Customer Engagement in Metaverse Using Big Data

Customer Engagement in Metaverse Using Big Data

Devesh Bathla, Raina Ahuja
Copyright: © 2024 |Pages: 12
DOI: 10.4018/979-8-3693-2215-4.ch005
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Abstract

Customer engagement and customer retention are closely linked, and the metaverse offers a unique opportunity for brands to build lasting relationships with their customers. Metaverse allows brands to create a virtual representation of their stores and to interact with customers in that space. The metaverse offers a platform for community building, where brands can create a sense of belonging and connection among their customers. Brands can create virtual events, social spaces, or games that bring their customers together and foster a sense of community. By building a community around their brand, customers are more likely to feel connected to the brand and to each other, which can increase the likelihood of repeat purchases and brand advocacy. The metaverse concept has been popularised in science fiction and video games but has gained momentum in recent years as a potential next step for the internet.
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Introduction To Big Data

Big data refers to large and complex data sets that are difficult to process using traditional data processing techniques (Elgendy & Elragal, 2014). Various sources, such as social media, e-commerce transactions, and customer interactions, typically generate these data sets. Big data has become an increasingly important tool for businesses, as it can provide valuable insights into customer behaviour and preferences.

Brands can use big data for customer personalisation in a number of ways. By analysing large data sets, brands can better understand their customers' needs and preferences and use this information to create personalised experiences that better meet their customers' expectations. One of the key ways that brands use big data for customer personalisation is by analysing customer behaviour. By tracking customer interactions and purchase history, brands can identify patterns and trends in their customers' behaviour (Campbell et al., 2020). This information can be used to create personalised recommendations and offers tailored to each customer's interests and preferences.

For example, Amazon is known for its personalised product recommendations. By analysing customer behaviour and purchase history, Amazon is able to suggest products that customers are likely to be interested in. This helps customers find what they are looking for more quickly and increases the likelihood of purchasing.

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