Esmeralda Cortesi, Data Analyst at WGSN, shares how WGSN uses search data to analyse future consumer trends.
Q: What do you do at WGSN?
My job is to add a data dimension to reports. The goal is to ensure our reports tell the full story, and data is essential for this.
A typical day includes deep diving into fashion or sustainability topics, talking to editors to share our findings and working with the design team to find the best way to visualise our insights. I am also part of our TrendCurve+ predictive analytics team, working to build shorter, more focused fashion planning reports centred around our machine learning forecasts.
All of this wouldn’t be possible without robust data sources, search data being one of them.
Q: What role does search data play in the trend forecasting process?
WGSN’s search index provides a historical view and a real-time snapshot of buzz around a topic on the most popular search engine platforms. This informs us what keywords users are searching for and is incredibly helpful in identifying the general interest in a vast range of trends. To go even deeper, we also include retail search data, which gives us product-focused knowledge and a full view of what items people are looking to buy.
This data is a direct window into users’ needs and interests. It is reliable in telling us whether a trend has been picked up by a vaster user base, such as Mainstreamers, and not only staying among a small circle of Innovators.
We often look at search data by country or whether a trend has surged around specific dates, following an event or a media phenomenon. We map out its timeline and geography, or refer to a topic’s related search terms to understand the different nuances the trend is shaping into.
For example, when researching carbon capture and storage (CCUS) technology for the Sustainability Bulletin, we noticed the topic has been growing slowly, but had spiked at the beginning of 2021. From this, we realised the story was right: in January 2021 Elon Musk announced a $100m prize for CCUS technology, but since then there have been difficulties in the development and adoption of CCUS, which slowed down its growth.
Overall, it is an essential step in understanding whether a trend is here to stay and is used in trend forecasting as both a confirmation and a lead.
Q: What kind of trends can you determine using search data and how far ahead can you predict these?
Search data is useful to assess factors such as a trend’s seasonality and, as we have data that spans multiple years, its long-term behaviour, but the real value comes when we combine this source with WGSN’s four other main data sources and insights from our analysts. In my experience, search data is particularly handy for verticals such as Food & Drink, Interiors and Consumer Tech, as people tend to be more specific with what they search for than in Fashion or Beauty.
Q: What’s an interesting/unexpected trend you’ve uncovered from search data?
It has been WGSN’s priority to forecast the impact of rising living costs across all verticals, and search data has been crucial in uncovering unexpected ways consumers are looking for cost-saving items, going beyond just where and how ‘cost of living’ is trending.
For example, the search index for thermal apparel is up 200% YoY since 2021. The category has surpassed more expensive alternatives such as cashmere or merino wool for the first time in 2021, and is expected to rise even more in 2022/2023. Search data indicates that consumers have been preparing earlier for the winter months and Holiday season, as searches began to surge in September compared to the usual uptick in mid-November.
In Food & Drink, search data highlighted the buzz around energy-efficient cooking appliances by capturing at early stages the peak interest in air fryers and microwaves, cheaper ready-made meals and meatless ingredient substitutions such as eggs, beans and vegetables recipes.
As an intersection between the energy crisis and sustainability we looked at YouTube search data to forecast adoption of solar-powered electronics and panels, and were surprised to see how clearly the data spoke: since 2020 YouTube search index for these topics increased by 45%.
Q: How can brands use WGSN’s Search data to plan ahead?
Search data provides knowledge on consumer behaviour often earlier than Shelf or Social data, especially for trends that affect a wider percentage of consumers. Search data is also useful for spotting geographical patterns and to capture early-on trends in countries that often lead innovation. Going back to air fryers for example, this trend only recently began to surge in the UK, but had already emerged in countries such as Taiwan, the US and the Netherlands in 2020.
The real value for brands is not only the search data itself, which can be overwhelming if used alone, but the forecasts we build by pairing it with Social, Sentiment, Show and Shelf data, and by working with industry experts. This way we are able to provide actionable points that respond to consumers’ concerns.
For more information on TrendCurve+ and our data-driven forecasting, please visit our website or request a demo here.