Smart Mobility Case Study: Ford

Executive Summary

Automobile Industry is growing through a major change, shaped by shifting consumer demands. The Industry is facing increasing challenges due to factors like falling demand, increasing regulation, demand for electric cars and increased use of shared mobility. Big data has a major role to play in shaping the strategy of Automobile manufacturers in the Future because data analytics can provide useful insights into changing consumer attitude from self-driving towards autonomous driving, efficient design and production, auto finance and Insurance, improvements to supply chain and Improvements to Marketing and Sales.

Ford Pass serves as an effective platform for Ford, that engages consumers to interact directly with the automobile Manufacturer. This in turn enables Ford to collect huge volumes of data that can be used successfully to offer new products and services, forge new partnerships, tailor marketing messages to increase customer engagement, thereby generating alternative revenue streams in order counter lower volume of sales of new car. The organization should expand Ford Pass to include data from Vendors to counter the effect of rising costs and negative disruption due to vendor business going bust. Finally, Ford should identify relevant data sources to draw insights from customer attitudes towards shared mobility and prepare for the changing future of automobile industry.

Introduction

Ford Motor Company was set up in 1903 by Henry Ford, with a vision of making cars that were practical and affordable to people from different walks of life. Henry Ford envisioned Ford to produce the largest number of cars that were simplest in design for the lowest possible cost, so that general public could afford them. Till date, this vision remains the core to the way Ford operates, and the organization’s vision is to be the leading consumer company for automotive goods and services by constantly improving products and services that fulfils the customers needs.                                                        
                                          (Source: Corporate Ford)

In this report, we will undertake an analysis of the challenges currently faced by automobile industry in general, the role of big data in shaping future strategy of automobile industry that serves as a motivation for data collection, insights into data collection and mining, Strategic gain from Ford Pass and finally how can Ford use data to compete in the changing Automobile landscape

Challenges faced by Automobile Industry

Automotive Industry is going through a turbulent phase, and as a result a number of automotive manufacturers are facing a slump in the demand for automobiles. Below are some of the reasons attributed to why automobile industry is struggling presently –

• Fall in demand – Some of reasons for falling demand a result of slowing global economy with fears of a global recession, falling demand in China which is one of the biggest market for automobile providers, trade tariffs and reducing customer demand
• Emission issues – New car registration in the UK dropped by about 7% in 2018, owing to air quality concerns and taxation changes. This has particularly impacted diesel car makers
• Electric cars – Car manufacturers are having to invest heavily in technologies to manufacture electric cars, but cannot say for sure, how ready the market is, to accept electric cars. Another challenge is the lack of enough infrastructure to support adoption of electric cars. For example, UK and US roads lack in the number of charging infrastructure needed to support complete electrification and drive demand for electric cars
• Shared mobility – Generation Z are financially conservative and are comfortable doing things that are different to a traditional mindset. There is an increase in demand for shared mobility as an alternative to privately owned vehicles. It is estimated that by 2030, one in ten vehicles sold will be used for shared service

(Source: Mckinsey,2016; Goldman Sachs 2015; Thomas,D;2019)

Motivations for data collection

By 2020, it is estimated that 75% of cars sold globally will be capable of transmitting data and the market for connected car data services is estimated to grow upto $40B. Cars will be able to capture and provide a number of data such as information related to vehicle maintenance, wear and tear, driving route history, driving speed, road and traffic conditions, entertainment preferences using a number of data gathering sources such as smart sensors, cameras, GPS, Lidar etc.. to name a few.
             (Source: Finjan,2018)

Data gathered from the above sources can be useful for a number of reasons. Below are some of the useful applications of data gathered through connected car services –

• Autonomous driving – An estimated 90% of deaths on roads are caused by accidents due to human mistakes. By analysing a pool of data, automobile machines can be trained to make appropriate decisions that are safer and more effective in comparison to human decisions. In addition, information that is possessed today by state and regional transport authorities through various IOT, can be used to improve road conditions, reduce traffic and create smart infrastructure. An estimated 10,000 to 500,000 injuries can be prevented by data obtained from smart sensors and IOTs
• Design and production – Data from driving analysis, maintenance and preferences can be analysed to derive insights into safety, quality, fuel efficiency, battery life and overall performance of automobile, helping manufacturers improve each of the above parameters in order to produce better automobiles.
• Automobile financing and insurance – Gathering data from customer route history, purchases done through connected car system, driving behaviors will help automobile manufacturers devise person centric financial plans or insurance plans either themselves or collaborating with other providers from Financial industry.
• Improvement to supply chain – Drawing accurate insights from data generated through connected car services should be able to help automobile manufacturers in order to compare supply chain costs, quality and reliability of different parts and components, thus improve quality and reduce cost. Data will also help to predict demand thereby helping automobile manufacturers to accurately procure the needed components and reduce stockpiling of inventories, helping to improve the cashflow. Advanced analytics help automobile manufacturers from a reactive supply chain model to a predictive/proactive model. An example, data from web interactions and use of product configuration will help automobile makers to pinpoint new trends accurately and mobilize resources to meet the demand.
• Improve sales and marketing – Accurate analysis of data generated from connected car services, will be useful in order to create marketing strategies for specific segment of customers because the automobile manufacturers will be able to accurately predict what the customers need. Data from a range of sources can help automobile manufacturers to analyze the impact of marketing investments and can provide an effective approach to predict where should the marketing budget be spent, in order to maximize impact. Data analysis will help automobile manufacturers to improve customer engagement and drive sales through targeted promotions and advertising.

(Source: Dayal,P 2018;Deloitte 2015)

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(Image Source: Big data and Analytics in Automotive Industry, Deloitte 2015)

As per a McKinsey report in 2016 on monetizing car data, there are four major trends that have the potential to change automobile industry. These trends are –

• Electrification
• Connectivity
• Autonomous driving
• Shared mobility

These trends will be responsible in shaping the changes to behavior in mobility, generate new revenue streams and shift from existing revenue streams, create new competition and joint ventures and finally advance technologies by leaps and bounds. Big data has a enormous role to play in order for companies to adapt successfully to be the leaders who share the new trends, and companies who are successful in generating accurate insights from the huge pool of complex data, that are currently available and unused by automobile manufacturers, will be at the forefront of the new revolution in automobile industry. Which is precisely why Ford, through its Ford smart mobility initiative has an opportunity to better engage with customers by providing what they need and in turn generate massive amounts of customer insights, which if used successfully can pave way in creating new strategy to evolve in the future.
             (Source: Mckinsey 2016)

Data Collection and Mining

Ford Pass is designed to gather data from a variety of sources, that comes from a day in the life of the customer. Some of the data sources include customer driving behavior, usual customer destinations, food and drink preferences, entertainment preferences, travel data, recreational preferences etc..  These data can be in the form of a structured data i.e. data that be processed by a computer in its original form, or data can be in the form of an unstructured data. Unstructured data can be in the form of text messages, emails, video files etc.. which will have to be converted into a machine-readable form to gather insights. There are three types of analytics that can be built from these data. Descriptive analytics, which provides insights into what is happening at the moment and provides current insights. Predictive analytics analyzes data over a period of time to predict what might happen in the future. Prescriptive analytics suggests corrective action in order to aid decision making based on analysis of historical data. Some characteristics that define the readiness level of data for analytics are –

• Reliability of the data source – This is important in order to eliminate data misrepresentation and basing decisions upon incorrect data. The source of the data needs to be accurately identified based on the purpose of data insights. It should answer the key question which is “Do we believe in the data source and are we confident about the data source”?
• Data content accuracy – This is important to ensure we are using the right data in order to solve an analytics problem. For example, in case of Ford Pass, a customer address should match with where a person lives in order to provide enhanced services to the customer. It should answer the key question, which is “Is the data right for the job”?
• Data accessibility – This is important to ensure the data is readily accessible or available when needed. Data might sometimes be stored in multiple locations and there may be challenges accessing the data real time.
• Privacy and Security – This is important to ensure that, the data is prevented from getting into the hands of someone who isn’t authorized to view or use it. There are a number of regulatory frameworks that emphasize the need for higher security and provide strict privacy guidelines
• Data consistency – This is important to ensure, data coming from different sources, but needed to solve a particular problem is accurately collected and merged in order to draw relevant insights.
• Data timeliness – This is important to ensure the data is not misrepresented due to time delays
• Data validity – This is important to ensure only valid data values are used for each parameter, that are important to solve a particular problem. For example, a valid value for age can only be a number
        
                             (Source: Sharda R, Delen D, Turban E 2018)

Some of the most commonly used data mining processes are CRISP-DM , SEMMA, KDD Process, My own (Proprietary processes) and domain specific methodology. Cross Industry standard process for data mining is a process that involves six steps which begins with Business understanding. This is basically asserting the managerial need for new knowledge and finalizing the business objective. Second step in the process is data understanding which is concerned with identifying relevant data from different set of databases. Third step is data preparation which involves preparing the data identified in the previous step, for analysis by different mining methods. Data preprocessing typically takes the most amount of time. Forth step in the process is building a model, using different modelling techniques on the previously identified and prepared data set, based on the specific business need. Fifth step is testing and evaluating the model for accuracy and finally the last step is deployment of the model that organizes and presents data in a way that makes sense to the user.
             (Source: Sharda R, Delen D, Turban E 2018)

Strategic gain from Ford Pass

Ford Pass was designed to help its customers navigate some of the challenges they face in their day to day life. Through Smart parking services, Ford can build analytics that can provide insights into where do their customers normally drive and park their cars, that can enable Ford to accurately predict and plan locations for new service centers, partnerships for parking spaces that will help provide additional services to customers thus increasing loyalty. By partnering with car hire giants like Hertz, Ford possesses data of how frequently their customers use car hire services and when do they typically use the same. These insights can help Ford, negotiate a better deal for their customers with Hertz, sell ancillary services to customers like travel insurance, sim cards etc.. by partnering with companies that cater to travelers needs. Through Vehicle Support, Ford enables their customer empowerment, that ensures their customers can themselves fix minor issues and in turn prevent engaging Ford labor for issues when vehicle is under warranty, that a customer can fix themselves. This has a potential to free up some part of labor time, that can utilized for other productive activities. Features like parked vehicle tracker, fuel finder, food and drinks finder, recreation finder can help Ford offer exclusive discounts to Ford customers through partnerships with local outlets. In turn, Ford can remove all the person identifiers and sell this data to businesses, who will be able to use this data to come up with new products and services. Ford can also use this data to collaborate with agencies in order to provide targeted advertisements to customers based on their general preference. Ford Credit can provide an insight into Ford customers financial details, where Ford can analyse data on their customers spending behaviors. The insights from this can then be used to predict the likelihood of customers spending on a particular product or service, which in turn helps Ford plan their products and services. Roadside assistance, breakdown alerts and service alerts provide immense relief to customers knowing that there is help at the fingertips. At the same time, the data from the car sensors are immensely helpful to Ford in order to plan their inventories, staffing, service center locations for increased customer convenience and engagement, review quality of the vehicles and strategize future improvement strategies. Overall data from Customer activities can be used to accurately segment customers into different categories, where Ford can accurately predict what each of these customers need and market Ford products or services accordingly, in order to get maximum customer engagement for their marketing campaigns and ROI is higher for the marketing spend.

Conclusion

Automobile industry is going through a major disruption and companies cannot survive in this climate by doing what they have traditionally done. In order to adapt to the changing landscape, Ford must anticipate their customer needs and come up with products and services that are valuable to customers. As evident Ford revenues are dropping year after year and the car sales have dropped significantly in the last 2 years. With Industry trend shifting towards Shared mobility and Autonomous driving, it is imperative that Ford uses the data generated from Ford Pass in order to create analytics that is descriptive in nature to start with. Once this is set up, Ford should then advance towards analytics that are predictive and prescriptive in nature, in order to create partnerships, come up with products & services that are valuable to customers that paves way for alternate revenue streams, that isn’t core to what Ford does today. Ford Pass should also extend to include vendor data, that can help Ford predict the cost savings, quality improvements that can be achieved through different vendors or if there is a danger of vendor going out of business, that can create disruption and increase cost of Ford Operations. There is also an opportunity to evaluate the customer engagement to various marketing and advertising campaigns through Ford Pass, that the organization should utilize in order to measure the efficiency of the marketing spend. Also in order to prepare for shared mobility in the future, data from Ford Pass can provide insights that can shape Ford’s strategy to mitigate the consequences of shared mobility on overall sales.

REFERENCES

• Dayal, P. (2018). Impact of Big Data on the Automotive Industry. [online] Newgenapps.com. Available at: https://www.newgenapps.com/blog/impact-of-big-data-on-the-automotive-industry [Accessed 28 Aug. 2019].
   
• Finjan Blog. (2018). Car Companies and Big Data | How Smart Cars Collect Your Data. [online] Available at: https://blog.finjan.com/how-smart-cars-collect-your-data/ [Accessed 28 Aug. 2019].

• Ford Corporate. (2019). Home. [online] Available at: https://corporate.ford.com/homepage.html [Accessed 28 Aug. 2019].
   
• Mckinsey.com. (2016). Monetizing Car Data. [online] Available at: https://www.mckinsey.com/~/media/McKinsey/Industries/Automotive%20and%20Assembly/Our%20Insights/Monetizing%20car%20data/Monetizing-car-data.ashx [Accessed 28 Aug. 2019].
   
• Sharda, R., Delen, D. and Turban, E. (n.d.). Business intelligence, analytics, and data science: a managerial perspective. 4th ed. PEARSON.
   
• Thomas, D. (2019). Five reasons the car industry is struggling. [online] BBC News. Available at: https://www.bbc.co.uk/news/business-48545733 [Accessed 28 Aug. 2019].
   
• Www2.deloitte.com. (2016). Big Data and Analytics in Automotive Industry. [online] Available at: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/manufacturing/deloitte-uk-automotive-analytics.pdf [Accessed 28 Aug. 2019].