As technology advanced, the scope of treasure hunting expanded, and searching for treasure in previously inaccessible or unknown locations became possible, providing new insights into the world.
Modelling consumer trends with large-scale scraping can be compared to a treasure hunt. Like a treasure hunter uses a map and a shovel to uncover hidden riches, a market researcher uses software and a database to uncover hidden insights about consumer behaviour.
Data-driven decision-making is an integral part of businesses today. By staying up-to-date on what consumers are interested in, companies can adapt their offerings to meet the needs of their target market and stay ahead of the competition. Therefore, companies can better position themselves for success by being proactive and anticipating consumer trends.
Monitoring consumer trends
Consumer trends reveal patterns and behaviours of consumers about products and services: A variety of factors, including changes in consumer preferences, economic conditions, and the availability of new products and technologies, can influence these trends. The researchers at McKinsey predict a fair share of industrial changes as well. According to them, companies who want to be at the top in 2030 must research upcoming trends and start planning for them now.
According to the same study, to take advantage of data and analytics, companies need to select various data sources and govern them properly, build models that will provide intelligence from said data, and effectively use the insights obtained. This necessitates extensive analytics capability, often sourced externally.
Additionally, companies need to monitor consumer trends to stay ahead of the competition constantly. This can be a daunting task, especially given the vast amount of data that is available online. Fortunately, large-scale scraping can help make this process easier and more efficient.
One way businesses can monitor consumer behaviour trends and make decisions regarding them is by scraping publicly available data. Large-scale scraping is used to collect large amounts of data from the internet, which involves using specialised software to extract information from websites and other online sources.
Armed with these valuable insights, companies can make better decisions in their marketing strategy, product development, and customer service processes. Therefore, web scraping can be helpful for businesses that want to gather data on consumer trends.
Web scraping for consumer trend modelling
As, on average, people spend almost seven hours on the internet daily, they inevitably generate enormous amounts of data about their attitudes, preferences, and behaviours as they immerse themselves in the digital world.
According to a study on “Web Scraping for Consumer Research“, – anyone researching consumers might profit significantly from these digital footprints. The publicly available data offers an unrivalled view into consumer conduct, enabling researchers to assess social and usage behaviours that would otherwise be challenging to notice, document, and examine.
However, to reveal consumer trends, it is necessary to understand the mechanics and nuances of working with web-scraped data. To model consumer trends using large-scale scraping, businesses typically begin by defining what they want to analyse. It could include changes in consumer preferences, adopting new technologies, or shifts in spending habits.
Businesses will use scraping tools to collect data from various online sources, such as E-commerce platforms and financial or news websites. This data is then organised while statistical and analytical methods are used to identify and analyse trends.
Once consumer trends have been identified and analysed, businesses can use this information to inform their decision-making and strategy and construct a consumer behaviour model to anticipate future trends. For example, they may use the insights they have gained to develop new products or services, target specific segments of the market, or to adjust their marketing and advertising efforts. This can assist businesses in making better-informed judgments regarding product development, marketing strategy, and other critical business areas.
Alternative data usefulness
A scientific publication by Johan Bollen provides a fantastic illustration of the usefulness of auxiliary data to draw further insights (or support findings). He gathered large-scale Twitter feeds, employed technologies to automatically assign moods (such as Calm, Alert, Sure, Vital, Kind, and Happy), and then correlated the mood to the Dow Jones Industrial Average (DJIA). Finally, the study discovered that Twitter moods accurately predict DJIA up/down movements by 86.7%.
Although Twitter users are not “consumers” of the Dow Jones Industrial Average, it is clear that specific signals might help us forecast economic motivations. E-commerce organisations may use web scraping and internal data collecting to adjust effectively to uncover clues for longer-term customer trends.
Overall, alternative data collection gives valuable insights into trends and patterns that standard financial data may not reveal, enabling investors to make more educated choices and earn higher returns on their investments.
In conclusion, large-scale scraping can be a valuable tool and treasure for modelling consumer trends. Collecting and analysing data from a wide range of online sources, including websites and E-commerce sites, can gain insights into consumer behaviour and preferences and identify patterns and trends in the data.
Businesses can then use this information to make more informed decisions about product development, marketing, and sales strategies. In addition, it can help them to understand their customers better and stay ahead of changing consumer trends.
Gediminas Rickevičius, VP, Global Partnerships at Oxylabs.
