Do Travel Advertisers Know What Color Underwear You Are Wearing?

(I’ll break the suspense up front – thank goodness, no)

Unlike, perhaps, the guy monitoring the airport security scanner, an advertiser can’t tell that you’re wearing your Batman boxers today, but they can learn surprising things that you may not expect.

Even more surprising is why they choose to know them and how they use them to sell more product.

Howard Luck Gossage once said:

“Nobody reads ads. People read what interests them... and sometimes it’s an ad.”

Clearly, the more information travel advertisers can learn about their potential buyer - past travel history, buying habits, your future buying wish list or maybe travel bucket list, the better they can tailor their content to increase your chance of looking at their ad and eventually, buying their product.

So, how do you get that information?

Consider traditional print or radio advertising...

If you are a brand looking to reach a traveling audience through either news media or magazines, you would probably investigate the media kit to see who they claim to reach.

For radio, you might search for stations that have a large audience where your targeted demographic is listening. From there, you might even select from basic statistics like circulation rate, averages for median age, household income, and so on.

All of this data is likely taken from a few random survey responses and is projected to reflect the entire reader/listener base. At that point, spending advertising dollars becomes a calculated risk.

It’s unavoidable to cause excessive waste in ad dollars spent when “blasting” a message to the masses in an attempt to seek and connect with a few qualified or interested people.

A way to reduce the ad waste and be smarter with spend is to use Targeted Advertising.

Get Targeted

“If you want to understand how a lion hunts, don’t go to the zoo. Go to the jungle.”
Jim Stengel

Travel advertisers are extremely aware of the demographic they are targeting. Past travel history and internal data resources abound with this information. The question is – how do you reach them again and, more importantly, find more people like them? Targeted Advertising takes a large part of guessing game away.

Instead of an advertiser paying for their ad to reach a million random people, they can now pay a bit more per person to reach 10,000 targeted people that are much more likely to buy. This form of advertising has actually been around, in a limited fashion, for decades. However, when Big Data and Data Science emerged, these lists are now “smarter” than ever.

So, what makes these targets more valuable? Nearly every website you visit includes a dozen different tracking cookies that gather information about your views and movement from site to site. This is why you now commonly see that product you just looked at on Amazon immediately show up as an ad on the news page you frequently visit.

These advertisers are gambling on the fact that you really were interested in booking that trip to Vegas, and just needed one more reminder to convince you of it.

This process works great for displaying relevant, but anonymous, information to a website. With only a cookie ID to go from, the advertiser doesn’t really know who they might be selling the Vegas vacation to - just that their cookie (or browser) previously visited a related product or service.

The magic starts when they combine that cookie with the identity of the viewer.

Enter Identity Matching

The missing factor of the individual identity has been the holy grail for advertisers. If only there was a way to find the person looking at my cruise line or hotel room. Matching companies are now paying website owners where a user establishes or has created a login and purchases their cookie ID to email address link.

This allows the advertiser to pull back the curtain and see that all of that the anonymous site views tracked from cookie id “dd8ba636-15b5-47c7” is actually from “Jane Doe” of Walla Walla, Washington.

This simple link is an advertising game changer. It unlocks all of the data available to a Data Management Platform (DMP) about the individual and can then be used for targeting that specific person. Make no mistake - there’s A LOT of data.

A DMP can pull together information from a credit report (not scoring) that are demographic characteristics tracked from “appending services”.

Things like.. Do you own a home? Is your car lease expiring soon? What is your political affiliation? What countries did you travel to?

These lists can include thousands of data points about a consumer and would make finding a very specific demographic for targeting incredibly simple.

This is immensely useful for niche products or luxury brands whose audiences are a small percentage of the general population.

There are many factors specific to travel marketing that make this service valuable.

A DMP can help identify a target audience by select traits:

  • Geographic Segmentation - Where is your buyer? Geographic targeting can be complicated for travel advertiser without enough information.It’s important to target people who aren’t AT the advertised location but will potentially be there in the future.
  • Demographic Segmentation - Who is your buyer? (race, ethnicity, age, gender, religion, education, income, marital status, and occupation)
  • Psychographic Segmentation - Why are they a buyer? (lifestyle elements, values, social class, personality)
  • Behavioral Segmentation – What specific past behaviors has the buyer exhibited? (purchase habits, product usage, loyalty, awareness, occasions, knowledge, social likes)

These same factors could be very useful to narrow down a large email marketing list to a smaller, relevant list to reduce spam.

And now, add some Data Science

Big Data + Data Science = Actionable Insights

We’ve all heard the term, but what is Data Science? In basic terms, it’s the use of an algorithm to recognize patterns that a human doesn’t or can’t see. Buying habits combined with history create patterns that can more easily predict who will buy, and when.

This intelligent information could prove invaluable to any online marketer.

Let’s say your company has an internal database of all past travel history for your travelers going back 10 years and you want to identify the factors that contribute to someone buying your holiday Caribbean cruise.

By getting the marketing team’s new best friend, the “Data Scientist” involved, a data science model can take input from the 200 data points about sales and buyers from the previous 100,000 cruise purchases and produce “actionable insights”, pointing to the relevant factors and ranges.

Rather than rely on intuition and experience, the output from a data science model will tell you the specific percentage and ranges for each data point (for example: location of the buyer, age of the buyer, duration of the cruise, destination of the cruise, etc.) that it contributed to the likelihood to purchase.It could tell you the key states to target and the most relevant income range bands.

These factors become a Target Segment.

What might this look like for a hotelier? A data science model could tell you that females who live in California aged 35 to 45, with an annual family income above $80,000 (from its Experian report), with 2 or more kids, who owns a dog (because their grocery store shopper’s card show dog food purchases and diapers), have a high propensity to travel in the spring and spend $150/night on a hotel room.

The brand can then create a segment for this group called “Cali Moms n’ Dogs” in the DMP. The DMP will match against an agglomeration of traits from hundreds of sources to find individuals fitting this same criteria for a targeted ad campaign.

While your company’s internal database of past travelers might have 300,000 people, and 5,000 of those are good target advertising candidates for a given campaign, matching the generated segment witha DMP could yield 500,000 highly qualified targets.

Some of this knowledge takes special processing with Big Data systems. This kind of processing functionality is way past an Excel spreadsheet and requires series server capacity and programming. As I like to say, “If it fits on a laptop, it ain’t Big Data”(sic).

Now I know who to reach, where do I find them?

Web Advertising campaigns are common features of travel marketing budgets, but the truth is that you may be missing many additional places to find your audience. They aren’t just browsing web pages, they’re on their phone, listening to music channels, and watching television.

The notion of reaching your audience everywhere they’re looking is known as “Omni-Channel Advertising” It’s screen agnostic, cross-device, and considers online and offline viewing. Reaching a specific customer on their web page, phone, tablet, and television channel is now a valid option for anyone promoting their product and is a key component of Targeted Advertising.

There are many applications for this new technology and advertising is not the only way for companies to utilize these tools. One of our internal 2018 initiatives at Rich Media Exchange is to use targeted advertising combined with data science to match our TripWriters blogger network with the brand partners who distribute rich media through our technology service.

With a blogger network of over 35,000 worldwide, we needed a way to connect our hotels, tour operators, cruise line, airline, and ground transportation partners with the right social influencer for their product.

In 2018, the Rich Media Exchange’s TripWriters blogger network will connect its existing blogger base with travel publishers, allowing brands to select bloggers with a relevant, targeted audience base to drive a brand’s message out to hundreds of thousands of followers.

Targeted Advertising and Data Science represent cost saving and reach expanding opportunities for advertisers in the travel space.

Companies can breathe new life to their existing databases and even transform them when combined with new information not previously available.

As Peter Sondergaard put it:

"Information is the oil of the 21st century and analytics is the combustion engine."

Peter Strimbu spent 25 years in IT with a focus on Integration, Big Data, Data Science, and Cloud Computing. Most recently, he ran Big Data and Data Science for a major television provider in the US for many years, facilitating audience insights and targeted advertising. He is now Chief Technology Officer for the Rich Media Exchange (, where he creates new advertising reach for thousands of hotels, destinations, and activity suppliers with tens of thousands of travel agents, bloggers and other media targets.