Decoding the Impact of Quality on Automotive Sales. My personal observation.

In 2013, while still a quality technician at a Volvo Trucks plant, I discovered the JD Power Quality Study. Since then, I've been tracking both Quality and Dependability ratings, which didn't exactly surprise me but did make me reconsider my perspective on the automotive market. As someone with a background in quality management, I've been continually astonished by certain trends over the years. Some brands maintained consistent performance and seemed oblivious to the fact that where we, as consumers, expect high quality, the reality was often quite disappointing. Today, a little over ten years into my observations, I finally did what I had planned back in 2013, but lacked sufficient data for back then (if you have jokes about long-term planning, feel free to share them in the comments).

I wanted to investigate whether quality ratings influence car sales. On one hand, it seems obvious that they should, but on the other, as Edward Deming famously said, "In God we trust, all others must bring data."

Before you start scrolling through the charts and diving into my conclusions, here are a few introductory points:

1. All data were sourced from publicly available resources.
2. The spike in 2020 is due to a change in the rating calculation methodology.
3. Sales breakdowns for some companies’ brands were not available; they are presented together.
4. Not all brands are included in the article, as data for some were difficult to find. Some brands appeared and disappeared in JD Power reports over the years.
5. A big thanks to the German brands for their crystal-clear annual reports.

One amusing observation from reviewing these annual reports: no brand shows a multi-year trend in their annual report. Perhaps this is because a drop from the previous year looks better than a consistent decline over the last five years when shown on a graph. Incidentally, this is the subject of one of my previous articles – "Honest Data That Misleads." In any case, it's all visible now.

What was done: I compiled all the JD Power Quality Studies starting from 2013 and transferred them into a table by brand, then matched them with company sales figures from the annual reports for the corresponding years. My hypothesis was that the market doesn't immediately react to problems, and indeed, a noticeable correlation appeared in the second year. Some might argue that there isn't enough data to calculate correlation, and I would agree – there isn't enough data, but we work with what we have. Another important assumption is that quality studies were conducted on the American market, while sales are shown globally. Understanding that the construction of cars for different markets varies slightly, I decided that the Quality Study for the American market would be relevant for others. Again, you might disagree, and I would understand.

Important: Ultimately, the sales chart is shifted by two years, and the yellow line represents sales two years later, i.e., above the 2013 Quality Rate (column), you see 2015 sales (line). The left scale shows the number of quality problems – the higher, the worse. The right scale shows sales. Correlation for the shifted values was calculated and it gave an interesting result.

Premium brands like BMW, Volvo, Audi, and Porsche showed a reverse correlation; I would say that the strength of the brand is such that current quality issues do not affect future sales. The same happens with the mass-market brand Volkswagen, and I don't think it's a coincidence.

For the other brands shown, the correlation is positive, and quality issues do impact sales.

For some premium brands with declining quality ratings but stable sales, I can assume that increasing sales volumes mask systemic engineering problems that require attention, as the sales signal drowns out the problem signal. It's hard to believe something is wrong if financial reports look good. On the other hand, premium brands often lead in technology, and these unproven solutions can lead to issues.

In my opinion, much more data is needed to draw significant conclusions, but the difference in the presentation of financial reports by different companies complicates this task.

In any case, the charts are below; take a look and draw your own conclusions.

Let's see everything together. It is the best representation I made. Lots of data doesn't give better options. For the following charts, the Study Average represents all market Quality Studies for premium and mass-market brands.

Right-click the mouse on a picture and open it in a new tab if you want to zoom it in.

As a conclusion, I will quote my quality management lecturer, who loved to say: "Quality doesn't cost money, quality makes money." But there are some exceptions.

By the way, there is no unsolvable systemic engineering or business problem. There are only root causes that have to be uncovered.

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