximise rented days and eliminate unnecessary gaps.
Contract priority – Prioritise accommodations based on contract types to boost occupancy for specific units.
Offering guests the option to reserve a specific unit can be beneficial in some cases. The main advantages of preference bookings include increased conversion rates, additional revenue, higher retention rates, and improved customer lifetime value.
Priority management – Implement business rules within Maxxton Software, such as target amounts, contract priorities, and plan board fitting.
Historical data – Recognise returning visitors through tracking pixels, submitted booking data, and logged-in guest portal users.
Filtering by website visitor – Many visitors arrive via specific search terms, such as "6-person holiday home with a swimming pool." Predefined filters ensure relevant accommodations are displayed immediately.
Data inconsistencies – Inaccurate or delayed data from different sources (front desk, OTAs, website) may lead to double bookings.
Increased communication workload – Automated processes help reduce time-consuming calls to support teams.
Conflicting homeowner interests – Clear configurations are necessary to avoid misunderstandings in homeowner contracts and unit availability.
Offering preference bookings reduces flexibility when optimising the booking chart. Maxxton's Reallocation Engine ensures optimal use of existing bookings by enabling strategic reassignment of accommodations.
Pricing is one of the most critical factors affecting booking likelihood. Maxxton is developing a revenue management feature that uses machine learning algorithms for dynamic pricing. This approach helps increase revenue by adjusting prices based on real-time demand and other influencing parameters.
For example:
If occupancy reaches 50%, the system can trigger a 10% price increase.
If bookings slow down, lowering prices can help maintain occupancy.
Selling at discounted rates is preferable to leaving accommodations unbooked.
By adopting a dynamic pricing approach, businesses can achieve higher average revenue and overall profitability compared to fixed pricing models.
Use integrated tools and data, such as a recommendation engine, dynamic pricing, and priority management.
Adjust booking steps dynamically based on priority management, historical data, and user filtering.
Generate content efficiently with templates, drag-and-drop widgets, and dynamic content integration.
Eliminate third-party dependencies to reduce additional costs.
Display only relevant data to optimise conversion rates, retention rates, revenue, and customer lifetime value.
By implementing these strategies, businesses can enhance the user experience while achieving higher profitability and operational efficiency.
