KLM’s Doubles Bookings With the Same Spend With Machine Learning
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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.”
Trading and Investment Companies will Increase Consumption of Cloud Services: Study Confirms
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While some traditional financial services companies have more slowly transitioned to the cloud, capital markets firms have embraced cloud computing across their entire value chains — front-, middle-, and back-office. We wanted to understand the dynamics behind this rapid adoption, the most common use cases, and the types of technology most in use, particularly as it relates to market data. Google Cloud commissioned Coalition Greenwich to survey 102 institutional capital markets professionals — at exchanges, trading systems, data aggregators, data producers, asset managers, hedge funds, and investment banks — in the United States, Canada, France, Germany, Italy, the Netherlands, Switzerland, and the United Kingdom.
Our research found that while there are many drivers, demand for easier accessibility is fueling widespread adoption of cloud-based market data services, and associated trading infrastructures, across the buy side and sell side. In fact, 68% of sell-side and buy-side users find it critical for market data providers to offer public cloud-based data services. At the same time, exchanges, market data providers, aggregators, and trading systems are embracing the cloud as a delivery model by offering access to data directly via their own cloud services, APIs or partners.
Here were five noteworthy takeaways from the study:
1. Cloud services are becoming ubiquitous for data delivery. Today, the cloud is pervasive, with 93% of exchanges, trading systems and data providers offering cloud-based data and services, according to surveyed executives. Moreover, 100% of those surveyed intend to offer new cloud-based services, such as derived data, in the next 12 months.
2. Commercial and investment banks are offering additional connectivity, real-time data feeds, and trading applications delivered via the cloud,demonstrating that it’s not only exchanges, trading systems, and data providers that are moving rapidly to the cloud. Internal use cases abound as well, with 67% of those surveyed consuming cloud-deployed market data, primarily for data analytics. 88% of surveyed sell-side firms intend to consume cloud-based market data services, with digital transformation, data science and quant research as the top use cases.
3. Buy side firms will consume even more cloud-deployed data. Today, 90% of surveyed buy-side firms are consuming cloud-deployed market data, mostly for portfolio management. 70% of buy-side firms intend to consume more public cloud-based market data services in the next 12 months, adding services such as compliance and regulatory reporting.
4. AI/ML, powered by cloud, is moving out of the pilot phase and into mainstream use. Today, 50% of exchanges, trading systems, and data providers are offering data products or services powered by AI/ML, and of those, 42% intend to offer AI-powered trade execution and trading analytics services in the next 12 months. Within commercial and investment banks, 55% said they are currently using AI/ML in the cloud, and while that was true for only 14% of overall buy-side respondents, 44% of large buy-side respondents are using it.
5. Exchanges, trading systems, and data providers are prioritizing public cloud for internal insights. 71% of these firms are using the public cloud, mostly for data transmission, processing, analysis, and long-term data storage. Over the next 12 months, 33% of new public cloud workloads will focus on data mining, data insights and advanced analytics, while 28% of new AI/ML tooling and infrastructure investments will focus on faster analytics and risk reviews, and 27% on data quality maintenance.
“We see new, dramatic shifts on the adoption of cloud across market data,” said David Easthope, Senior Analyst for Coalition Greenwich. “And we expect further proliferation of cloud-based services and greater consumption across the trading and investing lifecycle.”
Conclusions and future predictions
Based on the survey results, Coalition Greenwich predicts five following trends over the next 12 months:
- Exchanges and trading systems will continue to launch a wide array of new cloud-based and possibly cloud exclusive data services across derived data, end of day data, reference data and pricing data.
- Data providers will launch new data products such as pre-trade analytics powered by AI/ML in the cloud.
- Commercial and investment banks will offer additional connectivity, real-time data feeds, and trading applications delivered via the cloud.
- Buy-side firms will consume even more cloud-deployed data, including real-time market data, portfolio management data, and risk analytics.
- Exchanges, trading systems and data providers will explore proof-of-concepts around core systems on the cloud. Improvements to AI/ML tooling or infrastructure will ramp up as firms seek more rapid responses to risk initiatives.
To learn more about these findings, download our two full reports, The Future of market data: Distribution and consumption through cloud and AI and Exchanges and data providers: Prioritizing the cloud and AI for internal insights or our short infographic.
Research methodology
The survey was conducted online by Coalition Greenwich on behalf of Google Cloud from March 2021 to April 2021 among 102 executives in North America (n=82), EMEA (n=17) and other (n=3) who are employed full-time and who are participants or influencers in decisions around cloud and/or senior management with a role at a company which is an institutional asset manager, hedge fund, alternative investment manager, exchange and/or trading system, information provider, information aggregator, or other asset manager/asset owner. The survey included wide perspectives from a range of firm size and asset class focus, including equity, fixed income, FX, commodities, multi-asset, and other asset classes.
Foot Notes
1. We defined market data as direct feeds, consolidated feeds, terminal and desktop products, security and reference data, pricing data, historical data, alternative data, and index data.
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Bra Fit or Brad Pitt? Fun Moments of Using AI for Contact Centers
For many enterprises, the wish to tap into the power of AI is negated only by a lack of know-how: How AI works, what sort and how much data they will need to gather and structure correctly, and how to use the platforms that make AI adoption easier.
Google understands that. Which is why the AI solutions they have built “are basically plug and play, ready to go with our partners, so that you (businesses) can have immediate business value for your specific use case, and for your specific workflow without a deep investment of any kind into machine learning,” says Levent Besik, Group Product Manager, Google Cloud.
Among the more interesting use cases that’s seeing adoption is contact center AI.
“So one thing that we see quite often, especially from our B2C customers, is that they often have this growing pain in their call centers. They face a trade-off between operational efficiency and great customer service. With the advances in language and conversational AI, that doesn’t have to be the case anymore,” says Besik.
That’s exactly the problem in front of Akash Parmar, Enterprise Architect,. “One of the big challenge we had was: how do we effectively manage 14 million calls, which come into our contact center, stores and head office? In the past, these calls were managed by completely different platforms with their own IVRs, with their own routing and reporting solutions. It was expensive, plus the experience across them was very inconsistent.”
Digging deeper, Parmar and team figured that the challenge was in the fact that an IVR couldn’t really capture the hundreds of reasons customers call.
To get around the problem Parmar and team decided to let customer tell them why they were calling. They then used AI to decipher what the customer was saying, extract the customer’s intent from that, and then help them directly, or re-route them to the best department.
Overall, it proved to be a big success, says Parmer. Although there were funny moments.
“In our early days, we were getting some transcriptions from the Speech API, which were not what we expected. It was not word by word and we had to kind of train the model to make sense of it. For example, we started to get calls about Brad Pitt. We were like, we’ve really won the Oscar here. Brad Pitt is calling us. It was not actually call for Brad Pitt. These were calls about bra fit, which it’s a big business for M&S,” remembers Parmer.
Find out how three companies, including Marks and Spencer, are using AI today.
Le Figaro Uses Google Firebase to Personalize Experiences and Generates 3X Revenue Results
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Le Figaro, established in 1826, is France’s oldest and largest daily morning newspaper. The company’s mission is to provide timely, digestible and engaging news to their readers. As one of the first in the industry to offer digital content, Le Figaro engages their subscribers across 11 Android, iOS and web apps that cover news, sports, lifestyle and games. Le Figaro has about 22M monthly active users on their mobile and web apps and 120K paid digital subscribers.
The Challenge
In a saturated news app market, Le Figaro was looking to increase paying customers and to retain existing paid subscribers. To do this, Le Figaro’s development team needed to engage readers with personalized content at the right price point, but how could they pull it off with limited time and resources?
The Solution
Le Figaro used a number of Firebase products to retain existing users and increase paid subscriptions. They sent targeted notifications through Firebase Cloud Messaging reminding customers to follow topics and journalists they found interesting. This helped reduce churn by keeping subscribers engaged in content they valued. They also tested different subscription amounts using Firebase A/B testing, which helped Le Figaro identify the price points that led to the highest number of conversions among both Android and iOS users.
“Using Firebase has completely transformed Le Figaro’s digital business by making it easy to rapidly innovate and personalize content for our readers. With Firebase we have seen continuous increases in retention, downloads and screen time in our apps!”
Valentin Paquot, Mobile CTO, Le Figaro
Le Figaro found their biggest increase in paid subscriptions came from embedding real time interactive infographics into their mobile and web app articles. When a user added information into the infographic, it triggered a Cloud Function that accessed data stored in Cloud Firestore and returned a personalized infographic to the user in real time.
For example, in the article “Are you rich?” readers could input their income into the infographic and compare it against different income groups in Paris instantaneously. The infographics was behind a paywall and users had to subscribe to gain access.
According to Le Figaro, this infographic saw 3X the rate of paid subscription sign-ups compared to their other infographics. The team built this interactive infographic system in 3 days instead of their average time of 2-3 weeks using a traditional backend service. Using Cloud Functions and Cloud Firestore, they estimate they were able to reduce development time by 86%.
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Productionizing TensorFlow on Google Cloud with TensorFlow Enterprise
Machine learning is transforming every aspect of our lives and developers and enterprises are using ML to build impactful solutions that drive business value.
TensorFlow is one of the most widely used production-ready frameworks for machine learning and it’s open-sourced by Google so that everyone can take advantage of these powerful tools.
But if you are an enterprise trying to use ML there are some challenges you may face.
To address the needs of AI-enabled businesses, Google recently introduced TensorFlow Enterprise. It incorporates enterprise-grade support, cloud scale performance, and Google Cloud-managed services.
Watch Sandeep Gupta, Product Manager, TensorFlow, to learn how to get started and why the best way for businesses to experience TensorFlow is with TensorFlow Enterprise.
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Measuring and Improving Speech-to-Text Accuracy
Google Cloud’s Speech-to-Text API has a large number of uses including making customer service teams more effective and increasing their ability to improve customer experience.
Google Cloud’s Speech-to-Text API provides incredible accuracy out of the box. What many might not know is that it also has new tools for enhancing accuracy and customizing the model for your industry, domain, or use case.
In this video, Calum Barnes, Product Manager, Google Cloud, offers an overview of Google Cloud’s Speech-to-Text abilities, then he talks about how you can measure the accuracy of speech to text on your own data. He also discusses what you can do using Google Cloud tools to improve your Speech-to-Text accuracy levels.
Come learn how Google measures accuracy and how you can use its tools to customize your model and improve accuracy. Barnes will walk you through the basic concepts and introduce a lab that you can complete later on your time.
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