Chosen for its potential to significantly improve the way destination marketing organizations advertise, bd4travel was awarded the Brand USA Marketing Innovation Award at the PhoCusWright Conference’s Travel Innovation Summit. The summit, sponsored by Brand USA, features innovations from travel startups to industry giants. This year’s thirty-two demonstrators showcased a fantastic range ingenuity and creativity, which made picking a single winner a challenge. A key differentiator for bd4travel was its ability to demonstrate its immediate application and impact in today’s travel marketplace.
Building Situational Awareness
Once-sophisticated features like behavioral targeting and contextual advertising have become basic elements of digital marketing. While these terms refer to different things, they are essentially tactics marketers use in pursuit of the universal goal of advertising: to serve the right message to the right customer in the right place at the right time. As technology has transformed mass media, advertising has evolved along with it – from print to radio to TV to the Internet. Digital marketing is no longer a fledgling industry, but continues to change at a rapid pace due to increased processing power and speed, which paves the way for new advertising possibilities.
This processing power is what enables “big data” and the ability for companies to respond to an individual in real time with a customized product or message. At a basic level, marketers already have been doing this for years based on a shopper’s purchase history or the history of others like that shopper. Perhaps one of the best known examples is Amazon’s recommendation engine. Based on what items a shopper views, Amazon recommends other items based on the shopper’s history as well as others who looked at that product. At the time it was launched, it was the envy of online merchandisers. Now, it seems rather simplistic.
Simple is not necessarily bad – Amazon clearly has used its recommendations with great effect. For infrequent, expensive and complex decisions like travel, however, simple solutions do not work as well. Consider the example of a city center, four-star hotel. There are a number of reasons why this hotel might be appeal to a traveler. For a business traveler, distance to the client might be a top motivator. For a leisure traveler with children, the attraction may be the babysitting services or pool. For the person traveling with friends, it may be the combination of the spa and proximity to nightlife. Simply aggregating the history of past searches to recommend alternatives creates the same recommendations to all of these different travelers – even though their priorities are quite different.
One potential solution to this challenge is to have consumers indicate what they are interested in, such as “romantic getaway” or “family fun,” through some sort of wizard, quiz or, worse yet, a long list of options to check. Consumer behavior shows people typically do not have patience for such a task, and technology has not been able to deliver against its promise well because of the subjectivity that is often involved. For example, people have different ideas of “fun” and recommendations therefore need more specific factors to work well.
Big data can change this “one-size-fits-all” approach by allowing systems to read, process and react to user behavior in real time. Our recent winner, Bd4travel, aims to achieve this goal by capturing a broad range of details about an individual’s behavior and processing it in a way that makes it valuable context for determining the most appropriate product or message to deliver. Bd4travel is short for “big data for travel,” and one of the key things we admired is its departure from CRM-based historical profiles (that use a user’s past purchase history to predict what that person will do in the future) to a situational orientation that analyzes each trip in real time. It still learns by aggregating behaviors of many consumers, but it aims to center on the “why” of the trip rather than the “who.” Ultimately, a traveler should have different travel recommendations for a family trip, anniversary celebration or girls’ weekend getaway.
How does this type of technology relate to DMOs?
While bd4travel’s technology currently is applied to product recommendations, the same knowledge and segmentation that digital retailers use has a number of relevant applications for destinations, including general intelligence, shopper retention and prospecting. The application may not necessarily be for DMO websites directly, but there is great opportunity to leverage the technology as a targeting tool for online travel agency and retail websites. (As a note, these are conceptual examples of the technology’s potential, not the company’s current or planned capabilities.)
Aggregating detailed consumer behavior on digital retail channels potentially can yield a large range of useful insights, including:
- What markets most often compete with my destination (cross-search)?
- What might be triggering cross-search with other destinations (high prices, lack of appropriate product)?
- When are people searching for my destination (by source market) and, accordingly, when is my message most relevant?
- What is destination’s motivation mix of travelers (family trips, couple getaways, etc.)
While citywide events are universally welcomed, the strain on supply can create scenarios where travelers are essentially turned away. For multi-city marketers, such as state tourism organizations and Brand USA, technology is useful to identify shoppers who look at sold-out destinations and then offer them similar alternatives. The objective is to retain a traveler who would otherwise be lost to another region. This is a great way for a larger tourism organization to add value and relevance to its constituents. As an example, if a traveler in South Korea is searching for a hotel in Los Angeles and the city is sold out, that traveler could be targeted with ads featuring other California destinations.
For the group of travelers who are actively looking at non-U.S. destinations, Brand USA potentially can target them with ads to put comparable U.S. destinations in their consideration set. In addition, for cities with strong cross-visitation, such as New York City and Paris, travelers who have visited Paris could be targeted with a message to consider New York for their next trip (even if they have not searched for New York). Alternatively, travelers who recently visited New York and are shopping for a destination in another country could be targeted with a US city that is comparable to New York, depending on what their trip profile looks like. For a history buff, it might be Boston or Washington, DC. For a partier, it might be Los Angeles or Miami instead. Or, if the traveler has a tendency to visit the same places repeatedly, they could be reminded of what they love about New York.
For more information on the Travel Innovation Summit, click here.
For more information on bd4travel, click here.