This is a viewpoint by Claudia Collu, chief commercial officer for MainAd.
Collate, compare, convert. Marketers will recognize this as the customer journey for today’s connected users, who have access to a wider range of instant online research tools than ever before. And this is especially true in the travel industry, where online users repeatedly check in with a brand to secure the best deals of the moment, visiting an average of 38 sites before booking a vacation.
A reality of travel marketing today is that consumers are aware that price and availability fluctuate constantly depending on a variety of factors such as season, weather forecasts, events or amount of time remaining before departure. In this context it is crucial for marketers to be able to reach them at pivotal moments along their online journey to secure the conversion before the next-best brand beats them to it.
Tapping into traveler data – a data-driven vs rule-driven approach
The classic rule-driven approach to advertising has its limitations. Firstly, no rule applies to all cases, and secondly, the rule will always be biased depending on who created it. A data-driven approach not only reduces this bias, but it is auto-generative and remains updated.
More sophisticated methods of refining data are now making it possible for marketers to deliver far more personalized, relevant messaging based on various subsets of the data. An amazing opportunity can arise from big data generated by users’ interactions while they search for the perfect vacation – this is a powerful tool that can be leveraged to target the right customer, at the right time, with an offer they simply cannot resist.
Datasets could include: number of visits, volume of pages visited, time since last visit, position within the sales funnel, a new versus existing customer, price range, and seasonality. These datasets can be integrated with travel specific data such as route, destination, duration, type of product, hotel ratings, travel class, type of flights (direct only vs stopover), and the distance from departure date.
So how can big data help to optimize a retargeting strategy? By bringing the power of big data into predictive analytics so that patterns are intelligently identified, with possible outcomes foreseen and used to attract customers based on their own unique interactions with a brand. The end result is a higher engagement of the user with the ad. Machine learning models that produce predictive analytics deliver more precise, non-clustered targeting because the solution is data-driven, instead of rule-driven.
Dissecting the data
Research from recent MainAd analysis reveals just how useful customer data can be to a retargeting campaign. For example, due to the fluctuation in pricing, the average time it takes for a customer to purchase a flight is much lower than booking a hotel – three days versus 12 days respectively.
In addition, summer vacations tend to be booked three times more in advance than winter trips, serving as an indication to marketers that last-minute offers are more likely to lead to conversions in the winter months.
And it’s not only seasons that should be considered – the day of the week can also affect booking levels. During weekdays, consumers are more likely to make last minute purchases than on weekends, which may be due to the business travel market and corporate travellers’ ability to afford higher prices.
The research also highlights that trips are booked evenly across weekdays with weekend sales dropping by approximately 30%, and last minute offers work as the best tactic when marketing short trips. One in every two users books a vacation up to seven days before the trip, meaning promotions via retargeting the week before a consumer’s desired trip dates can be highly effective.
Where programmatic meets creativity
As with many sectors, programmatic advertising has become commonplace in the travel industry with marketers striving to accommodate increasing customer demands for instant updates on current deal availability, coupled with an extremely short attention span when carrying out activities online. Given this narrow window of opportunity, it is crucial for brands to be able to serve optimized versions of an advertisement in real-time.
As with everything, man and tech should work together cohesively to achieve the best results. This convergence is apparent in the use of machine learning models which allow brands to make decisions within seconds and serve the most relevant combination of elements in a creative (i.e. banner) to appeal to the individual customer.
It’s important to remember that a creative used for retargeting purposes does not need to be overly fussy or detailed – an ad which is aesthetically simple, aligns with the brand values, and contains just the right number of elements, is much more likely to garner interest than an in-depth ad providing too much unnecessary information.
It’s no secret that the travel industry has long relied on data to reach potential customers. But as customer experience remains a priority for brands, marketers should reassess the options available to provide the most relevant deals across multiple devices.
From refining data subsets for personalized ad targeting, understanding the marriage between man and machine for effective campaigns, and utilizing predictive analytics to achieve the best retargeting strategy – marketers can finally sit back and watch their revenues fly high.
This was a viewpoint by Claudia Collu, chief commercial officer for MainAd.
Image by BigStock.