As a marketer data is one of your biggest weapons. It helps you identify trends, business opportunities and fine tune marketing campaigns. In this article I will show you with examples how you can get access to amazing publicly available data from Google in Google Cloud Console and how to write SQL queries to get impactful data about fashion industry trends.
The fashion industry is constantly evolving, with new trends and styles emerging every season. As a marketer, it is important to stay up-to-date with the latest trends and consumer preferences in order to effectively target your audience. One way to gain insight into these trends is through the use of BigQuery Public Dataset – bigquery-public-data.google_trends. In this article, we will explore how this dataset can be used to identify marketing trends in the fashion industry.
Get access to Google Trends dataset
BigQuery Public Dataset – bigquery-public-data.google_trends, is a collection of Google Trends data that is publicly available on Google Cloud Platform. The dataset contains information on the search volume and popularity of specific keywords and phrases over time, across different regions and categories. This data can be used to gain valuable insights into consumer behavior and preferences, which can inform marketing strategies for fashion brands and retailers.
To get started with analyzing fashion trends using this dataset, you will first need to set up a Google Cloud Platform account and create a BigQuery project. Once you have done this, you can access the bigquery-public-data.google_trends dataset and start exploring the data.
Public Datasets in Google Cloud for marketers
Google Cloud offers a number of public datasets that can help marketers gain insights into their target audiences and optimize their marketing strategies. Here are some examples:
Google Trends: This dataset provides information on the popularity of various search terms over time and across different regions. Marketers can use this information to identify emerging trends, track the popularity of their brand or products, and optimize their SEO strategy.
Google Analytics Sample Dataset: This dataset provides a sample of website analytics data, including information on page views, sessions, bounce rate, and more. Marketers can use this data to gain insights into how users interact with their website, identify areas for improvement, and optimize their website for conversions.
Open Images Dataset: This dataset provides a large collection of images with associated annotations, such as labels and bounding boxes. Marketers can use this dataset to train computer vision models for image recognition and classification, which can be used to automate tasks such as product tagging and image analysis.
Census Bureau Data: The US Census Bureau provides a variety of datasets related to demographic and socioeconomic data, such as population, income, education level, and more. Marketers can use this data to gain insights into their target audience’s demographics and tailor their marketing campaigns accordingly.
NOAA Weather Data: The National Oceanic and Atmospheric Administration (NOAA) provides a variety of weather-related datasets, including historical weather data, real-time weather forecasts, and climate projections. Marketers can use this data to optimize their advertising strategies based on weather conditions, such as promoting cold-weather clothing during cold spells.
Writing your queries
In our case we will use Google Trends dataset to find out some data insights for fashion industry. One way to use this dataset is to identify popular fashion brands and styles. You can do this by querying the dataset for specific keywords or phrases related to fashion, such as “streetwear”, “sustainable fashion”, “luxury brands”, or “activewear”. By analyzing the search volume and popularity of these keywords over time, you can identify which brands and styles are currently trending, as well as which ones are declining in popularity. Here is an example of how to do this in SQL:
SELECT keyword, date, SUM(value) AS search_volume FROM `bigquery-public-data.google_trends.daily_trends` WHERE keyword IN ('streetwear', 'sustainable fashion', 'luxury brands', 'activewear') AND geo_code = 'US' -- limit results to United States AND date BETWEEN '2018-01-01' AND '2022-12-31' -- limit results to years 2018-2022 GROUP BY keyword, date ORDER BY keyword ASC, date ASC
Another way to use the dataset is to identify seasonal trends in fashion. For example, you can query the dataset for keywords related to specific seasons or events, such as “spring fashion”, “summer dresses”, or “holiday party outfits”. By analyzing the search volume and popularity of these keywords over time, you can identify which styles and trends are most popular during certain times of the year, and tailor your marketing campaigns accordingly.
SELECT keyword, SUM(value) AS search_volume FROM `bigquery-public-data.google_trends.daily_trends` WHERE keyword IN ('streetwear', 'sustainable fashion', 'luxury brands', 'activewear') AND geo_code = 'US' -- limit results to United States AND date BETWEEN '2022-01-01' AND '2022-12-31' -- limit results to year 2022 GROUP BY keyword ORDER BY search_volume DESC
You can also use the dataset to identify regional trends in fashion. By querying the dataset for keywords related to specific regions or cities, such as “New York fashion”, “Paris fashion week”, or “Tokyo streetwear”, you can gain insight into which styles and trends are most popular in different parts of the world. This can help you target your marketing campaigns to specific regions and audiences, and tailor your product offerings to meet their preferences.
SELECT geo_name, keyword, SUM(value) AS search_volume FROM `bigquery-public-data.google_trends.daily_trends` WHERE keyword IN ('New York fashion', 'Paris fashion week', 'Tokyo streetwear') AND date BETWEEN '2022-01-01' AND '2022-12-31' -- limit results to year 2022 GROUP BY geo_name, keyword ORDER BY geo_name ASC, search_volume DESC
In conclusion, BigQuery Public Dataset – bigquery-public-data.google_trends is a powerful tool for identifying marketing trends in the fashion industry. By analyzing search volume and popularity data for specific keywords and phrases, you can gain valuable insights into consumer behavior and preferences, and tailor your marketing strategies accordingly. Whether you are looking to identify popular fashion brands, seasonal trends, or regional preferences, this dataset can provide you with the data you need to make informed decisions and stay ahead of the competition.