March 22, 2017
Data analytics has the power to transform and disrupt organizations regardless of the industry they belong to. The transportation industry and its many subsets-- aviation, trucking, rail, public transit and others--have a history of working with big data, yet access to new analytics technology has changed the way that the data is leveraged.
The intersection of data and transportation is fueled by the Internet of Things and increasingly dynamic analytics capabilities that generate massive volumes of data in real-time. In transportation, a nearly infinite number of components exist to be tracked and optimized through data analytics, from ticketing, vehicle tracking and maintenance, to customer interactions and scheduling. The crucial differentiator between organizations that use this information to excel and those that do not is the wealth of business insight they are able to derive from data sets.
Applications of these tools span aspects of transportation organizations from end to end. Major uses, as defined by an IBM report on big data and analytics in travel and transportation include customer analytics and loyalty marketing, capacity and pricing optimization, and predictive maintenance.
Leveraging data to improve customer experience is a need across all transportation sectors, particularly air travel, where competitors seek any advantage they can to maintain or grow market share. Promoting customer loyalty and striving to serve the customer’s individual needs can immediately improve customer experience and can help shape product innovations going forward. This can be achieved many ways. For instance, just this week, Southwest Airlines announced that it is investing $500 million in a new reservation center - the company’s largest tech update to-date. This will allow them to accept foreign currency, decrease time between connecting flights and more easily change schedules and prices.
In transportation, capacity and pricing are closely intertwined and change constantly. Airlines, rental car providers, and others now have the ability to predict changes in capacity trends and adjust prices before demand shifts, increasing annual revenue by millions of dollars. Similar to the importance of adjusting for increases in demand, transportation companies can also reduce losses by predicting issues like storms or accidents and adjust prices accordingly. As stated in the IBM report, in addition to monitoring outside factors, airlines and other transportation services companies can monitor competitor pricing to better understand customer buying propensity and price elasticity.
Public transit systems globally are seeing an increase in ridership and in some cities, are faced with that demand outpacing capacity. MIT students researched data analytics in urban transportation and found that promoting changes in rider behavior, rather than working to reduce demand or expand system capacity, can be an effective way of managing spikes in demand. The MIT students implemented fare incentives to encourage travelers on Hong Kong’s MTR system to embark at nonpeak times and showed positive impacts for reducing congestion during morning commute hours. Other cities like Singapore offer similar incentives, allowing commuters to travel for free before 7:45AM, which resulted in a sustained shift of seven percent of riders commuting before peak hours--a significant number when considering the amount of commuters using the system daily.
Many different types of maintenance are required for airlines, cars rental companies, and public transit to run schedule. Data analytics can track each aspect of maintenance with the use of sensor and performance data, from restocking various equipment to monitoring upcoming service dates and allow for companies to both conduct preventative maintenance, and schedule their employees or fleet in accordance to maintenance needs. By taking care of maintenance before any part of the system breaks down, unplanned schedule delays can be entirely avoided, saving companies millions of dollars and consumers delay headaches.
Analytics technology also puts the power in consumers’ palms. Companies like Waze, acquired by Google for $1.1 billion in 2013, leverage social data to optimize travel routes and allow users to “outsmart traffic” by warning them of potential roadblocks and providing them with the guaranteed fastest route.
Innovative, futuristic concepts like driverless vehicles rely on multitudes of data to function, and have been picked up steam in recent years. Major market leaders have developed initiatives to actively compete. For example, Intel purchased autonomous driving technology leader Mobileye for $15 billion earlier this month, bringing the company much closer to the 27 car manufacturers Mobileye has developed partnerships with including BMW and Tesla. Introducing Mobileye to the already robust infrastructure and resources Intel has developed related to autonomous driving will set the company up for transformative growth looking forward.
Both the evidence and potential for data analytics to transform the transportation industry sets an overwhelmingly positive tone for what is to come. Partnerships between industry leaders like Intel and Google, leading universities, global urban leaders, and disruptive ventures like Mobileye, Waze and others have set a precedent for relatively expedited and effective trial and mass-market adoption of these technologies. This type of involvement of the entire ecosystem is well-aligned with TechNexus’s model of smarter venture development and, in this instance, has shown to be incredibly powerful.
March 22, 2017
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