Tesla "Revolutionise Your Commute": Where a Marketing Campaign's Persuasion Model Breaks Down

Academic case study for Aston University's Consumer Research & Customer Experience module. Full DOCX in this repo: [Tesla_Report_v2.docx].

The question

Tesla's FSD campaign clearly works on some viewers and clearly loses others. I wanted to know why, using consumer behaviour theory rather than intuition, and to locate the exact stage in the decision journey where positive attitude stops converting into purchase.

Method

I applied three models to the same campaign artefact (the YouTube promotional video) and cross-referenced the findings against real viewer comments rather than treating the theory as self-evidently true:

• DRIP Framework (Fill & Turnbull, 2019): mapped each campaign element to differentiate/reinforce/inform/persuade objectives.
• Elaboration Likelihood Model (Petty & Cacioppo, 1986): separated which content works on the central (rational) route versus the peripheral (emotional) route, and for which VALS segment.
• Hierarchy of Effects Model (Lavidge & Steiner, 1961): tracked the campaign through cognitive, affective, and conative stages to find where it stops working.

Finding

The campaign is strong on cognitive and affective persuasion, and weak on conative persuasion. Tesla shows the tech, builds the aspiration, and then withholds price. That omission does not read as neutral: for segments motivated by security and affordability (Strivers, Survivors in VALS terms), it reads as a loss of control over the decision, which opens an attitude-behaviour gap (Ajzen, 1991) that the emotional appeal alone cannot close.

The practical implication: a campaign built entirely on aspirational appeal will systematically under-convert the exact audience it successfully interests but does not successfully reassure. The fix isn't more aspiration; it's adding a rational anchor (pricing/financing) at the specific stage where the model predicts drop-off, without diluting the luxury positioning for the segments who don't need that anchor.

Skills this demonstrates

• Applying multiple theoretical models to one dataset and checking where they agree and where they diverge, rather than picking whichever model flatters the argument
• Reading qualitative signal (YouTube comments) as evidence, not decoration
• Locating a specific breakdown point in a process rather than giving a general "some worked, some didn't" verdict
• Turning a critique into a testable recommendation

References

Full reference list (16 sources: Ajzen, Petty & Cacioppo, Lavidge & Steiner, Fill & Turnbull, and others) is in the PDF.