Navigating Seasonality in Travel Marketing: Leveraging Predictive Analytics for Strategic Success

In 2023, over 48% of Americans intended to travel during the holiday season (up over 31% from 2022). Similarly, 85% said they intended to travel at least once during the summer months.

In an industry where demand is heavily influenced by holidays, weather, and global events, understanding and adapting to seasonal trends is crucial to success. For marketers, that means adjusting tactics, messaging, and budgets during specific times of the year to meet shifting consumer needs. 

One of the best approaches to addressing seasonality in the travel and tourism industry? Predictive analytics — machine learning technology that uses millions of historical data points to forecast seasonal demand and optimize your marketing campaigns.

Let’s explore the impacts of seasonality on the travel and tourism industry and how AI-powered forecasting technology can help you get the most bang from your marketing buck.

The Challenges of Seasonality

The way demand for travel and tourism ebbs and flows throughout the year presents a number of challenges marketers, including:

Revenue fluctuations 

Seasonality is usually viewed in three categories: peak season, when demand is high; off-season, when demand is low; and shoulder season, which takes place between peak and off seasons. Revenue surges during peak season and then shrinks during off season, which can be a challenge when you have fixed and overhead costs.

Changing budgets

Similarly, demand and revenue fluctuations often lead to changes to your marketing budget. With less money to spend on marketing and advertising during off season, it can be difficult to plan and fund campaigns throughout the year. 

Off-season promotion

Promoting a warm beach destination to Americans during a frigid holiday season might be easy — but what about during months like April and June? Marketers often struggle to convince their audience to travel or book during less-popular times of the year.

Adapting your strategy

Certain marketing tactics tend to be more effective during peak season, while others work better during off season. Between testing and measuring different channels and creative, finding the optimal marketing mix can be an arduous process.

Keep in mind, the travel and tourism industry is becoming increasingly competitive — digital ad spend in the U.S. is expected to grow 16.5% in 2024. As the market expands and evolves, the challenges caused by seasonality will follow suit. 

A Solution for Seasonality: Predictive Analytics

Sure, travel companies can work to combat seasonality challenges on their own by doing things like sourcing more online reviews or implementing a loyalty program. But a more powerful and impactful solution lies in predictive analytics, a tool used to automatically forecast seasonal demand and optimize your marketing campaigns.

Predictive analytics work by utilizing historical data, statistical algorithms, and AI/machine learning techniques to identify the likelihood of future outcomes. In the travel and tourism industry, marketers can leverage predictive analytics to intricately explore customer data, including previous travel preferences, search patterns, and feedback. Using this information, they’re able to craft highly personalized travel content featuring education or awareness messaging, packages, promotions, and offerings that address individual needs.

In short, predictive analytics help marketers place ads in the right place, at the right time and allocate their budget more effectively. That means better campaign results at a lower cost.

Predictive Analytics in Action

Even during off-peak travel periods, marketers need to maintain a strategic, in-market approach to keep consumers engaged and nurtured throughout the marketing funnel. This ensures a seamless customer journey, all the way from awareness to conversion and loyalty. 

Here are some examples of how predictive analytics can assist travel marketing throughout the sales cycle:

• Campaign optimization. Enhanced lead scoring and customer segmentation allow you to target people who tend to travel more during a particular time of the year.

• Dynamic pricing. Predict the highest price at which a given customer is likely to buy based on variables like market demand, available inventories, competitor pricing, seasonality, and more.

• Intelligent, personalized recommendations. Promote relevant experiences based on a user’s previous interactions on similar websites, the past purchases of similar customers, and time of year. 

Gain a Competitive Edge in a Growing Market 

The travel and tourism industry is inherently volatile, which makes it that much more important for marketers to have a clear understanding of market trends and consumer behaviors. Predictive analytics take the manual work out of identifying those trends and adapting your strategy accordingly in order to maximize ROI. 

Take your travel and tourism campaigns to the next level with AdRoll. Learn more today.

Last updated on February 13th, 2024.