Digital transformation has become one of the main drivers of retail today, requiring companies and brands to invest in solutions aimed at effective operations in the virtual environment. Digitization, in addition to strengthening and expanding the visibility of products and services, creates opportunities for innovation in the shopping experience, contributing to a projected global economy of over $100 trillion by 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 consumer behavior patterns and preferences. By cross-referencing and analyzing massive amounts of data, it has become possible to personalize offers and target campaigns individually, providing a more relevant and attractive shopping experience. It is worth noting that a key differentiator between business intelligence data usage and big data, beyond the volume of data, is the ability to make decisions based on present-day data and not just past data, given the high processing power of the technologies used in Big Data.
One of the most notable examples of the use of this resource is Amazon, which applies algorithms to suggest products based on previous purchases and each user’s profile—sometimes even making recommendations based on items already in their cart. Unsurprisingly, 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 confirmed, this scenario would represent an average annual growth of 21.2%.
Operational efficiency is also strongly enhanced by intelligent data management. Tools that optimize inventory control, demand forecasting, and logistics are essential for anticipating consumption trends and maintaining ideal operation levels, avoiding excesses or shortages of supplies. Moreover, it is necessary to highlight the integration of various sales channels—or in other words, the much-discussed omnichannel approach—which allows consumers to transition seamlessly from an online store to a physical or mobile one. This enables a fluid shopping journey and facilitates the completion or even repetition of transactions.
Some of the world’s largest retailers have a predictive logistics algorithm that cross-references user location data, page traffic volume for certain products, cart data, and estimated conversion to expedite the fulfillment process (i.e., a set of logistical operations involving a customer’s order until product delivery). This allows products to be pre-selected in the warehouse before they are even purchased.
But beyond operational impacts, how can we also increase customer loyalty through data? First, by targeting customers who have a tendency to be more loyal. It is possible to analyze a company’s historical order data to identify which items attracted customers with the highest recurrence of purchases and implement a price elasticity strategy for these items, determining the ideal pricingversusthe existing competition to increase conversion among these loyal consumers.
A second point is understanding 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 study’s results. The most recommended method for this research isOctalysis,with questions such as: What are my customer’s purposes? What fulfills my customer? What empowers my customer? What generates a sense of ownership? What influences my customer? What sparks curiosity? What benefits and advantages would my customer never want to lose? By collecting this data and building a retention strategy, loyalty results will certainly increase.
However, Big Data does not drive this revolution alone or in isolation. Other resources—and here, of course, we must emphasize the leading role of artificial intelligence (AI)—play a part as a fundamental competitive differentiator for brands. The optimization generated by AI can represent cost reduction, improved operational efficiency, and a host 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 from digital transformation. The former focuses on increasing operational efficiency, reducing costs, and maximizing revenue through scale, without affecting the core of the operation. Digital transformation, on the other hand, implies a complete change in a company’s business model, impacting products and thecore businessof the company. In other words, when we talk about retail, it is essential to understand that technology, especially AI, has revolutionary power. Therefore, to leverage it effectively, it is necessary to go further 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 maintaining consumer trust and safeguarding brand reputation.
The fact is, companies that effectively integrate continuous research, Big Data, and the latest technological resources will be better positioned to meet consumers’ high expectations. In a constantly evolving market, digitalization is the most recommended path to turn challenges into business opportunities.
