Learn · Marketing claims translated
Percent formula
Invented example
Attraction is simple math. Get the three numbers that decide who wants you.
Ideas it might be pointing at
A short claim can stand for several different ideas. Each is set out here with how well it is supported, and whether that support goes toward the advert's promise.
| Idea | What it is | Evidence | Does this support the advert? |
|---|---|---|---|
| A stated split | A fixed percentage share of attraction for each sex. | Evidence: Marketing claim1 | Untested. One coaching website states a fixed percentage split of attraction for each sex, with no source for it. The split is a claim, not a finding. |
| No fixed formula | Whether attraction divides into fixed shares. | Evidence: Plausible2 | No. No study found supports attraction splitting into fixed percentages for lifestyle, status and looks. The closest studies report effect sizes for specific attributes in specific settings, and in the one real-world swiping study attractiveness carried the most weight among the attributes tested. |
| Looks and earnings | What raises how people evaluate a possible partner. | Evidence: Well supported3 | Partly. Studies of photographs, descriptions and first meetings find that physical attractiveness raised romantic evaluations about equally for both sexes, and earning prospects had only a small effect for either. That is a pattern, not a fixed share, and not one split per sex. |
| Stated ideals | What people say they want, compared with who draws them. | Evidence: Supported with caveats4 | No. Several studies find that what people say they want in an ideal partner predicts poorly who they feel drawn to after meeting in person. |
| One person's preferences | What one particular person ends up wanting. | Evidence: Plausible5 | No. In two speed-dating studies from one group, more than 100 traits and preferences that people reported before they met did not predict which particular person they would end up wanting. |
What would make it testable
As stated, it cannot be tested. Percentages of what, measured how, for which kind of relationship, by self-report or behavior, whether the shares add up, and how much people differ are all unstated.
A testable version Among adults rating dating profiles, how much of the variation in rated interest is explained by photo attractiveness, described lifestyle and described income, measured by a pre-registered model that also reports how much the raters differ?
The useful question underneath
Wanting to know what to work on. The aim 'I want the exact formula' becomes: make your real life legible, with a clear, current photo, an honest picture of how you spend your time, and conversation you can practice. No share of anyone's attraction is yours to set.
Lessons that use this
Sources
Each numbered mark above opens the evidence behind that idea: the label, what it does not show and where it comes from.
Evidence: Marketing claimThe Personal Brand Creator Pro site states that women are attracted to men by lifestyle (65%), status (25%) and looks (10%), and men to women by looks (65%), personality (25%) and status (10%), with no source for the split.
Why this label
Something a seller says about their product, without published evidence. A numerical attraction formula presented as fact. It is the kind of claim the claim-audit exercise questions: population, measure, relationship type, additivity.
What it does not show
- No source cited
- Percentages are presented as universal
Who it applies to
A pattern across groups of people, not a statement about any one person.
Sources
- Personal Brand Creator Pro (undated). Instagram Dating Coach for Men | Attract Elite Women, personalbrandcreatorpro.com.
- Personal Brand Creator Pro (undated). Dating Coach for Men FAQ | PBA, personalbrandcreatorpro.com.
Evidence: PlausibleNo study found supports a formula in which attraction splits into fixed percentages for lifestyle, status and looks; the closest studies report effect sizes for specific attributes in specific settings, and in the one real-world swiping study attractiveness dominated.
Why this label
One study, or indirect evidence from a nearby question. Demonstrates: the closest evidence is Witmer et al. (2025; 5,340 swipes, 445 daters), where a conjoint analysis found physical attractiveness dominating the selection decisions, with intelligence, height, job, bio and homophily effects 7 to 20 times smaller; and Joel et al. (2017) and Eastwick et al. (2022), which partition variance or test predictors and do not give additive weights. Existing claims: attraction-foundations-c2 (attractiveness is among the strongest predictors in first meetings) and evolutionary-mating-preferences-c2. Lifestyle is not measured as a category in these studies. Marketing framing: precise percentages for what attracts people, such as lifestyle, status and looks (common framing, unattributed). Bears on A4: §49 claim audit; supports the audit questions (population, measure, relationship type, self-report or behaviour, additivity, individual variation) and blocks showing such percentages as scientific fact.
What it does not show
- A 'No study found' claim: searched Crossref and OpenAlex for stated-versus-revealed preference, policy-capturing, conjoint and relative-importance studies of looks, status and personality; other studies exist (for example Li et al. 2002, Hitsch et al. 2010, Sprecher 1989) but were not read in full or lacked an abstract here.
- Witmer et al. (2025) covers one app's swiping decisions, is a field study of selection not a statement of what people 'care about', and needs hand-checking of its effect-size wording.
- The attractiveness dominance finding is a population pattern in one setting; it does not give a percentage of importance and is not additive across contexts.
- No replication of the conjoint study found; self-reported and stated weights measured elsewhere often diverge from choices (see attraction-foundations-c3).
Who it applies to
A pattern across groups of people, not a statement about any one person.
Sources
- Witmer, J., Rosenbusch, H., Meral, E. O. (2025). The relative importance of looks, height, job, bio, intelligence, and homophily in online dating: A conjoint analysis, Computers in Human Behavior Reports.
- Joel, S., Eastwick, P. W., Finkel, E. J. (2017). Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction, Psychological Science.
- Eastwick, P. W., Joel, S., Carswell, K. L., Molden, D. C., Finkel, E. J., Blozis, S. A. (2022). Predicting romantic interest during early relationship development: A preregistered investigation using machine learning, European Journal of Personality.
Evidence: Well supportedWhen people evaluate potential partners (photographs, descriptions or first meetings), physical attractiveness raised romantic evaluations about equally for both sexes, and earning prospects had only a small effect for either.
Why this label
A meta-analysis or several independent studies agree, with no serious failure to replicate. A meta-analysis of 97 samples found attractiveness predicted romantic evaluations moderately to strongly and earning prospects weakly, with sex differences in these links small and not significant, even though stated preferences differ by sex.
What it does not show
- Mostly evaluations of hypothetical targets, photographs or first meetings; mostly Western samples.
- Short-term evaluations, not long-run relationship outcomes.
- Disagreement exists on how best to test preference matching; not assessed here.
Who it applies to
A pattern across groups of people, not a statement about any one person.
Source
- Eastwick, P. W., Luchies, L. B., Finkel, E. J., Hunt, L. L. (2014). The predictive validity of ideal partner preferences: A review and meta-analysis, Psychological Bulletin.
Evidence: Supported with caveatsWhat people say they want in an ideal partner does a poor job of predicting who they actually feel drawn to after meeting someone in person.
Why this label
Several studies agree, with stated limits such as student samples or lab settings. Ideals reported before a speed-dating event did not predict real desire at the event, and in lab experiments, matching a written profile to someone's ideals raised interest, but the effect vanished after a live interaction. The traits that are decisive on paper are reinterpreted when you meet the person.
What it does not show
- Mainly speed-dating and lab samples (largely students, plus one middle-aged adult longitudinal sample), largely from one research group, so independence is limited.
- Ideals can predict outcomes in other stages, such as evaluating a current partner, per the review and the longitudinal study.
- Self-report of preferences and desire; people may lack introspective access, which is the authors' interpretation, not directly tested here.
Who it applies to
A pattern across groups of people, not a statement about any one person.
Sources
- Eastwick, P. W., Finkel, E. J. (2008). Sex differences in mate preferences revisited: Do people know what they initially desire in a romantic partner?, Journal of Personality and Social Psychology.
- Eastwick, P. W., Finkel, E. J., Eagly, A. H. (2011). When and why do ideal partner preferences affect the process of initiating and maintaining romantic relationships?, Journal of Personality and Social Psychology.
- Eastwick, P. W., Luchies, L. B., Finkel, E. J., Hunt, L. L. (2014). The predictive validity of ideal partner preferences: A review and meta-analysis, Psychological Bulletin.
Evidence: PlausibleBefore two people meet, more than 100 of their self-reported traits and preferences did not predict which particular person they would end up wanting.
Why this label
One study, or indirect evidence from a nearby question. Demonstrates: in two speed-dating studies, models fed more than 100 pre-date self-reports predicted some of how much people desired others in general and how much others desired them in general, but could not predict desire for a specific partner beyond those tendencies; in a preregistered 7-month study of 208 singles, the perceiver-by-target compatibility account got only modest support. Marketing framing: a single hidden attraction trigger that works on a particular person (see dating-frameworks-c7).
What it does not show
- Same research group produced both studies; no independent replication found
- Speed-dating and young single samples, mostly US
- Self-reported traits; models predict statistically, not for an individual
- Absence of prediction here does not show compatibility does not exist, only that these measures did not capture it
- Eastwick et al. 2022 ran a different design (7 months, 208 singles), not a replication
Who it applies to
A pattern across groups of people, not a statement about any one person.
Sources
- Joel, S., Eastwick, P. W., Finkel, E. J. (2017). Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction, Psychological Science.
- Eastwick, P. W., Joel, S., Carswell, K. L., Molden, D. C., Finkel, E. J., Blozis, S. A. (2022). Predicting romantic interest during early relationship development: A preregistered investigation using machine learning, European Journal of Personality.