Have you ever wondered what happens on the internet in 60 seconds? According to theDOMO’s annual report, by the end of 2023, each person was generating 102 Mb of data per minute. During the same period, 360,000 tweets were posted on X, 241 million emails were sent and 694,000 Reels were shared via Instagram messages, and Taylor Swift’s music was played 69,400 times. This situation, brought about by the evolution and development of technology, has created what is known as big data.
Indeed, brands have used this environment of big data to gain a better understanding of their customers, identify trends in consumer behaviour and improve the effectiveness of their campaigns. This has paved the way for the big data marketing.
Big data marketing: getting closer to users
The big data, broadly speaking, has its origins in the explosion of quantitative data in the digital context. It is a phenomenon closely linked to technological progress: the more digital platforms there are, the more data is generated. In this regard, the big data refers to a dataset whose size exceeds the capacity of standard database software tools to capture, store, manage and analyse (McKinsey Global Institute, 2011).
Based on this definition, the big data marketing It is a strategy that collects data from various sources, such as commercial transactions, social media interactions, internet browsing behaviour or demographic data. This data is process and analyse using advanced data analysis tools to extract valuable insights about customers and the market.
In this way, brands can segment their audience more precisely, personalise customer experiences, to improve the effectiveness of advertising and marketing campaigns, identify opportunities market share and optimise the return on marketing investment.
Personalisation as part of big data marketing
Haven’t you ever found that sometimes, when you’re browsing the web, an advert pops up for a product you actually want or need? No, it’s not magic or a coincidence. It’s called customisation and it is key to the big data marketing. By tracking clicks on specific links, search history, form submissions or the time spent viewing content on a particular topic, companies gain an insight into consumer behaviour and preferences.
Here are a few ways in which personalisation is integrated into big data marketing:
- Precise segmentation: Data analysis enables companies to segment their audience into more specific groups based on demographic characteristics, purchasing behaviour and interests.
- Personalised recommendations: As we mentioned, companies use data-driven recommendation algorithms to suggest relevant products or services to each customer. This improves the customer experience and increases the chances of conversion.
- Personalised content: By analysing data on browsing behaviour and social media interaction, companies can offer personalised content – such as emails, social media posts or messages on websites – that is tailored to each user’s specific interests.
- Optimising the customer experience: 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’ve already given you a sneak preview of two ways to use the big data marketing: personalisation and market segmentation. In addition to these, there are other applications that can benefit businesses and brands. Let’s take a look!
- Optimisation of advertising campaigns: Big data enables companies to analyse the performance of their advertising campaigns in real time and make adjustments as necessary to improve effectiveness and maximise return on investment. This may include adjustments to audience targeting, ad design or bidding strategies.
- Market trend forecasting: Companies can identify patterns in consumer behaviour that enable them to forecast future market demand. This allows them to anticipate customer needs and adjust their marketing strategy accordingly.
- Price optimisation: By using dynamic pricing analysis techniques and market demand data, companies can adjust the prices of their products or services to maximise revenue and profitability.
- Improving the customer experience: identifies areas for improvement in the customer experience and takes steps to optimise it. This may include improvements to customer service customer or the personalisation of the online shopping experience.
- Attracting new customers and build loyalty in addition to the existing ones: Companies identify behavioural patterns that indicate who their most valuable customers are and which strategies are most effective at attracting and retaining them. This enables them to personalise their messages and offers, target potential customers more accurately, and improve the customer experience to boost long-term loyalty and retention.
Success stories in big data marketing
Major brands have already incorporated the big data marketing as a fundamental and essential part of their marketing strategy. For example, Amazon It uses machine learning algorithms to analyse consumer behaviour. This enables it to offer highly personalised recommendations and boost conversion rates.
Another example is Netflix (and almost any streaming platform) that tailor content recommendations based on users’ preferences, viewing history and ratings. This improves the user experience and increases subscriber retention.
Finally, the big data marketing strategy of Starbucks which uses customer transaction data, location information and social media data to personalise offers and promotions for its customers. It also uses the Starbucks Rewards mobile app to collect data on purchasing behaviour and offer personalised rewards, thereby increasing customer loyalty and sales.
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