Why is Important to Identify Your True Competitors (and How)

From my experience, both retailers and consumer goods companies use category definitions almost always dictated by themselves, even when somehow considering consumer behavior. For example, Corona is considered a "premium" beer, and Glacial a "value" beer, but this classification was not established by consumers (and, most of the time, they don’t even know about it).

In many cases, these definitions do not reflect how shoppers actually behave. This is why it's crucial to focus on DATA! And by data, I mean the true behavior of shoppers (what they actually do, not what they say they do).

Understanding who your brand is truly competing with is one of the key reasons this is relevant. Your brand competes with brands that consumers buy when yours is unavailable or too expensive. The implications are many: for example, how to communicate with consumers, in which channels your brand should be present, and how to position your products on store shelves.

There are sophisticated methodologies to determine direct competitors (primarily by understanding cross-penetration between brands and "consumption repertoires" – which requires household penetration data that is not always easily accessible). However, there’s a simple way to form a strong hypothesis, using data that most companies already have "in-house": understanding the cross-elasticity of your brand relative to others.

• Start with the relative price and relative volume of your brand compared to another brand (or a set of other brands); relative price and volume help minimize factors like seasonality, for example.

• Analyze the correlation of these factors: if there’s a strong correlation, there’s a good chance these brands are competitors – the variation in relative volume is largely explained by the variation in relative price. If there’s no strong correlation, the price change of one brand doesn’t significantly affect the volume of the other, indicating that the brands are likely not direct competitors.

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Practical example in snacks category:

There was a strong hypothesis that extruded snacks (like Cheetos) and potato chips (like Ruffles) competed with each other.

Upon analyzing the data, we found a very low R² (correlation) of less than 10% between the relative price and relative volume of the categories. The low correlation suggests that changes in relative price don’t significantly explain variations in relative volume, indicating low cross-elasticity between the categories.

In a specific region, we found a higher correlation (about 50%). However, the "slope" of the curve was very low – indicating that significant price changes led to only minimal volume variations.

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This same analysis can also be applied between brands, packaging types, and others. But remember: correlation is not causation – it’s always important to dig deeper into the data and understand how the shopper thinks/behaves, without relying solely on this number as an absolute truth.

When to use this methodology?

It works well for understanding shopper behavior using data that most companies already have in-house. It helps form solid hypotheses about who the true competitors are – which can have various implications, such as defining the "perfect store" strategy.

When not to use it?

This is not a detailed elasticity study, so I wouldn’t recommend using it for tactical pricing decisions. For that, more robust methods, such as a conjoint analysis, should be used.

Contact us at contact@insignia.partners and discover how we can contribute to the success of your strategy.

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Bruno Bullio

Associate Partner

Bruno brings 15 years of experience in strategic consulting, specializing in retail and consumer goods, with a strong track record across Brazil and Latin America.