GOALS

• Develop smarter, more effective media buying models through data
• Drive relevant advertising
• Scale predictive modelling across all touchpoints in the customer journey

APPROACH

Combined contextual data to create a predictive model with granular layers

Activated data in real-time

RESULTS

• 40% lower cost per booking
• More than twice as many bookings at same spend
• 1.4 times higher click-through rate for test group than control

KLM partnered with Relay42, whose data management platform (DMP) empowers marketers to achieve data-driven personalisation at scale.

The Relay42 DMP works by stitching together all touchpoints and data sources (including all Google solutions), orchestrating customer journeys in real-time and then activating the unified data to drive business and user experience results.

Relay42 and KLM created a data flow setup with the Relay42 DMP at the core. Thanks to Relay42’s tag management system, all customer interactions on KLM’s website and app, as well as relevant indicators from other channels and data sources such as email, social, CRM, call center and affiliates can be tracked and synced in Google Analytics 360.

Data gathered via Relay42’s own tags and DoubleClick Floodlight tags can also be sent from the DMP to the DoubleClick platform so that relevant ads can be targeted to appropriate audiences.

In order to perform deep-dive analyses, KLM simply exports raw-level data (from DoubleClick using Data Transfer and from Google Analytics 360 through seamless integration) to BigQuery. BigQuery can then correlate site behaviour with ad impressions, which enables Relay42 to activate the data and inform the build of predictive models.

“We believe that data in combination with technological innovation is bound to make advertising smarter and more relevant on every touchpoint. By combining data sources, leveraging first-party data and activating this in real-time, advertising can turn into a personal dialogue, a rewarding one-to-one interaction instead of one-to-many push messaging.”

– Kevin Duijndam, Cross Channel Marketing Manager, KLM

A Predictive Model to Improve Display Remarketing

KLM decided to test a new approach to remarketing.

“Our goal was to get rid of irrelevant ads, as they are simply annoying”, explains Kevin Duijndam, the airline’s Cross Channel Marketing Manager.

“Our assumption was that people who fly with us often already know us, so it would be irrelevant to tell them about flying with us again. However, we were wondering when exactly someone is a ‘frequent flyer’. The more we thought about it, the more complex the set of business rules became, so in the end we realized we couldn’t just focus on frequent flyers, but instead should use machine learning to understand when ads are irrelevant.”

KLM developed a real-time buying setup to include predictive modelling. In this setup, website and app interactions are tracked in the DMP thanks to the Relay42 tag management system.

Relevant consumer behaviour can be streamed in real-time to a prediction engine developed by KLM in the Google Cloud Platform, with the outputs then streamed straight back to the DMP. From here, the DMP can activate rule-based segments based on the prediction outcome. And by syncing this with DoubleClick, ads can be served and targeted to maximise relevance.

The team tested their new predictive model to assess any gains in performance. KLM deliberately chose to measure the results in an A/B setup within a defined period rather than measuring the differences year over year or month over month. Such comparisons are less reliable due to rapid changes relating to seasonality, internal capacities and external factors caused by competitors.

Business Gains and Customer Experience Wins

In the test, KLM linked the Relay42 DMP customer interaction data to their predictive model to predict how relevant their ads would be. The setup enabled a decision to be made in real-time whether or not to serve a specific ad to a user.

Through the A/B tests it became clear that the new model in fact did generate a significant uplift in bookings. With the cost per booking 40% lower during the test period, KLM was able to achieve more than twice as many bookings at the same spend.

 The test produced wins in terms of customer experience, too. The click-through rate for the test group was more than 1.4 times higher than for the control group, indicating that the new model was successfully reaching users with messages they found to be relevant rather than annoying.

 The success goes beyond improving KLM’s display remarketing efforts, though.

“Even more importantly, we’ve laid the IT data flow foundation in such a way that KLM is now able to execute on our data through all of our digital marketing channels and apply our prediction models at scale”, Kevin says. “So we can be flexible to plug in other models but can also now scale through other online media channels like search or video.”

At Universe, we serve customers day and night and are always working to make sure they have a great experience, whether online or at one of our live events.

Our technology has to make that possible, and our legacy systems weren’t cutting it anymore. What we needed was a consistent, reliable infrastructure that would help our internal teams provide a fast and innovative ticket-buying experience to customers.

With our data well-managed, we could free up time for our developers to bring new web features to customers, like tailored add-ons at checkout.

Our team of about 20 software engineers needed more flexibility and agility in our infrastructure; we were using various data processing tools, and it wasn’t easy to share data across teams so that everyone saw the same information.

We also needed to incorporate streaming data into the data warehouse to ensure the consistency and integrity of data that’s read in a particular window of time from multiple sources. Our developer teams needed to be able to ship new features faster, and the data back ends were getting in the way.   

In addition, when GDPR regulations went into effect, we needed to make sure all our data was anonymized, and we couldn’t do that with our legacy tools.

Finding the right data tools for the job
To make sure our customers were getting a top-notch online experience, we had to make the right technology choices.

Our first step was to centralize multiple data sources and create a single data warehouse that could serve as the foundation for all of our reporting requirements, both internal and external.

The new technology infrastructure we built had to let us move and analyze data easily, so our teams could focus on using that data and insights to better serve our customers.

Previously, we had lots of siloed systems and applications running in AWS. We did a trial using Redshift, but we needed more flexibility than it offered in how we loaded historical data into our cloud data warehouse. Though we were using MongoDB Atlas for our transactional database, it was important to continue using SQL for querying data.  

The trial task that really sold us on BigQuery was when we wanted to alter a small table that had about 20 million rows, used for internal reporting.

We needed to add a value, but our PostgreSQL system wouldn’t allow it. Using Apache Beam, we set up a simple pipeline that moved data from the original source into BigQuery to see if we could add the column there.

BigQuery ingested the data and let us add the new value in seconds. That was a significant moment that led us to start looking at how we could build end-to-end solutions on Google Cloud. BigQuery gave us multiple options to load our historical data in batches and build powerful pipelines. 

We also explored Google Cloud’s migration tools and data pipeline options. Once we saw how Cloud Dataflow worked, with its Apache Beam back-end, we never looked back. Google Cloud provided us with the data tools we needed to build our data infrastructure. 

Cloud for data, and for users
Introducing new technologies isn’t always simple—companies sometimes avoid it altogether because it’s so hard. But our Google Cloud onboarding process has been easy. 

It took us less than two months to fully deploy our BigQuery data warehouse using the Cloud Dataflow-Apache Beam combination.

Moving to Google Cloud brought us a lot of technology advantages, but it’s also been hugely helpful for our internal users and customers. The data analytics capabilities that we’re now able to offer users has really impressed our internal teams, including developers and DevOps, even those who haven’t used this type of technology before.

Some internal clients are already entirely self-service. We’ve hosted frequent demos, and also hosted some “hack days,” where we share knowledge with our internal teams to show them what’s possible. 

We quickly found that BigQuery helped us solve scale and speed problems.

One of our main pain points had been adding upsell opportunities for customers during the checkout process. The legacy technology hadn’t allowed us to quickly reflect those changes in the data warehouse. With BigQuery, we’re able to do that, and devote fewer resources to making it happen. We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.

We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.

Two features in particular that we implemented using BigQuery have helped us improve the performance of our core transactional database. First, using Cloud Dataflow to convert raw MongoDB logs to structured rows under a BigQuery table, which we can then query using SQL to identify slow or underperforming queries. Second, we can now query multiple logging tables using wildcards, since we load Fastly logs to BigQuery. 

Along with MongoDB Atlas as our main transactional database, much of our infrastructure now runs as Google Cloud microservices using Google Kubernetes Engine (GKE), including the home page and our payment system. Kubernetes cron jobs power background scheduled jobs, and we also use Cloud Pub/Sub. Cloud Storage handles any data storage if any space constraints emerge. 

Our overall performance has increased by about 10x with BigQuery. Both our customers and internal clients, like our sales and finance teams, benefit from the new low-latency reporting. Reports that used to be weekly or monthly are now available in near-real time. It’s not only faster to read records, but faster to move the data, too.

We have Cloud Dataflow pipelines that write to multiple places, and the speed of moving and processing data is incredibly helpful. We stream in financial data using Cloud Dataflow in streaming mode, and plan to have different streaming pipelines as we grow. We have several batch pipelines that run every day. We can move terabytes of data without performance issues, and process more than 100,000 rows of data per second from the underlying database. It used to take us a month to move that volume of data into our data warehouse. With BigQuery, it takes two days.

We’re also enjoying how easy and productive these tools are. They make our life as software engineers easier, so we can focus on the problem at hand, not fighting with our tools. 

What’s next for Universe
Our team will continue to push even more into Google Cloud’s data platform. We have plans to explore Cloud Datastore next. We’re also moving our databases to PostgreSQL on GCP, using Cloud Dataflow and Beam. BigQuery’s machine learning tools may also come into play as Universe’s cloud journey evolves, so we can start doing predictive analytics based on our data. We’re looking forward to gaining even more speed and agility to meet our business goals and customer needs.