FORD VS TESLA: How Data Analytics Transforms Businesses

Written by Mark James, lecturer at UCD Professional Academy

The competitive advantage of car manufacturers has historically been based on brand, scale, product range, and an extensive dealer network that manages customer relationships. Traditionally, this has made it very difficult for new competitors to get a foothold, with many trying and failing. But the industry is evolving, and as with other industries, new factors are emerging as sources of competitive advantage. Data has perhaps become the most potent, both in terms of crafting the customer experience and maintaining operational efficiency. It’s worth exploring how that evolution happened.

The Rise of Ford

Henry Ford founded the Ford Motor Company in 1903 and built it using a decision-making approach that was grounded on determination, intuition, and personal experience. This worked really well in its first two decades of operation. Henry Ford’s belief that cars should be within the budget of most people helped focus his efforts on efficiency and simplification, leading to innovations such as the introduction of the assembly line and minimising the movement and complexity of processes for workers. This allowed him to grow the business quickly and take a large portion of an expanding young market.

But the problem with using intuition and personal experience as a basis for decision-making is that they change very slowly - if at all. Ford once famously said that a customer can have a car painted any colour they want, so long as it is black – at that time, black paint was cheap and easy to apply. Ford believed that utility was everything – function and price were the only considerations that the public should make. They were far too slow to adapt to the changing tastes of consumers in the 1920s and 1930s. The company continued to produce the Model T for many years, even as competitors began to offer cars with more features and options, gradually eating into Ford’s market share.

Enter the Whizz Kids

In the years between 1926 and 1946, Ford Motor Company struggled to be competitive and was barely making money. Without a clear understanding of what its customers really wanted, and the drivers of cost in the business at this scale, it stopped growing and entropy kicked in. Inefficiencies grew exponentially throughout all functions. As Robert McNamara described it – “it was a god-awful mess”. McNamara, a second-generation Irish American, was a Harvard professor and key part of a pioneering group that became known as the ‘Whiz Kids’. This was a group of ten former US Army Air Force officers who saw the value of data and analytics in decision-making and were brought into Ford in 1946 to modernise the company's management. As the star of the group, McNamara was known for his reliance on extensive data analysis to drive decision-making and took these principles to Ford in 1946.

McNamara began to rigorously examine the company's operations using data. This approach shone a light on what had become a convoluted and incomprehensible set of operations that were disconnected from the business’s strategy. Efficiencies were quickly identified and actioned, with clear data-backed reasoning. Ford didn’t have a market research department – McNamara set one up and used data on consumer preferences, fuel costs, and other variables to identify market segments. At the time, the general belief was that Americans wanted conspicuous consumption – a trend that Ford had failed to identify in the preceding decades. Cadillac was a prime example of this grandiosity, selling cars with big wings and flashy lights – the kind of car that comes to mind when you think of the 1950s. McNamara wanted to know if there was a market segment that they were missing, and asked for data – ‘who buys the Volkswagens?’ he asked his team. At the time, it was a small segment of the market, but McNamara wondered if it could grow. This data-driven approach led to the development of the Ford Falcon, a smaller, more fuel-efficient car – very much not what comes to mind when thinking of 1950s America. But the Falcon went on to be a huge success for Ford.

Safety became a major focus for Ford under McNamara as well. McNamara pushed for data to be collected to see if car design played any role in the outcomes of car crashes. This data showed that current designs were very poor for safety and that cars could be redesigned to protect people. McNamara thought that safety features could be a selling point for cars, so Ford redesigned their cars and introduced seat belts as standard in 1956, ahead of most of the industry. Data and how it was used was the critical success factor for Ford and has continued to play a big role in the industry.

Are we in the midst of another data revolution?

In 2016, Tesla set an ambitious public goal to compete with traditional car companies such as Ford on scale by increasing production of their first mass-market car, the Tesla Model 3, to 500,000 units by 2020. Many people were highly sceptical of this – of all the barriers to entry in the industry, the scale had long been the most difficult to overcome – and Tesla lacked experience in mass production. As it happens, Tesla did not meet this production target, disappointing many who had pre-ordered the Model 3. It seemed that scale was the ultimate competitive advantage in car manufacturing.

But Tesla is different. It is fundamentally a data business and has built its competitive advantage around it. It collects, analyses, and interprets enormous amounts of data to guide decision-making processes at all levels in the company. It employs a host of techniques such as AI to analyse data from production lines and improve manufacturing processes. It used a data-driven approach to overcome the traditional production disadvantages it faced – and it worked. Since 2020, Tesla has managed to scale its production considerably and lead the electric vehicle market. In Europe, the Model Y is currently the best-selling car in 2023 so far.

Tesla has integrated their supply chain which allows them to react more quickly to changes and apply data-driven insights to improve both production and distribution. This agility and quick reaction to data-driven insights are part of what has allowed Tesla to succeed despite their initial lack of experience in mass vehicle production.

There are many examples of this. In 2021, Tesla used real-time telemetry to identify that adjustable lumbar support in the front passenger seat of the 3/Y was rarely used by its customers. This insight prompted the company to remove this feature from production – a decision that not only saved resources that could be used more efficiently elsewhere but also demonstrated how close that data brought them to understanding their customers and responding to their needs. But it isn't just data. Correctly interpreting what the data is trying to tell you is at least as important. For instance, how could Tesla be sure that low usage meant a lack of interest or low utility? It could have been interpreted as a customer awareness issue, or that the feature had been implemented badly.

The most significant way Tesla uses data is in developing and improving its Autopilot and Full Self-Driving (FSD) features. All of Tesla’s cars have the hardware for FSD, and are constantly gathering data while they are being driven, even when these features are not engaged. This data is sent back to Tesla, where it is used to train and improve their AI models, helping to refine the car's ability to interpret its surroundings and make appropriate driving decisions. Tesla recently announced that FSD will be moving out of beta. This is a significant milestone that signals a high level of confidence in their technology's safety and efficacy and symbolises the successful interpretation and application of literally billions of kilometres of real-world driving data.

FSD was first made available to a limited number of customers in 2020 and has provided Tesla with valuable data about its performance in various scenarios. However, interpreting this data wasn’t quite so straightforward. It involved understanding the bias in the data, as it was mostly collected from FSD advocates who had different driving habits and risk tolerances compared to the broader customer base. Tesla has had to generalise the insights from this data to make the feature reliable for a diverse range of drivers. Part of how they’ve approached this is by gathering large amounts of qualitative feedback from users so that they can interpret the quantitative data in context and understand anomalies or unexpected patterns.

Conclusion

Whether in Ford's revival in the 1950s or Tesla's contemporary success, one thread remains consistent - the integral role of data. Businesses have gradually moved away from decisions based on intuition and experience to those driven by data. Data has become a critical component of competitive advantage. But data alone isn’t enough; it's about understanding it, interpreting it, and using it to tell a story that enables better decision-making. An effective data analyst is therefore one part detective, one part storyteller, knitting together a compelling narrative that drives innovation and progress. Henry Ford was right about one thing though – Tesla lets you choose any colour car you want, but all colours other than black cost extra!

Join us Wednesday, July 26th at 1pm.

In this webinar we will cover:

• What is Data Analysis & why it’s important

• Ford vs Tesla - How data transforms companies

• Excel Masterclass - Live Demo

• Brief Intro to Power BI & Tableau

• Overview of UCD Professional Academy Data Analytics courses

In addition, you will also have the opportunity to ask questions in a live Q&A