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  • Case study

    Category insights for Pasta

    How Sila helped understand usage patterns for pasta in Saudi Arabia.

    There was limited understanding of the usage and attitude towards Pasta, the key brand and their perceptions in the GCC.

    The challenge? Find out why users in Saudi Arabia weren't buying pasta sauce.

    A challenge that had been identified – the pasta sauce category was not keeping pace with the pasta category itself. A curiosity at the best of times.

    Barilla looked to Sila to help solve this via its insight and researchers to uncover what might be the cause of this, and what could be done to address it.

    Start at the start.

    Rather than starting by asking questions, we started by looking at audiences. It’s the Sila approach – audience first – that yields a considerable bias reduction.

    By looking at those audiences, and importantly, the people who create the recipes they follow, we were able to better understand how pasta was being used to arrive at the insight.

    This approach allows us to look at data a different way – focusing on real-world results rather than the researcher’s own opinions or assumptions.

    Humans + AI = insight from data.

    At the best of times, data can only do so much. The need for processing the information into workable insight is the next step.

    Sila’s researchers took the substantial data points and analysed trends, along with personality insights, to understand the issue, and more importantly, the usage better. The data is then trended and allows for a greater appreciation of a trend, or just momentary blips.

    The benefits of this approach are:

    • Greater vision of the results;
    • Trended data over time gives strong indicators towards long term usage;
    • Normalising results for different quantities allows ingredient examination trended;
    • Audience reviews allow personality and other traits from behaviours to be better understood.

    What did we find?

    An anomaly, a trend, and a crushing problem.

    Once our team reviewed the data there was several findings that stood out. The details are confidential, but the data pointed us towards:

    • Usage of the products were incorrect among a core demographic;
    • Performance was impacted;
    • Disappointment followed; 
    • Products weren’t then being re-tested or repurchased by the target demographic;
    • This was a long term trend and wasn’t one that was caused by any specific incident;
    • The crushing problem? A cultural insight that was relatively simple to fix but needed to be educated. Why was it crushing? The target group were doing everything right, just in the wrong context.

    For the details, you’ll have to reach out to us!

    What then of the data? What happened next?

    Following the insight received through the analysis, our Influence team then created a brief around the findings.

    The first step was to gently educate the audience, the second step was to encourage trial and the last step was to get feedback on the trial.

    Interested in implementing
    Sila into your business workflow?
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