With the spread of new technologies, customers and their shopping habits are changing. A recent study by Comarch and Kantar TNS shows why the retail sector is ahead in artificial intelligence, how AI projects of international scale are made successful and what expectations customers have when visiting a cosmetics retailer’s store or online shop.
Cosmetics buyers also want to remain loyal to retail stores in the future: 33 percent believe that they will mainly shop there in five years, 25 percent will buy in-store more often than online. On the other hand, 15 percent see themselves more often and 18 percent mainly when buying cosmetics online. According to the study, customer loyalty to cosmetic products is divided roughly in half.
Artificial Intelligence Is Revolutionizing Retail
The digital possibilities not only influence the consumers in their actions. New technical tools are also available to retailers themselves, which they can use to get to know their customers and their needs better. Therefore, according to the consulting firm IDG, particularly customer-oriented sectors such as the banking, retail and process industries are currently among the most active in artificial intelligence. Retail will see the highest annual growth in AI projects of all these industries. Cognitive technologies will revolutionize retail.
Innovative AI use cases span the entire product cycle, from sourcing to merchandise and post-sales customer care. You can enhance the customer experience by providing hyper-personalized product recommendations and offers, online shopping guides, dynamic pricing, real-time information, and customer service. They are also invaluable for optimizing business operations, helping predict customer behavior to increase sales and revenue, and forecasting demand based on buying patterns to optimize inventory levels. The most common AI applications used by retailers in optimization and development processes are machine learning, statistics, personality analysis.
Machine Learning Supports Business Analysis
Machine learning technology supports human decision-making by improving accuracy, confidence, speed and agility. Machine learning extends two traditional areas of business analytics, namely descriptive analytics (what happened) and predictive analytics (what is likely to happen), complemented by a third analysis (why something happened). With the discovery, machine learning brings new possibilities to prescriptive analytics (what should happen) and represents a new level of business analysis.
In the face of digital transformation, Oriflame also felt the need to digitize its processes to meet the growing and changing customer needs. Direct sales are the most important building block in the dealer’s business model, which means that the sales consultants play a key role. Therefore, Oriflame needs to ensure they understand these consultants well, meet their expectations, and communicate individually yet efficiently. A suitable starting point for the digital transformation of Oriflame was the optimization of the internal processes for the analysis of all consultant-related data. It had already created huge data pools from around 3.5 million consultants. Assuming
The automated segmentation platform is crucial for a better understanding of sales and more efficient action planning, including marketing communications. The platform also helps Oriflame track a range of KPIs, including basket sizes, network development, segment sizes and more. In this way, the efficiency of different activity groups can be controlled. In addition, the self-adapting segmentation results serve when introducing new products.
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