"Using data as a prediction model is a real gamechanger."
José Fernandez is chief of Marketing and chief of Digital at one the oldest family-owned businesses in the world: car distributor D’Ieteren. José has a knack for creating great customer experiences using data. He’s seen many major transitions in digital innovation throughout his career. We talked to him about the rise of data, and what he considers his breakthrough moment.
“For a long time, data was mostly used for reporting, until we discovered you can use it to predict client behavior. That transition to a prediction model was a true revelation to me, a real gamechanger.”
With a master’s degree in commercial engineering, José’s career path has taken him through many companies and domains. His dense resume harbors enough experience to fill three careers. He brought sales and marketing to the digital age at Vandemoortele, joined CRM firm Actito and witnessed the entire lifecycle of a successful start-up, became CEO of digital marketing pioneer Digitas, and was chief commercial officer at media concern Dentsu before landing his current job at D’Ieteren. He now leads a team of 80 people that covers all things digital and data in customer service.
How important have data and AI been?
“I’ve never been tied down to one specialization or sector. I always followed the trail of innovation, regardless of the industry or domain. Every company I joined was at the time on that tipping point of having a new technology disrupt the industry. Naturally, data and AI have heavily influenced my decisions, especially in the last decade.”
“I guess I took the first step towards data during my time as an associate partner at CRM start-up Actito. Back in 1998, we built websites in HTML without having any means to measure customer experience. At Actito, we started collecting customer data to reshape customer care, or what we nowcalla CRM(customer relationship management)system. This was at the very beginning of the digital age. We quickly realized that user experience would become immensely important, so we started collecting data to optimize our UX. We actually had an entire creative team interpreting the data and translating it into a better user experience. It was quite innovative at the time.”
How did this first experience with data power your next steps?
“After Actito?, I joined LBI, a global marketing and technology agency. I applied the lessons I learned at Actito, and we founded a creative team that focused on how to visualize data for customers. So from collecting and interpreting, we went to visualizing and presenting data. The focus on user experience remained, as this was around the time smartphones became omnipresent.
“I became CEO at an LBI spin-off: Digitas. They build digital platforms and apps for major players like NMBS, Thalys, Nationale Loterij, Nissan and Toyota. The real eye-opener here was the enormous amount of data we collected. I thought the creative agencies owned the most data. What I realized at Digitas, which is part of the Publicis Group, was that the publishing world was in control of the biggest data sources. Every single screen of just one of those apps was an enormous source of consumer data. This was really a tipping point in the evolution of data: we built data warehouses, started using SEO, SEA and optimized budgets through data.”
Was this a breakthrough moment?
“The endless possibilities were indeed very exciting. We made an app for high-speed rail operator. Their previous app was purely transactional: you could buy and present your ticket. That was it. We redefined the customer journey and added extra services based on geolocation: the possibility to directly order a taxi in the app or launch a satisfaction survey the moment the customer arrives at their destination. We transformed this transactional app into a completely new model to generate revenue and allow partnerships, all through the collection of location data.”
“The biggest breakthrough presented itself in my next endeavor. Because I was convinced that the media industry held the future of data, I made a move away from digital and creative agencies and joined Dentsu, which was one of the largest media and advertising concerns in the world. They were sitting on an enormous amount of data, but it was mostly used for reporting.”
“Back then, data was still a buzzword for Dentsu; they didn’t have an actual data team. We gathered a group data scientists to do more with the raw data we had, much like we did at Digitas. That’s when we found that it can be used to predict customer behavior. It was a total gamechanger. Dentsu became pioneers in programmatic trading, where advertising budgets are optimized in real-time. Our strategy was built on analysis, prescription and prediction. By creating algorithms in the analysis phase, we succeeded in building a prediction model for advertisers.”
So, what instigated your move to D’Ieteren?
“Although programmatic trading is still valuable, more and more advertisers own data and do the data science in-house. It became harder for a generalist agency like Dentsu to make an impact. That’s why I moved to D’Ieteren, which owns and processes data themselves. I wanted to go where the data was and where I could create more value for customers.
‘As you know, D’Ieteren is an enormous company, with a global revenue of about 1 billion euros. Our core business is positioning the manufacturer’s products (the cars) within the market. The task is twofold: we take care of the logistics of distribution and the marketing. We find the right target audience for a new car and market the product. We are responsible for digital conversion through online and e-commerce platforms. With the exception of Porsche, all D’Ieteren brands are available to buy online without having to go to a dealership.”
How does D’Ieteren use and apply data?
“We collect two types of data: consumer data and operational data. The customer data is every piece of data that is linked to a consumer. D’Ieteren has about 1.6 million B2B and B2C clients. We use data analysis and prediction to understand our clients better and reshape the customer experience from start to finish. A team of 6 data scientists are building algorithms and developing machine-learning applications. One of the projects we’re currently working on is the car configurator. Aprediction model helps us optimize this tool to make specific recommendations.”
“The operational data helps us optimize our margin on imported cars, calculate investments, enable dealerships to calculate optimal prices... We use Microsoft Power BI to present this information in dashboards. Let’s say a Volkswagen dealer is selling a car. They can easily calculate whether a 7% interest or some free options are more interesting, both for the dealership and the customer."
“We are also in the midst of stepping up our digital transformation and increasing our data use to help shape the mobility of the future. D’Ieteren is part of an ecosystem made up of 8 start-ups offering mobility solutions, including Poppy (carsharing), Leasy (leasing), Skipr (full digital integration for company cars) and Lab Box (electrification). Our strategy is based on 4 trends: decarbonification, car connectivity (as the saying goes: a car is a smartphone on wheels), autonomous driving and the sharing economy.”
Is the self-driving car the most exciting innovation in the automotive industry right now?
“I’d say so. The fact that we can use data to enable cars to drive autonomously is phenomenal. The future of mobility is very exciting. Many new developments are on the brink of breakthrough thanks to data science – just consider Amazon delivering packages with drones. It seems very sci-fi now, but in 5 to 10 years we won’t even question it. Of course, this poses some ethical questions as well.”
Which issues concern you?
“If an autonomous car has to choose between running over a child on the street or protecting its passenger, how do we program its response? This is an important question. A second issue is privacy. The Cambridge Analytica case, where social media data was used to influence US elections, proved that using data can have severe political consequences. Killing machines concern me too. We could fight wars with robots, thanks to data. It seems farfetched, but Google recently decided not to offer AI services to the military anymore. We need to tread it carefully.”
What is your vision on how to tackle these ethical questions?
“I don’t have the answer. We will need strong regulation, but who will define the rules? Is it the government? The industry itself? The truth is that we don’t know what AI is capable of yet. It might even help us to figure out these issues. Take the COVID-19 vaccination strategy, for instance. If it were up to me, I would run an algorithm and let it identify the most effective strategy. We will see more of these ethical dilemmas solved by AI in the next few years. But it’s a sensitive issue, of course. At what point do we accept that our lives are shaped by non-humans? I believe we’re in the first hour of the first day of the next 1,000 years. I’m happy to be in the front row.”
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