This is the second post in my two-part blog series that focuses on utilizing data in the mobility industry. You can read the first part, which covers the virtuous cycle of data, value and trust,here.
Having witnessed the digital revolution across a variety of sectors, we know that a whole new breed of new business models and solutions – powered by a combination of digital services and connected physical products – can appear virtually overnight and spread at an incredible pace, often profiting from network effects.
At the same time, new ideas can get copied equally quickly due to low entry barriers as well as business models that are trivially easy to imitate – consider electric scooters, for example– or disrupt due to a weak linkage between the customer and services or products. In times of high competition, customer experiences and trust are what counts.
For anyone planning to come out on top, the new status quo calls for better experiments that help companies identify where their core strengths and weaknesses lie relative to the value they create for the customers and the kind of customer relationship they are pursuing. At the same time, new ideas can be tested for their uniqueness and ease-to-copy during the experiment by asking a series of questions investigating how sustainable an advantage is.
Experimenting to extend your own domain
Ideally, these experiments should go beyond each company’s own offering. In order to leverage the true potential of your customer relationship in a world where no one will ever control the entire multi-modal customer journey, you are better off teaming up and building partnerships that are in line with your value proposition, and have similar promises towards the customer. The users won’t stop where your offering stops – if you and your partners are unable to meet their needs, they’ll simply look elsewhere. Additionally, an ecosystem that shares usage data in various levels of anonymization will allow you to understand your customers before and after the touchpoints where you directly interact with them.
The more multi-modal our partnerships become, the more (and better!) data we will generate, and this enables us to fulfill not only vertical, but also horizontal user needs. Uncovering patterns and predicting behaviors allows you and your partners to continuously adapt your offering and optimize value creation throughout the value chain.
At its core, forming data-enabled ecosystems is a matter of scale with far-reaching consequences. Small tweaks to your service alone may result in incremental improvement of your offering and marginal gains in your customer relationship. But as I’ve discussed in the first part of this blog series, there’s a lot more to be done with the latent data we collect around public and private mobility. At the moment, however, there isn’t much exchange between the different providers, and consequently, only very limited added value to the individual user, resulting a very low utility for the users who choose to share their data. We are missing out on a huge opportunity to improve the quality of mobility and its value proposition, and failing to show users how their contribution of data helps make their travelling better.
As digital literacy and awareness of the true value of data increases, expectations grow as well. If the benefits of sharing are not made completely obvious and apparent to the average customer, we risk moving into a direction where asking users to share their data will be met with growing skepticism and friction. And for anyone who wants to expand their business by collecting data, that’s not a good situation to be in.
It’s not about who can collect the most data
The collection of data comes with a considerable fallacy. As described by Andrei Hagiu and Julian Wright in their article on data creating a competitive advantage, many companies directly link the collection of data to network effects, assuming that the only way to succeed requires a very large amount of data to create a competitive advantage. But not every company has the possibility to create a product that relies on network effects. In these cases, another approach might be more relevant.
Until very recently, getting customer insights has been an arduous process that requires extensive customer research using focus groups, surveys and other labor-intensive research tools. Especially in the automotive industry, OEMs – due largely to their distance to customers through national sales organizations and dealers – have had a hard time getting direct customer insights and optimizing their products accordingly.
Recent developments in technology and communication, such as cloud or edge computing, fast wireless networks and increased processing power, have changed this. Today, even car manufacturers and their supplier network can collect customer and usage data directly and gain a more comprehensive understanding of their customers. Now that companies are able to interact with their customers more directly, demonstrating the value of their data sharing and building a trusting relationship is much easier, and that, in turn, can help them develop an advantage over their competitors.
It is now easier than ever to analyze how the products built on data really change the way we use our means of transport through real-time traffic information, vehicle-to-everything connections, crowdsourced data, optimized charging, or sharing platforms. Companies like Mobileye, who provide driving assistance systems, require a huge amount of data used to optimize a product for the customer. The more high quality data they have access to and are able to use, the safer their product becomes, and each incremental improvement in safety benefits every user.
It should be perfectly clear, then, that it isn’t the company that collects the most data but the one that uses it best, that can generate the most significant competitive advantage. After all, if the safety of your children is on the line, who wants to have only the second best system? This alone requires a lot of trust from the customer, which in practice means that you must demonstrate the value clearly and show that you directly use it to improve the product for the benefit of everyone.
It should also go without saying that appropriate use of data includes taking the necessary steps and precautions to keep it safe. Nothing is as efficient at undoing every last bit of trust between your customer and your company as losing or misusing data. And as trust disintegrates and users flock out of your platform with their data, you’ll also lose any competitive advantage that trust may have gained you.
Bridging the gap without direct access to consumers
Of course, not everybody has direct customer contact or the ability to provide direct value for shared data. This includes companies like Urban Engines which uses real-time consumer travel data to help public transit agencies visualize, analyze and improve public transit network performance, or TransLoc and RideCell that are helping transport agencies optimize and automate operations by developing technology platforms to help them integrate flexible, on-demand services that can supplement their traditional high-occupancy, fixed-route fleets. Another great example is Inrix, that enables businesses, cities, public transport companies or OEMs to improve everything from the mobility experience over traffic flows to finding the best location for your retail business.
For these companies, it is crucial to be able to communicate the bigger picture of how they use data, and send the message that their offered value makes everybody profit in the long run. Their ultimate goal is not to provide direct value to the individual customer, but improve the offering as a whole. By proxy, the utility for the end-user is less direct and the advantage comes through the mass of data as well as the precision of their predictions, tools and algorithms.
In these cases, it is not so much a question of whether your predictions are better by one percent at improving individual cases – it is much more about how you are able to use the real-time and historic data to improve the experience as a whole, maybe by 10 or 15 percent, perhaps even a hundred. This also implies that an individual customer’s insights are not going to increase the overall utility the same way it does with more user-centric products.
Why predictive analytics – and the right kind of data – matters
As new services are launched into the market, a legitimate (or perceived) lack of regulation, or confusion about their supporting infrastructure or complementary role within the mobility ecosystem may emerge and spark public controversy. In the worst case, this may result in bans and prohibition, similar to what has happened with many sharing services like electric scooters, ride-hailing or city bikes.
In these cases, the related issues were already a subject of heated public discussion by the time that the service providers began focusing on the way these services are in fact used and studying whether they tend to replace pedestrian traffic or trips by car. By predicting usage and behavior beforehand, we can prevent this and ensure that new services live in a symbiotic relationship with other forms of mobility services.
Another important aspect to consider is that you act on data that is still relevant and valid. If you keep recommending your customers a route with a public transit connection based on outdated incidental data, and another provider is able to offer real-time data instead, you will probably end up losing this customer because your service provides less value. Speed and precision is key here.
Having access to real-time is much less of a concern for example when the focus is on improving infrastructure, like Replica or Remix. In these cases, the optimal outcome is more dependent on being able to make the right decision based on all the available data that you were able to collect. Much akin to search engines, this requires a lot of historical data, and the more high quality data you have and are able to use to create value, the higher your competitive advantage.
New urban intelligence platforms like Placemeter try to combine these two aspects, aggregating all kinds of data from videos and a wealth of different platforms to optimize smart mobility solutions, streamline traffic flows, improve pedestrian crossings or assess the safety of intersections.
Unique data is the best protection against copycats
The last important aspect I want to shed light on in this article is the ease to copy, collect or reconstruct the user data that you need in order to offer your service. If the same data is very easy to collect, or maybe even openly available – e.g. from a public mobility provider – and you simply use it to show departure times or give route recommendations, your advantage may be quite small in this regard.
On the other hand, when you collect data through your own hardware or ecosystem, and you have discovered a way of using this data to create value for your users, it is typically much harder to copy. This, in turn, creates higher entry barriers into your market.
A trusted ride-hailing company likely knows exactly where and when the demand hits its peaks, and can use that information to ensure rides are available at a moment’s notice when the time is right. Using data to scale your ability to respond to micro-level changes in customer needs like this is one of the cornerstones of creating value in the era of data-enabled mobility services.
Get started sooner rather than later
Regardless of where you are on the path of establishing trust with your customers to generate data, and leveraging that trust to create a competitive edge, it is important to get in motion early on, and keep thinking a couple of steps ahead.
Seven out of the ten most valuable companies in the world today have built their business around data. Several new companies also in the ride-hailing and micro mobility domain have already established solid data strategies and platforms, and built entire business models around data.
For traditional organizations, responding to this wake-up call means integrating existing portfolios and developing new intelligent services, partnerships and ecosystems under their parent brand or as a standalone entity as they try to catch up.
All of this – from data strategies and platforms to designing and building new human-centric offerings and services as well as the optimal technology stack to support them in a resilient manner – is something we at Futurice are well equipped to help with.
Cities are in constant motion, and urbanization increases the speed and density of this movement, which translates to an increasingly wide range of behaviors and mobility needs and scenarios. Only by knowing their customers well enough, and offering them the right mix between convenience, safety, equity, sustainability, flexibility, effectiveness and connectivity, are companies able to build a service that is difficult enough to copy.
In summary, we don’t think there is just one answer to how you should utilize the trust of your users to create more value and a competitive advantage for your company. There are many aspects to consider, but the overarching theme is that in order to maximize your chances for success, you will need to understand your own goal for using data, as well as who you are improving the product or service for, in what context and what time frame.