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How does Social Media impact the retail market today?

Feb 1, 2024
3 min read

Updated: Sep 2

Unexpected viral moments directly affect e-commerce delivery dynamics



The sudden buzz products can gain on social media has exposed brands to an urgent need to review processes involving inventory volume at distribution centers and stores, as well as increasingly fast and unprecedented delivery logistics. In this era of on-demand consumption, where the desire to buy needs to be met within the first 24 hours, companies find themselves having to develop new, increasingly bold supply-versus-demand planning strategies.


In this era of viral moments, anything posted on social media can take on enormous proportions. It is important to understand that there is no barrier separating online from offline. Mistakes made in the real world will have an impact online. And if brands are not prepared to handle the results of a viral post, it can generate negative outcomes both institutionally and financially.


Once an influencer has the power to make a product go viral, regardless of the company's commercial strategy, it falls to the logistics department to redefine its entire product chain, tracking the evolution of demand flow in real time.


According to GP Bullhound Global Insights, the digital market is expected to reach US$3.8 trillion by 2030. Brazil currently has 150 million social media users, and 43% have already made online purchases due to the influence of social networks and their influencers. Given this scenario, Camila Affonso, partner at Massimo Consulting and a retail segment specialist, points to important strategies companies need to adopt to avoid the risk of delivery delays and customer dissatisfaction.



What can be done to prevent the negative effects of going viral?

To anticipate demand, ensure good inventory management, and maintain delivery efficiency, brands need to adopt certain measures to avoid being caught in a crisis triggered by the virtual environment. Applying the concept of Logistics 4.0 can help prevent negative outcomes generated by social media, using technology more productively in the following ways:

 

Monitor what's happening on social media and anticipate demand:

Brands need greater agility in the face of large-scale viral moments. In this sense, demand anticipation should aim to meet short-, medium-, and long-term goals, involving Artificial Intelligence and Machine Learning to extract the data needed to address this need. Monitoring must be based on real-time social media data, using mention and engagement KPIs across all media synchronously.

 

Efficiency in product organization and fulfillment:

Companies need to implement more automated processes within the distribution center to increase the speed of demand fulfillment; among the possibilities is the adoption of picking robots and machines capable of handling a wide range of products and shipment types with minimal error. This makes product pulls from distribution centers to stores more accurate and agile. Another trend is RFID (Radio Frequency Identification) for integrating in-store and online inventory in omnichannel retail.

 

Safety stock:

Correctly sizing safety stock is essential for a brand to be able to meet an unexpected demand spike — an influencer's viral post, for example — without excessively driving up costs. To do this, precise market analyses are required, also taking into account the regions served by each distribution center, which represents the brand's largest stock holding.

 


Today, companies cannot monitor just a single target audience; they need to pay attention to multiple groups and niches, as well as all social media platforms. The universe of digital influencers has been moving the retail sector in a remarkable way, and the engagement power they generate is often surprisingly greater than that of many major brands' marketing campaigns. For this reason, it is essential to learn from past crises — documenting the entire process is indispensable — in addition to using new technologies such as sales integration, automation, and mathematical modeling based on Machine Learning, which can be the solution to the crisis.

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