Digital transformation has become one of the main drivers of retail today, requiring companies and brands to invest in solutions aimed at effective performance in the virtual environment. Digitalization, in addition to strengthening and increasing the visibility of products and services, creates opportunities for innovation in the shopping experience, contributing to a projection of over US$100 trillion for the global economy in 2025, according to data from the World Economic Forum.
The advancement of Big Data is a clear example of this transformation, enabling the identification of behavior patterns and consumer preferences. Based on cross-referencing and massive data analysis, it has become possible to personalize offers and target campaigns in an individualized way, providing a more relevant and engaging shopping experience. It is important to highlight that a significant dividing line between the use of business intelligence data and big data, beyond the volume of data, is the ability to make decisions based on present data and not just past data, given the high processing power of technologies used in Big Data.
One of the most notable examples of using this feature is Amazon, which applies algorithms to suggest products based on previous purchases and each user's profile – sometimes even providing recommendations based on products already in your cart. Not for nothing, according to analyst Mordor Intelligence, the Big Data market in the commercial sector was estimated at $6.38 billion last year and is projected to reach $16.68 billion by 2029. If the scenario is confirmed, the amount would represent an average annual growth of 21.2%.
Operational efficiency is also greatly enhanced by intelligent data management. Tools that optimize inventory control, demand forecasting, and logistics are essential to anticipate consumption trends and maintain ideal operational levels, preventing excesses or shortages of supplies. Furthermore, it is necessary to highlight the integration of various sales channels – or in other words, the much-discussed omnichannel approach – which allows the consumer to transition seamlessly from an online store to a physical or mobile store. Thus, it is possible to consolidate a smooth purchasing journey and make it easier for the operation to be completed or even repeated.
Some of the world's largest retailers have a predictive logistics algorithm that cross-references user location data, page access volume for certain products, cart data, and estimated conversion rates to expedite the fulfillment process (i.e., a set of logistical operations involving the customer's order until the product is delivered). Thus, it is possible to separate the products in the logistics warehouse even before the items are actually purchased.
But beyond the impacts on operations, how can we also increase customer loyalty through data? Firstly, attracting clients who tend to be more loyal. It is possible to analyze a company's historical order data and understand which items brought customers with the highest purchase recurrence, and develop a price elasticity strategy for these items, determining the ideal pricing.versusthe existing competition to increase the conversion of these loyal consumers.
A second point is to understand what motivates the customer through data, which can be achieved by conducting surveys with the customer base and using gamified solutions with offers based on the results of this study. The most recommended method for using this survey is theOctalysiswith questions like: What are my client's purposes? What does my client do? What empowers my client? What creates a feeling of possession? What is an influence for my client? What sparks curiosity? What benefits and advantages would my client never want to lose? By collecting this data and building a retention strategy, the loyalty results will certainly increase.
However, Big Data does not generate this revolution alone or in isolation. Other resources – and here, of course, we need to reinforce the protagonism of artificial intelligence (AI) – play a role as a key competitive differentiator for brands. AI-generated optimization can represent cost reduction, improved operational efficiency, and a series of other benefits, but it is the digital optimization driven by more sophisticated assistants that truly has the potential to revolutionize business models.
At this point, it is important to differentiate what we call AI optimization and digital transformation. The first focuses on increasing operational efficiency, reducing costs, and maximizing revenue through scale, without affecting the core of the operation. Now, digital transformation involves a complete change in the company's business model, impacting products and thecore businessof the company. In other words, when we talk about retail, it is necessary to understand that technology, especially AI, has a revolutionary power. Therefore, to make the most of it, it is necessary to go beyond and seek more interactive and personalized tools.
However, technological advancement must go hand in hand with investments in data security and privacy. Protecting sensitive information through biometric authentication, encryption, and automated fraud detection systems will be essential to maintain consumer trust and data, as well as to safeguard brand reputation.
The fact is that companies that effectively integrate continuous research, Big Data, and the most current technological resources will be better positioned to meet consumers' high expectations. In a constantly changing market, digitalization is the most suitable way to turn challenges into opportunities for businesses.
