Top 5 Applications of Big Data in Digital Marketing

5 min read

Big data has brought opportunities as well as challenges to marketers. Digital marketers can reach more information to conduct a thorough marketing analysis. However, the situation requires them to possess in-depth knowledge of leveraging statistics and metrics to help them make a marketing strategy. In this article, I’ve listed the top 5 applications of big data in digital marketing.

Forecasting demand

One of the most common applications is that companies utilize Big Data to forecast demand. Demand forecasting is important as it reduces the risks of stockouts and controls the production cost, and it also brings new opportunities to grow revenue. By looking back on the data from the past, companies get to know what worked best for them before, why it did, and can try to create the same magical effect in the future. 

For example, when Hurricane Frances was about to hit Florida in 2004, Walmart’s chief information officer decided to study the trillion byte’s worth of shopper history and figure out what was sold the most when Hurricane Charley had stricken several weeks earlier. The data showed that besides flashlights and some other products that were popular before the storm, the sales of strawberry Pop-Tarts were 7 times higher than usual. Walmart quickly stocked a large amount of the strawberry Pop-Tarts, which almost sold out before the hurricane.

Planning for future campaigns

Another usage of past data is that marketers can plan future campaigns or activities more accurately. For instance, chain restaurants like McDonald’s can leverage customer order information to determine the performance of their products in their marketing campaigns. Marketers get to know which meals are popular and profitable, which meals are neither popular nor profitable, which meals are somewhere in between, etc. According to the data collected, prices and menus can be adjusted in the future to achieve better marketing results. 

Making wiser pricing decisions

Traditionally, companies price their services or products based on basic information like product cost, demand, or the general market price. With Big Data, the traditional pricing strategy has been largely improved. Big Data Analysis reveals more thoroughly which factors influence the perception of prices of different market segments. These factors vary under different situations, which may include the broader economic situation, product preferences, etc. Through big data analysis, marketers are able to set the best price for their products and services. 

For instance, in the B2B sector, companies prefer to make pricing decisions as granularly as possible because each business is different from the others. Big Data makes this process easier by automating the process of identifying narrow market segments and matching them with historical transactional data. Eventually, the factors that drive value for each segment are discovered, and this supports making better pricing decisions. 

Personalized Targeting 

Also known as one-to-one marketing, personalized marketing is about creating and delivering messages to individuals or groups of the audience after doing data analysis. Marketers analyze consumer data such as geolocation, browsing history, clickstream behavior, purchasing history, etc to know their audience better and provide more customized services.

This marketing strategy is widely used in product recommendations, targeted emails/ads, and so on. Amazon is a good example of product recommendation, using market basket analysis for cross-selling. Big Data helps Amazon to recommend a product based on the customer’s buying history, as well as the buying history of other people who bought the same item. Similarly, in the case of targeted emails, Big Data helps reveal information about the customers, including their interests, which specific products they were using, etc. The idea is to build a personal bond with customers, which can lead to increased sales.  

Performing customer sentiment analysis

To know their customers better, not only do companies collect customer information throughout their buying journey, but they also extract customer reviews on social media channels to conduct sentiment analysis. Sentiment analysis, also known as opinion mining, is about analyzing the underlying sentiment behind a customer’s comments – whether it is positive, neutral, or negative. After gathering customer feedback, companies are empowered to have an idea of how the market is perceiving their brand or products, and then they can work on whatever needs to be improved and even create new business opportunities. 

In conclusion

Big Data plays an important role in digital marketing. In the digital era, the data collection software companies like Octoparse have made data extraction easier than ever for non-tech-savvy marketers. Making decisions based on the comprehension of data can lead to important breakthroughs in business such as better control of inventory, bigger savings, and eventually higher sales turnovers. 



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