Big Data in marketing: how to improve your segmentation

Have you ever wondered what happens in 60 seconds on the Internet? According to the annual report from the company DOMO, at the end of 2023, each person generated 102 Mb of data per minute. In the same period of time, 360,000 tweets were published on X, 241 million emails and 694,000 reels were sent through Instagram messages, and Taylor Swift was played 69,400 times. This situation, caused by the evolution and development of technologies, has created what is called big data.  

Precisely, brands have used this context of large volumes of data to better understand customers, identify trends in consumer behavior, and improve the effectiveness of campaigns. Thus, big data marketing has emerged. 

Big data marketing: closer to users 

Big data, in general, originates from the explosion of quantitative data in the digital context. It is a phenomenon closely linked to technological advancement: the more digital platforms exist, the more data is generated. In this sense, big data refers to data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze (McKinsey Global Institute, 2011). 

Based on this definition, big data marketing is a strategy that collects data from various sources, such as commercial transactions, social media interactions, internet browsing behavior, or demographic data. This data is processed and analyzed using advanced data analysis tools to extract valuable information about customers and the market. 

In this way, brands can segment their audience more precisely, personalize customer experiences, improve the effectiveness of advertising and marketing campaigns, identify market opportunities, and optimize the return on marketing investment. 

Personalization as part of big data marketing 

Hasn't it happened to you that sometimes you are browsing the web and an ad appears for a product you exactly want or need? No, it's not magic or coincidence. It's called personalization, and it is key in big data marketing. Through clicks on certain links, search history, forms, or time spent viewing content on a topic, companies get an idea of consumer behavior and preferences. 

We explain some ways in which personalization is integrated into big data marketing: 

• Precise segmentation: data analysis allows companies to segment their audience into more specific groups based on demographic characteristics, purchasing behaviors, and interests. 

• Personalized recommendations: as we said, companies use data-driven recommendation algorithms to suggest relevant products or services to each customer. This improves the customer experience and increases conversion chances. 

• Personalized content: by analyzing data on browsing behavior and social media interaction, companies can offer personalized content, such as emails, social media posts, or messages on websites, that adapt to the specific interests of each user. 

• Customer experience optimization: by collecting real-time data on customer interactions with websites, applications, and other channels, companies can make real-time adjustments to improve the customer experience.  

Five uses of big data marketing 

We already mentioned two uses of big data marketing: personalization and market segmentation. Added to these are other applications that can benefit companies and brands. Let's take a look! 

• Optimization of advertising campaigns: big data allows companies to analyze the performance of their advertising campaigns in real time and make adjustments as necessary to improve effectiveness and maximize return on investment. This may include adjustments in audience segmentation, ad design, or bidding strategies. 

• Prediction of market trends: companies can identify patterns in consumer behavior that allow them to foresee future market demands. This enables them to anticipate customer needs and adjust their marketing strategy accordingly. 

• Price optimization: using dynamic pricing analysis techniques and data on market demand, companies can adjust the prices of their products or services to maximize revenue and profitability. 

• Customer experience improvement: it identifies areas for improvement in the customer experience and takes measures to optimize it. This may include improvements in customer service or the personalization of the online shopping experience. 

• Attracting new customers and building loyalty with existing ones: companies find behavioral patterns that indicate who their most valuable customers are and what strategies are most effective for attracting and retaining them. This allows them to personalize their messages and offers, target potential customers more accurately, and improve the customer experience to increase long-term loyalty and retention. 

Success stories of big data marketing 

Major brands have already incorporated big data marketing as a basic and essential part of their marketing strategy. For example, Amazon uses machine learning algorithms to analyze consumer behavior. Thus, they offer highly personalized recommendations and increase conversion rates.

Another case is Netflix (and almost any streaming platform) that personalizes content recommendations based on user preferences, views, and ratings. Thus, it improves their experience and increases subscriber retention. 

Finally, Starbucks' big data marketing strategy is also interesting, as it uses customer transaction data, location information, and social media data to personalize offers and promotions for its customers. Likewise, they use the Starbucks Rewards mobile application to collect data on purchasing behavior and offer personalized rewards, which increases customer loyalty and sales. 

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