The technological advances will be essential for the average retail. The use of marketing automation or AI can make a difference. In the case of generative AI, it will be a great help to detect patterns and optimize the use of data, and take a prominent role, since it is becoming a key tool when predicting consumer behavior and optimizing advertising campaigns.
In recent years, Retail Media Networks (RMNS) have emerged as one of the most innovative trends in the world of digital advertising. These platforms allow retailers to monetize their digital infrastructure through the supply of advertising spaces to brands within their ecosystems. The irruption of artificial intelligence (AI) is transforming this ecosystem, expanding its abilities, optimizing its results, and redefining the way brands interact with consumers. This will help retailers to predict trends in advance and customize product recommendations. In addition, the importance of optimizing the use of data cannot be overstated. Brands should take care of this data to obtain quality information that improves the consumer experience and to take advantage of the AI potential.
Given this scenario, it could be said that predictive AI will become the norm for average retail segmentation; and it will also continue playing a more important role in forecasting and planning. For brands that seek to assign budget to retail average inside and outside the site, connected television (CTV), social networks, and other platforms, AI tools can prove where the budget is better invested to obtain optimal results.
The AI, key engine in retail media networks
AI is revolutionizing the way in which RMIs work by improving various key aspects. Among them, the following stand out:
- Customization of consumer experience: Thanks to Machine Learning, MRIs can analyze massive data on consumer behavior to offer personalized ads and recommendations. For example, if a customer looks for products related to fitness, AI can show relevant sports equipment or nutritional supplements, increasing the probability of conversion.
- Optimization of advertising budgets: IA algorithms can analyze data in real-time to adjust bids strategies depending on the performance of the campaigns. This allows brands to maximize investment return (ROI) by prioritizing advertising spaces with the most likely to generate sales.
- Predictive analysis: AI also plays a crucial role in the prediction of consumer trends. Through the use of prediction algorithms, MRIs can anticipate purchase behaviors and adjust advertising campaigns before trends are evident.
BENEFITS OF THE AI IN THE RMNS
The use of AI within the media networks also provides significant benefits for retailers and brands. Among them, it should be noted:
- Greater segmentation: AI allows a much more precise segmentation of audiences. Brands can go to consumers depending on factors such as historic purchases, past interactions with the brand, and even predictions of future behavior.
- Measurement of real-time results: With AI, RMNS can analyze real-time campaigns performance. This allows brands to adjust their strategies on the march to optimize results, something that was more difficult in traditional advertising models.
- Reduction of operational costs: Automated processes promoted by AI, such as advertising inventory management and the allocation of resources, help reduce operational costs for retailers and brands.
What are the challenges when integrating AI in the RMNS?
On the contrary, although the benefits are evident, there are also challenges and challenges when integrating AI into retail media networks:
- Privacy and regulations: The use of data raises concerns about consumer privacy. In this sense, retailers must comply with current regulations to ensure that data is used ethically and legally.
- Technological Barriers: Implementing AI in retail media networks requires advanced technological infrastructure and trained equipment, which can be an obstacle to smaller-scale retailers.
- Data dependence: The effectiveness of AI algorithms depends largely on the quality and quantity of available data. If the data is incomplete or biased, the results will also be.
Looking ahead, as technology continues to progress, the role of AI in RMNS will continue to expand. Among the trends that will be observed in the face of the future, it is worth mentioning: the automation driven by AI could be extended to the creation of custom advertising content, optimizing not only where and when the ads appear, but also what messages are transmitted. Similarly, the omnichannel integration will allow for a more fluid integration between online and offline shopping experiences, with the objective that advertising messages are consistent in all points of customer contact. Another trend will be a greater emphasis on sustainability, where AI could help retail networks to optimize logistics and inventories, reducing waste and supporting sustainability initiatives, something that consumers value more and more.
In short, the use of artificial intelligence in average retail will cause a better advertising experience in all areas. On the one hand, it will help professionals in the sector to optimize their work and make more effective actions, and on the other, the user experience will be much more positive. The average retail and AI are two of the terms that are most giving to talk in the advertising industry in recent months. On the one hand, investment in retail average is increasingly important, and on the other hand, artificial intelligence, especially its generative variant, is becoming an essential technology in virtually any field of advertising.