Digital transformation has become one of the main drivers of retail today, demanding that companies and brands 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, allowing the identification of consumer behavior patterns and preferences. From the cross-referencing and massive analysis of data, it became possible to personalize offers and target campaigns in an individualized way, providing a more relevant and engaging shopping experience. It is worth highlighting that an important watershed between the use of business intelligence data and big data, beyond the volume of data, it is the possibility of decision-making 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 the use of this resource is Amazon, that applies algorithms to suggest products based on previous purchases and each user's profile – sometimes, even creating recommendations based on products that are already in your cart. Not for nothing, according to the analyst Mordor Intelligence, the Big Data market in the commercial sector was estimated at US$6,38 billion last year and is projected to reach US$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 strongly benefited by intelligent data management. Tools that optimize inventory control, demand forecasts and logistics are essential to anticipate consumption trends and maintain ideal operating levels, avoiding 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 move from an online store to a physical or mobile one without interruptions. 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 largest retailers in the world have a predictive algorithm for logistics that crosses user location data, access volume on the page of certain products, cart data and estimated conversion to expedite the fulfillment process, a set of logistical operations that involves a customer's order to the delivery of the product. Thus, it is possible to separate the products in the logistics warehouse even before the items are actually purchased
But beyond the impacts on the operation, how to also increase customer loyalty through data? Firstly, capturing clients who are more likely to be loyal. It is possible to analyze the historical order data of a company and understand which items brought customers with the highest purchase frequency and implement a pricing elasticity strategy for these items, understanding what the ideal pricing isversusthe existing competition to increase the conversion of these loyal consumers.
A second point is to understand what motivates the customer through the data, what can be done when conducting research 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 theOctalysis, with questions like: What are my client's purposes? What my client does? What empowers my client? What generates a sense of ownership? What is an influence for my client? What awakens curiosity? What benefits and advantages would my client never want to lose? 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) – assume the role as a fundamental competitive differentiator for brands. The optimization generated by AI can represent cost reduction, the improvement in operational efficiency and a number of other benefits, however it is 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, but without affecting the center of the operation. Now digital transformation implies a total change in the company's business model, impacting products and thecore businessof the company. That is to say, when we talk about retail, it is necessary to understand that technology, especially AI, has a revolutionary power. Therefore, to make the best use 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. The protection of sensitive information through biometric authentication, cryptography and automated fraud detection systems will be essential to maintain consumer trust and data, in addition to safeguarding the reputation of brands
The fact is that, companies that know how to effectively integrate continuous research, Big Data and the latest technological resources will be better positioned to meet the high expectations of consumers. In a constantly changing market, digitalization is the most suitable way to turn challenges into opportunities for businesses
