[PODCAST] The Travel Marketer’s Guide to Modern Digital Marketing

Hello, and welcome to ‘My Podcast’.

My name is Jim Price and I am Head of Travel North America for RTB House.

For this installment of our podcast, I will be sharing, in around 2 minutes of your time, some thoughts on Deep Learning AI and how it is changing programmatic performance marketing.

Let’s start with: what is Deep Learning?

Deep Learning is the latest evolution of Artificial Intelligence. AI has been around for decades, back to the sixties, whereas Deep Learning has been possible for the past decade – in great part because of the availability of affordable cloud computing and storage. It is a technology that mimics how our brains deal with data and problem solving. It thrives while collecting and organizing unstructured and chaotic data into new and revised algorithms to solve complex problems in ever-changing marketplace. Deep Learning outperforms its predecessor, Machine Learning, because it has more and better data to make better predictions and is constantly evolving to new market conditions.

So why does Deep Learning matter to you as a travel marketer?

Deep Learning processes 2500x more data, allowing full funnel performance marketing solutions to make better predictions and decisions about which travel shoppers are most valuable; which shoppers are most likely to convert (and not just abandon); which products to show them in an ad; which publishers to advertise on; how much to pay for that placement; how to maximize campaign reach – all the while maintaining your campaign goals.

Bottom line, we see approx. 50% increase in reaching your audience.

We see roughly a 29% increase in performance across click-through rate (CTR), conversion rate (CR), and average booking value.

There is no additional cost to you for hitting your goals.

Finally, in a cookie-less world – Deep Learning will be an even more powerful factor as we change what data is available to make decisions on “right user, right time, right price”. Having 2500x more data and being able to use that data to make better choices is going to be another game-changer.

We will be releasing another podcast in a couple of weeks – keep an eye on LinkedIn for the next installment. If you would like to continue this conversation, please DM me.

Thank you.

If you were able to listen to my last podcast, I offered up a high-level overview of Deep Learning AI. First, what it is, and secondly – I would argue more importantly – why it matters to you.

What I’d like to do in this episode, in just 2 minutes of your time, is go a little more deeply into why it matters to the travel sector.

As you know, this journey before the journey, as I describe it, is a time people take to consider and plan their trips, can take 30, 60, 90 or more days. People visit dozens, sometimes hundreds of websites. They’re collecting all sorts of information and tracking that user through that journey, through the awareness stage, through the consideration stage and down to the conversion stage, can be very challenging. But this is what Deep Learning is set to do, what it’s meant to do.

It collects lots of data – 2500x more data than machine learning. And it allows us to provide real-time and data-driven recommendations based on harnessing that data from every touchpoint where that user’s been, where we believe they will go, and then using that to create that audience. It’s something that allows us to make very precise recommendations. And higher conversion rates come from precise recommendations.

I welcome you to DM me if you’d like to learn more about this. Thank you for your time and I hope you have a great rest of your day.

Recently I was asked to provide some insights for an article on effective digital campaigns for OTAs, and it occurred to me that there are some interesting commonalities about the way OTAs bring their offerings to market and the way RTB House brings our offering to market.

The first is technology and marketing platforms.

OTAs work very hard on technology to make their customer experience as good as possible. Of course, we, as a performance marketing solution provider, also work very hard to do the same.

OTAs are constantly developing technology. It doesn’t seem like a week goes by that I don’t see some announcement about an OTA offering some new service and technology that’s going to make their consumers happy.

And we do the same, we work tirelessly to continue to evolve, most notably our deep learning technology that tremendously increases the relevance of the recommendations that are displayed.

Common goals, of course, to keep our customers happy, and help them through the booking journey.

That journey, from the beginning of aspiration to ultimately traveling can be a long one and like an OTA does with their platform, RTB House will help them find the right pathways through millions of possibilities to the single best outcome.

And then how do we go about achieving that goal? Personalization, of course, and then using the data to make that experience as good as it possibly can be.

So, if you are interested in continuing this conversation, I hope you’ll DM me and we can set a time to talk. Look forward to hearing from you and look out for my next installment coming soon. I hope you have a great rest of your day.

My topic for this episode will be about what we call multiple retargeting partners.
This can be a bit of a controversial topic, so hang on.

Notable competitors, Google and Criteo and Yahoo, don’t believe that it’s a good idea to have multiple retargeting partners working with you at a single point in time. I/we at RTB House disagree with that. We have plenty of data to support the idea that utilizing multiple retargeting partners increases the reach that you will have into your audience by as much as 50%.

And further, it doesn’t overlap as much as you might imagine, and we can control for frequency issues. So let me dive a little bit deeper into each of these topics.

As you know, as a professional travel marketer, your bids for an ad placement and retargeting solution both have to go through an internal auction and an external auction. So just by simple logic, if you’re involved in more auctions, you have more opportunities to win. If we continue to drive towards your goal, be it a ROAS goal or a cost-per-booking goal or something of that nature, you should have every confidence that you’re not going to pay for anything that exceeds that goal.

You are not buying media, you are buying leads. So that is where we come back to the idea that there’s actually surprisingly little overlap as it relates to retargeting. We see it ending up at around 20% to 25%, with some customers seeing it as low as 5%. No frequency issues, because we control for that, and I strongly encourage you to consider this as an alternative to where you might be currently.

You might be not attending to as much as 70% – 80 % of your audience, because you are not pursuing them.

If you’d like to learn more about this and debate it with me, if you like, please DM me below.

Hi and welcome to another episode of our podcast.

Today, we will talk about the Power of Personalization and why, when done right, it elevates your online travel campaign efficiency.

Please welcome Aga, our personalized marketing expert, who will share some insights.

Hi, Jim, thank you.

We see that personalization increases marketing efficiency spend dramatically – so ROAS metric goes up. We see a reduction in acquisition costs, higher retention and conversion rates, and revenue increases.

Why does personalization have such a tremendous impact?
Today I will focus on information overload.

Well, the human brain strives for efficiency.

It is easier to make a decision if the number of options is limited and all of them are relevant. So if your brain gets overloaded, it can damage the decision-making process.

Personalization in this context can be understood as an information reduction to what’s relevant for the user at a given moment in time.

Also, consumers expect personalization, and the vast majority claim that they get frustrated when it doesn’t happen. As a result, your brand gets associated with frustration which is a negative emotion.

People want brands “to make it easy to navigate” and to “get only relevant product recommendations” – in the case of travel where there are infinite possibilities of destinations or hotels – guiding travelers through to the booking is what personalization is about.

And to truly personalize – you need to automate. Travelers will search for a long time and move up and down in their hotel booking journey, and the most efficient approach is to personalize messages based on behavioral data interpreted in real time. As a result, you will need less ads served for the booking to take place, so the effectiveness of ad spend increases.

I am sure your travel organization has already mastered personalization.
So – can you do it better? Well, there is always room for improvement, so keep up with the technology and test. And I will leave you with that thought.

Thank you very much.

Hello and welcome to another episode of our podcast.

Today, I have another subject matter expert joining me, Mateusz to talk about the future of cookieless in the programmatic display space. I hope you enjoy it.

Hi. Thank you, Jim.

So a cookie is a piece of data from a website that is kept within a web browser that can be accessed later. It allows you to understand individual user interests based on their activity on your website and later recognize that user across domains to display personalized ads.

Will you be able to recognize and reach your targeted audience when 3rd party cookies are gone?

The answer is yes.

There is already a large initiative run by Google Chrome that we work together with to make it possible while protecting user privacy. As a result, users can still be observed as individuals on your website. But when they leave, they are only visible as members of the interest group. This is called a group-based targeting mechanism and it is supported by your first-party data.

We build the interest group on your website to include users fitting your campaign’s target group. Assignment criteria can vary significantly based on the technology used. It can include information such as keywords that were searched or parts of the website launched.

Simple targeting by group-shared preferences, like interest in a holiday in Hawaii, would not allow for displaying ads with the specific trips that the individual traveler has searched for.
To overcome this, we developed a concept that allows for saving the user’s product preferences in the browser on the user’s device and then using it when the ad is to be served.

None of the information stored in the browser, neither interest groups nor user preferences, can be used to re-identify an individual after they leave your website.

So how can you prepare for the cookieless future? Rethink data collection and sharing strategies and reconsider your current partnerships – making sure your partners are ready from day 1.

Thank you!

Hi and welcome to another episode of our podcast.

Today, we will talk about incrementality and how it applies to travel marketers.

Please welcome Jaysen Gillespie, head of analytics and data science for RTB House.

Hi, everyone.

Whether you’re an OTA or a specific travel brand, the concept of incrementality is critical to understanding marketing effectiveness. In both cases, you want to ensure that your marketing partners are delivering – not just bookings but bookings that would not have occurred otherwise. This is especially important as you think about the kinds of bookers you have.

You have some very high loyalty bookers that are going to book you or your suite of brands under most circumstances. So the incrementality there comes from, perhaps, the shift in channel.

On the low loyalty side, you have low loyalty, low intent folks that are more susceptible for brand marketing. You want to keep them warm, mass branding, wide reach. Stay present and relevant. The low loyalty, high intent bookers – you want to capture that booking. You want to make sure that user doesn’t defect to another booking option. An incrementality can come from, either a change in the booking channel or change in the underlined chosen travel brand.

So how is incrementality different from traditional attributed sales in systems, like Google or Adobe Analytics? Well, those are web analytics systems, and they use attribution – so they measure attributed sales. Attribution connects the booking to the ads that occurred before the booking for that same user, or the ads that some would say generated the booking.

The attributed, sales are connected to set rules such as: give all credit to the last click before the booking. The challenge, of course, is knowing whether that booking would have occurred without that specific click, or without use of that specific vendor. And that’s where incrementality steps in.

Incrementality can answer those tough questions, but it requires a careful study of two populations. You need to set up one population that gets all your normal marketing treatments and another population with the same look and feel, but where one – and only one – vendor’s specific ads are removed or not served to that second or control population. You should then see a lift in performance or bookings per user between the population that has the missing vendor and the population with the vendor understudy. This lift, this kind of one vendor control, allows marketers to determine the incrementality of one specific partner. You can then compare that to what’s in your attributed or web analytics system, and over time, with repeated tests across all your major partners, a full picture of incrementality can emerge for your brand or for your OTA.

We think that’s a very worthwhile set of tests for marketers to run. I look forward to speaking with you more about that.