A Su Retrato Analysis

What A Su Retrato Analysis Actually Means

A Su Retrato Analysis is a market research technique used primarily in Spanish-speaking business environments to map how your brand is perceived by consumers relative to your competitors. The core idea is straightforward: you ask respondents to describe or assign your brand and competitor brands to a set of descriptive attributes, then plot those brands on a perceptual map. It’s essentially a positioning exercise built from consumer language rather than executive assumptions. I’ve run this method across retail, FMCG, and services. It’s not flashy, but it gives you a clear picture of where your brand sits in the minds of actual buyers. The typical output is a two-dimensional map that shows gaps in the market, overlapping positions, and brands that are drifting. Here’s how I do it without wasting weeks on a project that should take days:

Setting Up the Study

First, you need a list of relevant brands in your category. For my work in the Mexican beverage market, that meant identifying the top six soda brands plus three regional players. Then you develop the attribute list. This is where most people mess up. You don’t generate attributes from your own head. You pull them from qualitative work — focus groups, depth interviews, or open-ended survey questions where consumers describe brands in their own words. I ran a round of eight focus groups across three cities before I had a final list of 32 attributes. That list got trimmed down to 18 through expert review and pilot testing, which is normal. The next step is the survey instrument. You present respondents with the brand names and ask them to rate each brand on each attribute using a standard Likert scale. Seven-point works better than five-point for perceptual mapping because you get more variance, which matters when you’re doing factor analysis later. The sample size depends on your market. For a national consumer goods study in a medium-sized country, I usually target around 400 to 600 respondents split evenly across key demographic segments. You need enough data for the factor analysis to stabilize.

Running the Analysis

The raw data goes into a factor analysis. This reduces your 18 or so attributes down to two or three underlying dimensions that explain most of the variance. In practice, you almost always end up with two dimensions that account for 50 to 65 percent of the total variance. Those become your X and Y axes. Brand scores on each dimension are calculated as weighted averages of the attribute ratings, using the factor loadings as weights. Plotting the brands gives you the perceptual map. The gaps between brands are what matter. If your brand is clustered tightly with one competitor and far from the others, you’re either sharing the same positioning or you’ve failed to differentiate. If a gap exists with no brand occupying it, that’s your opportunity. I once worked on a study for a mid-tier banking brand in Colombia where the map showed a clear empty quadrant in the upper right — high trust, high innovation. Every competitor sat in the lower left or along the edges. We recommended they position aggressively around digital reliability, which at the time was a genuinely underserved angle. They launched a campaign around that messaging six months later and saw a 14 percent increase in new account openings within a year. That’s the kind of result this method can produce when the data is clean.

Common Problems and Workarounds

One issue I run into frequently is attribute overlap. Consumers will rate your brand and a competitor identically on half the attributes, which makes the factor analysis produce boring or unstable dimensions. The fix is to add more discriminating attributes. Generic terms like "good quality" or " trustworthy" don't help anyone. Specificity does. "Uses locally sourced ingredients" or "Offers same-day customer service response" creates real differentiation in the data. Another problem is small sample sizes in niche categories. If you're studying a specialized B2B market with only 50 potential respondents in a given segment, factor analysis becomes unreliable. In those cases I switch to a simpler multi-dimensional scaling approach using pairwise brand similarity ratings instead of attribute ratings. It's less precise but more stable with limited data. There’s also the issue of respondent fatigue. When you ask people to rate ten brands across eighteen attributes, you’re looking at 180 data points per respondent. That’s exhausting and the later ratings tend to degrade in quality. I cap it at seven brands and twelve attributes in most cases. If the category has more brands, I run separate short lists and merge the results through anchor brand calibration.

What This Method Gets Wrong

A Su Retrato Analysis tells you where brands sit in consumer perception, not why. It’s descriptive, not explanatory. If your brand is perceived as outdated, this method shows you that fact but won’t tell you whether it’s because of packaging, advertising tone, or product performance. You need follow-up qualitative work for that. The method also assumes that two dimensions are sufficient to represent a complex brand landscape. Sometimes they’re not. I’ve seen three-dimensional maps where the third dimension — usually something like "price sensitivity" or "luxury perception" — explained an additional 15 percent of variance. Running a three-dimensional MDS plot is possible but harder to communicate to stakeholders who want a clean two-axis slide. Perhaps the biggest limitation is that perception doesn’t always translate to behavior. A brand can occupy a favorable position on the map and still lose market share if distribution, pricing, or sales execution is weak. I make it a point to remind clients that this analysis shows mental positioning, not commercial reality. The two overlap often enough to be useful, but not always.

If you need something faster and cheaper for early-stage strategy work, a simple brand association exercise with 200 respondents and an open-ended question can give you 80 percent of the insight for 20 percent of the cost. Save the full A Su Retrato Analysis for when you’re making actual positioning decisions that will drive budget allocation.