Automakers love millennials, and this is not a surprising fact since they work and are able to pay for their products. Auto market is looking for innovative ways to reach this group, and it is not easy. In our report, we have tried to combine the results of our large analytical study of the data of 1M real Millennial trips and recommendations concerning marketing campaigns. They use our Connected Car solution, which indicates that they are drivers who are open to using technology. We have analyzed their behavior in order to help marketers at automakers and dealerships understand how to work with this audience, what to rely on when developing a marketing strategy, where to advertise and what to do in order to gain a repeat customer.

We did not put forward any hypotheses, but we simply asked questions whose answers would allow us to make proposals about how automakers and dealers should conduct their marketing activities. We formulated our proposals concerning measures to promote dealers and automakers on the basis of the results obtained.

I am happy to share with you the top 10 insights that we have found by studying the driving behavior of Millennials.

• Trips that are completed at an average speed of over 100 kph are mostly recorded in Spain (3%), compared to Germany and France (about 1.5%), and the UK and Italy (less than 1%). If you have information about the speed that your customers are traveling in your toolbox, you will have a more complete profile of your customer. I will keep the intrigue and offer you to download our full report to find out who are the most likely to have lead feet in Europe.
• Next, we decided to look at the peaks of millennial activity during the day. We made the charts demonstrating the number of trips that are taken at different times of the day by country. Do you know who those early raisers are on the roads? In France, drivers wake up early and are already on the road at 6 am. This time represents the first peak. The next activity stream is observed at 8:30 am. Peaks of driver activity allow us to contact our customers in time, offer them a particular advertisement in time, as well as to properly set up service notifications as well as any other types of advertising.

• Some automakers and dealers have noticed that millennials have a decreased desire to drive a car. We decided to see how millennials used the car during the day, and then we tried to see if there were any patterns in how they used the car during the week, month, and year. Here I can say that on average, millennial drivers drive more between Thursday and Saturday than on other days of the week. They drive much more on Friday than on the other days of the week.
• In talking about the average amount of time that millennial drivers spend behind the wheel by season, we concluded that Millennials prefer not to travel by private car in the summer.
• Together with activity peaks that vary depending on the season, we decided to investigate whether millennial drivers go through periods when they do not use their cars. And it turned out that millennials, on the whole, do not tend to take long breaks from using a car.
• Our report is primarily focused on players in the automotive market, so that is why we decided to help dealerships by providing them with information about car dealership visitation by millennials. As a result, we found out that, on average, millennials go to the dealer once every 3 months. A total of 25% go to the dealer once a month. And 10% visit once a year or less. Also, we analyzed the correlation between trips to the car dealer on holidays and weekends, and we have broken down the statistics by country to obtain a more meaningful result.
• We also found it interesting to understand do millennials prefer big city parking lots or on-site parking at their buildings? It seems to us that information about parking could be useful to those who wish to place offline ads. Millennials mostly park spontaneously, i.e., we have learned that they spend quite a lot of money on city parking.

• When formulating questions based on the data, we understood that marketers not only at automakers and dealers, but also every company that does business with the millennial generation could draw the greatest insights from precisely this part of the study. So we decided to research what destinations do millennials visit most frequently. Based on the information obtained, it can be concluded that millennials use their cars most of all to visit places associated with food. They drive their cars to restaurants and bars.
• We also decided to assess how aggressive millennial drivers are. To do this, we used Remoto Scoring AI, which assesses the aggressiveness of drivers based on the telematics data that is collected from their trips. So according to the scoring results the average millennial is a more aggressive driver than the typical older driver.

Don’t wait around. Start building a sound marketing strategy and improving your communication with millennials, the most active audience of consumers with our report “Millennials on the Move”. Complete the form here to get your free copy.

Millennials on the Move: top 10 insights about millennials driving behaviour based on 1M trips was originally published in Bright Box — Driving to the future on Medium, where people are continuing the conversation by highlighting and responding to this story.

All dealers need to change: top 2 trends from the biggest dealership event

The dealership business is currently under threat. The threat consists in the fact that automakers are redefining the place of dealerships in the sales and after-sales service chain. And the dealer has a smaller and smaller role to play.

At NADA 2019, which I attended together with Bright Box CEO Ivan Mishanin, this problem was emphasized in particular by the Association’s new chairman, Texas dealer Charlie Gilchrist: “If you look at all of us, overall, the profitability of our new vehicle departments is under attack. It’s a real serious issue. If we can sell cars profitably, we can take care of our customers.”

Dealers today really do need to change if they are to stay afloat and not lose their business. And they are trying to do it. Here are the two trends that we noted at the main dealership event of the year in the US.

First of all, dealers have made excellent progress in the field of automating lead generation. On the one hand, internal processes for internal automation have been created, including inventory management and the automation of internal procedures and sales.

Thus, it turns out that we are able to generate a lot of data. On the other hand, the car sales channel is shifting more to online. Correspondingly, lead generation is shifting more to online, too.

Vendors have identified opportunities to combine these needs. They have combined the data, and they are using it to offer dealers analytics to automate the lead generation process in order to organize online sales of new cars and services as effectively as possible.

Secondly, dealers are currently focused on after-sales service. They understand that the margins on sales of new cars are declining, especially in the United States. Car sales levels are themselves decreasing. And the time has come for dealers to concentrate on service, including for used cars, and to do it better online, including through the use of mobile online platforms.

Therefore, dealers are now introducing systems that will enable them to make this distribution channel and lead generation channel as efficient as possible. But we must understand that online the dealer’s task is still to bring the customer to the dealership and then to work with him there.

Both trends are tied to automation and digitalization, areas where telematics are indispensable. At NADA, we noted that dealers demand solutions based on telematics. They especially want systems to be integrated with internal dealer systems that are already mature enough to support such integration.

All warehouse processes have already been solved internally, and customer databases have been created. Thus, the customer can be identified by phone number, and you can see their service history. All of this data is linked together with telematics, analyzed, and then certain services and accessories are upsold to the customer automatically based on artificial intelligence. Telematics is also extremely necessary in warehouse management and operational efficiency processes, because it is important to know where a particular car is in stock, its condition, and whether it is fueled up.

Bright Box, together with Zurich Insurance, is offering dealers in the United States a powerful telematics platform for all of your inventory. With our solution you can boost customer retention and sales, leverage your customer data, and automate your inventory management. Everything that the dealer needs today, to change in line with the times. After all, if we take the development of electric vehicles into account as part of such long-term trends, the reliability of cars is increasing, which means that service revenue will decline over the long term. Obviously, services will become increasingly important for dealers, which means they need to change.

All the dealer needs to change: top 2 trends from the biggest dealership event was originally published in Bright Box — Driving to the future on Medium, where people are continuing the conversation by highlighting and responding to this story.

Last weekend we — Bright Box team — supported our first big hackathon by providing the anonymous driving history of 1000 millennials from 6 countries for participators to retrieve and share valuable insights and to turn them into products or services for the Challenge organized by Zurich Insurance and Esri. The challenge from Zurich Insurance & Esri was named «Millennials on the Move». The idea of the challenge was in by the anonymous data from Bright Box should helping to find out the driving behavior of the millennials and how it can help improve and protect the lives of millennials on the move. Coders also got access to ArcGIS; a complete mapping and analytics platform for developers: use location intelligence to the advantage and combine demographic and lifestyle data with millennials’ movements. And I want to say that we had a blast!

The workshops

Day 1 was fully packed with workshops from 20 big companies. They addressed the subjects like safety, create new tech-driven insurance solutions, sustainability, battling fake news, GDPR.

The attractiveness of our workshop was that participants could work with real anonymized data. We are focused on empowering the business of our customers through the value of connected vehicle data. And we hoped to find among the participants of the hackathon who will be interested to make automotive data as valuable as possible to drivers. And we did. 80 hackers were with us to the workshop «Millennials on the Move».

Research shows that millennials are horrible drivers. Texting, emailing, chatting behind the wheel, running red lights, eating and drinking are just a few of the reckless acts millennials do while driving. The first challenge was to retrieve and share valuable insights about how millennials really drive and how new solution can help improve and protect the lives of millennials on the move. And the second aim was to turn the insights into products or services.

During our workshop, we showed what specific date sets participants will work with. One year’s data on millennial drivers compared with the all-age group at the same time, in the same city. And we provided access to ArcGIS to help rediscover all the ‘behind-story’ data given any GPS location and time stamp, such as demographic and lifestyle data. Participators of the workshop asked about the availability of accelerometer data, what is the frequency of the data (interval between points and time). As a result, our challenge was attractive for 8 teams. And it’s definitely a success.

Developers are rock!

It was so exciting to see all those cool projects built in a little over 40 hours! By the end of Hackathon, 8 teams had a working proof of concept and presented their hacks at challenge «Millennials on the Move». Some of them were very prepared and already had the demo version and published the code on GitHub.

Below you can find the overview of some most interesting of alls by our opinion.

CAR TaiLOR

Car tailor uses advanced analytics to aggregate behavior as well as personality data from various sources. These aggregated user profiles allow us to recommend suitable products, that match the lifestyle and personality of customers.

• GitHub Repo
• tailor.scapp.io

SafetyMatters

The app has two parts. One alerts the authorities of dangerous junctions where many people have sharp changes in speed and have many car accidents in that area. The second part uses a personalized learning algorithm that learns where the driver needs to be alerted before places he is driving in an unsafe manner. This gives a comprehensive solution to risk mitigation of car accidents.

Safety Matters Demo

Safetify

A prototype app uses a machine learning model that takes traffic, driver, road and weather data into account, predicts how dangerous a particular road segment is and alerts users. The probability of a segment to be a danger zone was computed with neuronal networks. The area of the danger zone was computed by using Gaussian Process Regressot to smooth out the probabilities and provide a contour.

• GitHub Repo

Savfe

The app detects whether the vehicle owner is driving and looking at the smartphone. If he in the app his screen will start flashing warning colors (yellow and red) depending on how dangerous the situation currently is. If he is distracted by another app, the app shows a message so that he should take care. If the vehicle owner didn’t use his device while driving, that’s great! And the app will reward him for it with a special currency. And he gets even more of it if he manages to ‘uphold your streak’ and doesn’t use his smartphone for consecutive trips. And he can spend his points to get back a part of his monthly insurance cost.

• GitHub Repo

And our workshop “Millennials on the Move” made by Esri, Zurich, and Bright Box, was won by four ladies with the project “Safety Matters”. Girls power!

The hackathon was the first for the Bright Box. And it was amazing. This event for us wouldn’t have been possible without the invitation of Zurich Insurance Group. And we are grateful for the opportunity to test such creative ideas on our data that once again proves their value.

Hackathon #HackZurich: what useful solution can you code for drivers in 40 hours? was originally published in Bright Box — Driving to the future on Medium, where people are continuing the conversation by highlighting and responding to this story.

As the process of transforming data into currency, monetization can take the form of the direct sale of data. There is also indirect monetization — the process of extracting benefit by way of business optimization, i.e. using data to fortify and build partnerships, reduce risk, and increase financial efficiency.

Almost every business has the ability to monetize its data. It doesn’t have to involve the literal sale of that data.The Stanford Marshmallow Experiment proved that choosing delayed rewards can lead to greater benefits.

Why is selling data ineffective?

There are only few companies that are interested in buying data right now. The main buyers of data are insurance companies that can use data as a basis on which to develop certain products with dynamic pricing, where the price depends on specific vehicle telemetry parameters. These could include how fast a driver drives, whether they drive in the city or the country, how aggressively they drive, and how carefully they follow traffic laws.

However, aside from insurance companies, I don’t think we can find very many other firms that would be interested in paying a significant amount of money for our data. So, even with all their connected cars, car manufacturers can’t make as much money from their data as they’d like to unless they sell it to insurance companies. And, given the institution of the GDPR and the social tension surrounding the topic of data as a whole, selling data just doesn’t seem very attractive. So, instead of selling data, establish partnerships. We know that car manufacturers strive to improve drivers’ quality of life. By creating an ecosystem that attracts third-party service suppliers and exchanging data with them, we can improve our customers’ quality of life. Thanks to the monetization of this ecosystem, a car manufacturer will be able to make money off of data. This is the delayed reward from the Stanford Marshmallow Experiment.

An automaker can’t satisfy all of its customers’ needs and make full use of anonymous data by itself. However, if it has an ecosystem in which it can work with outside partners, data monetization can really take off. For example, if a pizzeria that a driver frequently goes to gets data about the driver’s location just as he’s driving by, the driver will get an offer to get a discount on a pizza if he picks it up right then and there, hot and ready to go. This is possible because the car knew that the driver would drive past the pizzeria in 30 minutes. Consumers are willing to pay for that kind of service. And companies such as pizzerias are willing to pay for it too. This isn’t just selling data. As a business model, “just selling data” is doomed to extinction.

How can an automaker organize this ecosystem properly?

To create this ecosystem, a car manufacturer needs to rely on the experience of vendors that create similar partnerships. For example, social networks with lots of apps, smartphone manufacturers with their stores, or banks with their loyalty programs and satellites that are willing to give customers cash back and points. Today there isn’t a single automaker with a long-term loyalty program because motivating someone to buy a second car is difficult and expensive. But what if we make someone loyal within their car’s lifespan and bring them into an ecosystem while making a profit off of transactions within that ecosystem? That’s something a car manufacturer can do, but there are certain risks.

First of all, there’s the risk of threats to cyber-security and the theft of personal data. In order to defend against this, you’ll have to take standard steps such as constantly improving the level of your system’s security and performing penetration tests.

There are also organizational risks, since there will always be certain dishonest players within the ecosystem that could use users’ data for their own illicit ends. I should note that users always consent to the use of their personal data. And if, in the case of social networks, people didn’t appreciate the significance of the box they were checking, when it comes to their car people will pay much closer attention to their data and will only give their consent if they find the service truly necessary and useful.

There’s also the risk of having a small number of players in the ecosystem. If you want to attract a lot of players to the ecosystem, you need to have a lot of users, but if you want to have a lot of users, you need to have a lot of players in the ecosystem. This is where automakers can focus on something specific. Our recommendation is to start with the after market. In other words, connect people not to cars that just came off the conveyor belt, but to ones that are already on the road. The average automaker sells a few million cars a year, but this number is too small for mobile app developers because they’ll only get a few thousand subscriptions when they need tens of millions. So, you need to bet on the aftermarket and try to connect people who are already driving. That’s the kind of ecosystem that players like our company Bright Box will flock to.

We’re a vendor of a connected car platform, and we can connect cars that are already on the road. We can provide automakers with connectivity systems and drivers with interfaces. In addition, we’re part of an ecosystem, since we’re part of the Zurich global insurance group. We can provide products on a dynamic price basis using data from automakers, which will facilitate the development of automakers’ own ecosystems.

Why the Auto Industry’s Data Monetization Strategy is Due for a Change was originally published in Bright Box — Driving to the future on Medium, where people are continuing the conversation by highlighting and responding to this story.

In mid-March Elon Musk, one of the most famous innovators and technology entrepreneurs, reiterated his concerns about the lack of regulation of artificial intelligence, noting that “AI is far more dangerous than nukes, by far. So why do we have no regulatory oversight? This is insane.”

3 weeks ago the House of Lords Artificial Intelligence Committee published a new report entitled “AI in the UK: Ready, Willing and Able?” The report outlined various restrictions aimed at the spread of AI for both the British government and UK-based businesses as AI increasingly expands in power.

In Singapore there are also concerns at the state level about the scope of AI and the need to control it. Dr Janil Puthucheary, Senior Minister of State, Minister of Education, Minister of Communications and Information. and Minister-in-charge of the Government Technology Agency made a public statement in which he discussed AI in the following terms: “It means that from a policy perspective, we need to focus on the risks and issues that can be foreseen on the basis of what is happening today and perhaps accept that there will be unknown unknowns and we will deal with them when the time comes.” And in the last episode of Westworld producers warn AI creators: ‘Stop’…

I don’t count myself among the naysayers. In fact, I don’t think there are any serious risks at all involving AI. In the end, there are always other solutions that can counter AI systems. And all restrictions are likely to simply lead to a slowdown in AI-related innovation and development.

Artificial intelligence is a kind of hardware and software with functions similar to those that can be perform by a living brain. Artificial intelligence is divided into two categories in terms of scope: “general” and “narrow.” When government officials and entrepreneurs talk about the risks for mankind, they are probably referring to artificial general intelligence or “AGI” because the functionality of AGI resembles that of the human brain and is capable of learning several tasks at once.

Narrow AI systems perform specific tasks that would require intelligence in a human being and may even surpass human abilities in these areas. However, such systems are limited in the range of tasks they can perform. 99.9% of today’s AI applications (including self-driving cars, DeepMind’s Alphago, and pretty much everything else you have seen or read about) are in the category of what we call “narrow AI” (or “weak AI”). These are AI applications that operate at or above human-level of intelligence, but in a narrow domain. A self-driving car application cannot be ported over to drive a motorcycle, for example, let alone trade stocks. Even DeepMind’s Alphago was trained to play Go on a 19x19 Go board, but if we were to give it a board that is bigger, smaller, or even triangular, Alphago would need to be re-architected and re-trained.

Elon Musk predicts that self-driving systems will be able to perform essentially all driving-related tasks by the end of next year. However, this is a narrow AI, and any potential risks involved in it have no bearing on those commonly associated with strong AI.

Fear of AGI is simply not justified right now for a number of reasons.

First of all, these systems don’t have enough computing power

Reinforcement learning is currently a hot research topic in the world of artificial intelligence. Reinforcement learning uses trial-and-error approaches to maximize an algorithmic reward function. Some experts think that RL is the path to AGI because this approach makes it possible to get feedback from the environment.

Machines can learn to solve a certain problem not because they are given examples — “this is good, and this is bad” — but because they find themselves in a certain environment where they have a reward and a punishment, and they suffer for a long time until they get the result they need. For example, a robot can learn to walk or jump over obstacles. In theory, this kind of robot can start self-training. But in practice, this kind of solution has not been applied or tested on an industrial scale. The problem with these kinds of simulation tools is that it would be rather expensive to provide 200,000 cars to be destroyed by the technology. Therefore, the simulation itself is almost impossible to implement — as is providing a quantity of data comparable to that stored in the human brain.

Concerns about artificial intelligence are based on the fact that AI can and should work as a human brain does, which means that, just like a human, it can also pose a threat. However, the quality and quantity of data for development is still negligible compared to the human brain. In 2014 IBM boasted about its work on modeling the neural networks that make up the foundation of AI. IBM researchers claimed that they had developed a computer chip with the same power as the brain of a frog. Last year an IBM research staff member accepted that we should not expect to see AIs that can rival the depth of human consciousness anytime soon: “we’re only scratching the surface.”

No one knows how “it” works

Neural nets are very powerful when it comes to solving certain machine learning problems, but they cannot in and of themselves create machines that can think, or even reason, from sparse and ambiguous data, which is a core feature of AGI.

Nevertheless, neural networks are not a “black box.” It is possible to build many visualizations of the internal state of a neural network that could be used to analyze what the neural network looks at when making specific decisions. Processes in neural networks can be analyzed, but not in linear regression. When people say that they do not know how a neural network works, they are merely trying to please the crowd.

Being afraid of AI is the same as being afraid of software integration. All industries have embraced automation. And weak artificial intelligence can improve service quality. As with autopilot, the driver doesn’t need the car to drive itself. If the car can just assist the human, the number of accidents will decrease. You don’t have to entrust everything to a neural network, but it can help you out if you miss a thing or two. This is because a human can get tired or be influenced by external factors, while a neural network doesn’t care.

For example, let’s take a look at emergency braking systems for cars. They are not based on artificial intelligence, but rather on linear algorithms. When the intelligent connected car is in motion and gets close to a pedestrian, the system brings the car to an abrupt stop. Systems of this kind will also be developed and will also work, and they will place limits on what robots and AI can do.

The symbiosis of a neural network and a human can offer immense assistance to the human. It’s similar to putting an exoskeleton on a person. And today, in the age of weak AI, there is not a single process in a person’s life where they might encounter an uncontrolled neural network. Humans control everything, all the time. There are currently no neural networks that could ever rebel against their masters, and they are unlikely to emerge because, in order for a specific confrontation against humans to occur, AI would require computing power that private companies will not provide, and governments will oppose.

Should we be scared of Artificial Intelligence in real roads? was originally published in Bright Box — Driving to the future on Medium, where people are continuing the conversation by highlighting and responding to this story.