The LCBO And Structural Equation Modelling: How To Test Emotional Brand Connections
Likely the most well-known crown corporation in Ontario, the LCBO is actually one of the world’s largest buyers of alcoholic beverages. With 639 retail stores offering about 24,000 products annually, it’s no wonder. They service the beverage needs of an entire province and, last year, they made over $4.9 billion in sales – but for the LCBO, it’s about a lot more than that.
Since 1990, they’ve won over 200 awards in customer service – and keeping that level of care is no small feat. The LCBO is dedicated to understanding their customers, and that dedication is exactly what led them to partner with Environics Research in 2009. It was then that we began conducting our ongoing study to further the LCBO’s understanding of their customers.
Specifically, the LCBO sought to understand how exactly their customers perceived their brand, and how their emotions informed those perceptions. To put it another way, the LCBO already had a solid understanding of the tangible benefits they were providing, but what about the intangibles customers were receiving? How could they go about understanding the emotional connections and benefits customers shared with the LCBO?
It’s not an easy question to answer. It was clear to us at Environics that a standard customer satisfaction survey wouldn’t give us the insights that we needed. How could customers accurately explain their own emotional connections – and the roots of those connections – when many of these connections are completely subconscious? We needed a more in-depth analytical tool to draw out the emotional drivers that customers themselves might not even be aware they have. We needed an approach based on a structural equation model.
Structural Equation Modelling (SEM)
Now, unless you’re reading this as an experienced analyst, you’re probably wondering what SEM is. In it’s simplest terms, SEM is a statistical tool that lets us explore and measure the strength of causal relationships using a combination of qualitative ideas and quantitative data. It’s both confirmatory and exploratory, in that it can both allow us to test theories about how variables that we cannot directly measure (latent variables) cause or are caused by other variables, and to what degree, and also allow for new theories to be formed and tested.
As you can see from the example structural equation model below, various pathways are hypothesized and tested with customer data. Here, like the LCBO’s model, those pathways begin with in-store activators, which lead to customer benefits and key drivers of brand equity. Finally, those pathways end when an emotional or a rational connection, or both, is formed between the customer and that brand. It should also be noted that every different brand will have its own unique set of hypothetical pathways. The way that the model looks, and the pathways that are formed, will depend on how customers interact with the brand in question.
The basic strength of the approach taken here is that it allows us to estimate variables that we can’t measure directly
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