The road to hyper-personalization: an opportunity in the current Latin American eCommerce scenario
Increasingly discerning customers demand personalized experiences, so how do we keep up with them?
The digital shopper market in Latin America experienced a significant increase in recent years, driven by the pandemic, and the accelerated expansion of technology infrastructure, increased adoption of mobile devices and the growth of the digital economy.
Latin America is home to more than 300 million digital shoppers, a figure that is forecast to grow by more than 20% by 2027 | Statista - statistics portal
In 2022, the return of regular activity in physical stores, coupled with the impact of inflation on consumption, caused a contraction in online retailing throughout the country. In addition, users are becoming more demanding and informed.
The challenge for 2023 in this context, to combat the slowdown in consumption, or better yet, for companies to continue to grow, is to strengthen relationships with their customers. Knowing much more about them, creating increasingly personalized messages, and exceeding their expectations. To be one step ahead of what they need, to offer them what they expect from each brand, at the right time.
62% of consumers expect the companies they buy from to recognize them as individuals and know their interests.
This requires creating unique and hyper-personalized experiences. To achieve this, it is necessary to have a Customer Engagement platform that contributes to a better understanding of customers.
The hyper-personalization
Hyper-personalization is a marketing approach that seeks to create a highly personalized user experience tailored to the individual needs and preferences of each customer (which some time ago seemed like a dream for every marketer). To reach this level of personalization, you need to collect and analyze large amounts of customer data (including preferences, purchase behaviors, browsing history, past interactions and thousands of other pieces of information left by each user's digital DNA), which can be a big challenge without the right tools.
Once the data has been collected, patterns and trends must be analyzed and identified, customers must be segmented according to their needs and preferences, and their behavior must be predicted.
Today, there are platforms with machine learning capabilities for customer segmentation. They work by using artificial intelligence algorithms to analyze consumer behavioral data: past purchases, online searches and website navigation. These habits can help the algorithm understand patterns and trends. The information obtained by AI is used to predict which products and services are most likely to interest a shopper, increasing the likelihood of conversion and customer loyalty.
