Before this course, I thought that keyword research was just a synonym for educated guessing on what people search for on Google. We had to pretend we worked for Tesla and use keywords to help. So if I was the Tesla marketer, I’d think that people would be searching “Tesla” or “electric cars” or “tips for electric vehicle” as a parenthesis of guessed keywords. That’s all I thought it was. But after conducting keyword research for Tesla, I realized how far off I was. Keyword research isn’t guessing; it’s getting to the bottom of what people are actually looking for and how they navigate the purchasing process to determine if they want this high-cost purchase—the electric vehicle.

Therefore, it made more sense when I began learning keyword research for Tesla in class. Keyword research tells you what topics to talk about, how to structure, what appeals most to people. For example, when I first created my project on Tesla, I assumed that EV cars themselves would have the highest searched volume. In reality, EV Trucks and EV SUVs were the most often searched. They not only had the highest searched volume but also the highest CPC bids. This implies to me that there’s a more mature market in the USA than I initially thought, boasting electric trucks and SUVs and many options for those buyers. I didn’t even understand that buyers were crying out for so many different types of electric trucks until I saw the sheer volume of numbers that came up, and that’s when keyword research made sense on a grand scale. If I was guessing keywords, I would have ended up in various categories but wrong.

Using Google Keyword Planner was also less daunting and complicated than I assumed. Once I understood what each column meant, it was almost enjoyable to work in. I entered seed terms like EV Truck, EV SUV, EV Sedan, Reliability and Performance and even the names of competing manufacturers. The latter threw me off, because I figured a vehicle like Tesla would compare itself to more luxury makers. It turns out, in America, people do use Ford as a comparison far more often than Rivian. This shocked me because I figured people would want to stick to electric competitiors and recent ones to give them the benefit of the doubt. Keyword results prove otherwise. This taught me not to assume I know what keywords people are using when, in reality, they use completely different terms.

The search intent also played a important role in this project. Before diving into the Tesla search results, I thought that the keywords with the highest search volume would be the best keywords to use. However, after uncovering what worked best for the brand intentions of Tesla, I realized that intent was king. For example, simply searching “electric vehicle” probably means that someone is still in the early stages of research. Someone searching “best EV truck for towing” is narrowing down their options. Finally, someone searching “Tesla vs Ford truck” is deciding between brands, and all of these nuances matter and Tesla needs different content to speak to different types of buyers. As a result, once I better understood buyer intent, it was easier to concretize which words deserved priority in my assignment.

When it came time to create clusters for my Tesla keyword project, I clustered them all by category because it just made more sense that way; EV Trucks/SUVs/Sedans/Price/Reliability/Used vs Competitors Comparison. This organized my analysis better and helped me justify where the strongest crossover was due to search volume and commercial value. For example, while EV trucks and SUVs boast strong demand (search volume) in the United States, Reliability has some of the strongest CPC bids suggesting that buyers still remain wary long-term quality answers before they proceed one way or another (commercial value). The UK search volume results were much more generalized in scope with distinctions and government deductions not related to the American search inquiries.

I did make mistakes along the way; at first I opted for long tail keywords that were too general to garner any real significance from a marketing perspective. I also didn’t notice long tail searches that ultimately are the most defined (and valuable) for conversions. When I changed my focus to find narrower options, it all came together better.

Overall, keyword research with Tesla taught me how useful data in digital marketing is. It positioned me to think like a digital marketer as opposed to a student struggling to put information into fields without stakes. It taught me what Tesla should emphasize if they want high-intent customers in both markets and why. This was probably the most relevant project that prepared me for work in the real world outside of a classroom.