In many ways, the travel industry is one of the fastest adopters of new technologies. For example, while many marketers argue about mobile’s viability for e-commerce, players like HotelTonight have been driving bookings for years. In other ways, though, it’s amazing how long things can stay the same.
I used to run a metasearch product at a top travel publisher. I worked with advertisers to help them convert our traffic into bookers. Often, I’d hear frustrations about how there were so many metasearch sites with which to work, and each had unique processes and workstreams. Fast forward about three years from that time, and nothing really had changed. On one hand, having so many metasearch sites being viable traffic options is good for business. Unlike SEM, where Google is dominant, metasearch offers opportunity from major sites like Kayak, TripAdvisor, Trivago, Hipmunk, and others, as well as even Google, with their air and hotel search products.
On the other hand, having so many players makes it really difficult for a marketer to efficiently manage a full suite of programs. Thankfully, technologies have developed in the past couple of years to address this, much like how the SEM world came up with solutions for managing multiple search engines.
PMG uses Koddi, which is a fantastic platform. Koddi is to metasearch as Kenshoo and Marin Software are to SEM. It provides a single platform to manage programs across multiple metasearch programs. It makes it easy to toggle between multiple publishers and accounts, and develop views on performance based on whatever need you have.
Some of our favorite features and commonly used functions include:
- Heat map – This provides quick visualization of where our heaviest booking markets are, making it easy to spot unexpected opportunities, as well as identify unexpected problem areas.
- Dimensions and filters – Instead of manipulating spreadsheets and really going crazy on pivot tables, we use Koddi to quickly pull the cross-cut of data we need, which allows the team to spend our time analyzing data and driving improvements in performance (vs. reporting).
- Bulk edits – As a team, we still share war stories about working within Google’s Hotel Ads program a few years ago. The campaign management interface at the time was laughably difficult to use, and even worse to use at scale. (We estimated updating bids for 40,000 properties to take two weeks of uninterrupted – i.e., no sleep – activity.) To their credit, Google has vastly improved the platform, but it still pales in comparison to Koddi, where we can update bids by individual hotel or groups of properties within minutes.
- Automated bidding – While we would never rely solely on an algorithm to manage our bids, having this feature is a huge timesaver. We define the goals in the system, customize our thresholds, and allow the algorithm to give us unbiased recommendations to maximize revenue and profitability.
- Scheduling – We have multiple reports scheduled to run on a regular basis, usually hitting our email in the mornings. It feels great to come in and be able to jump right into building performance instead of waiting for a reporting request to complete, sometimes not until late morning. Moreover, we also schedule downloads of bid files which, if you’ve worked with some of the metasearch sites, can be v…e…r…y… s…l…o…w… With this tool, we don’t waste time waiting for something that really should be simple and straightforward.
- Labels – This is by far our favorite. It allows us to flexibly manage hundreds of thousands of hotels. Our recommendations are to group hotels by supplier chain, star rating, destination type, peak seasonality, amenities, and margin and revenue attributes, at minimum. This is a customizable feature, so you really are only limited by your imagination.
If you’re managing multiple metasearch programs, we cannot emphasize enough the value of using a tool like Koddi. With the breadth of sites out there, and the nuances inherent to each of them, you want something that enables your team to spend their time doing analysis and optimization, and not killing themselves pulling reports and matching data sets.