Big data marketing or how to connect better with customers

Have you ever wondered what happens in 60 seconds on the Internet? According to the annual report of the DOMO company, 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 viewed 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 behaviour and improve the effectiveness of campaigns. Thus, big data marketing has given way. 

Big data marketing: closer to users 

Big data, in general, has its origins in the explosion of quantitative data in the digital context. It is a phenomenon closely linked to technological advancement: the more digital platforms there are, the more data is generated. In this sense, big data refers to the set of data 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, interactions on social networks, internet browsing behaviour or demographic data. This data is processed and analyzed using advanced data analysis tools to extract valuable insights 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 about a product that you just want or need? No, it is not magic or coincidence. It’s called personalization and it’s 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 behaviour 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 behaviours, interests. 
• Personalized recommendations: as we told you, companies use data-based recommendation algorithms to suggest relevant products or services to each customer. This improves the customer experience and increases the chances of conversion. 
• Personalized content: By analyzing data on browsing behaviour and social media interaction, companies can offer personalized content, such as emails, social media posts, or website messages, that are tailored to each individual’s specific interests. user. 
• Customer Experience Optimization – By collecting real-time data on customer interactions with websites, apps, and other channels, businesses can make real-time adjustments to improve the customer experience.  

Five uses of big data marketing 

We already told you 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! 

• Advertising Campaign Optimization: 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 to audience segmentation, ad design, or bidding strategies. 
• Predicting market trends: Companies can identify patterns in consumer behaviour that allow them to predict future market demands. This allows 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. 
• Improving customer experience:  Identify areas of improvement in the customer experience and take steps to optimize it. This may include improvements in customer service or personalization of the online shopping experience. 
• Attract new customers and retain existing ones: companies find behavioural patterns that indicate who their most valuable customers are and what strategies are most effective to attract and retain them. This allows them to personalize their messages and offers, more accurately target potential customers, and improve the customer experience to increase long-term loyalty and retention. 

Big data marketing success stories 

Large 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 behaviour. Thus, they offer highly personalized recommendations and increase conversion rates.  

Another case is Netflix (and almost any streaming platform) that personalize content recommendations based on user preferences, views, and ratings. This improves your experience and increases subscriber retention. 

Finally, Starbucks‘ big data marketing strategy that uses customer transaction data, location information, and social media data to personalize offers and promotions for its customers is also interesting. Additionally, they use the Starbucks Rewards mobile app to collect data on purchasing behaviour and offer personalized rewards, increasing customer loyalty and sales. 

If you want to expand your knowledge about this and many other areas related to marketing strategies, do not hesitate to follow us on the Educa.Pro blog.