Social Media Influencers: The Newest Players in Counterfeit Risk

While e-commerce was already flourishing before the pandemic, in the face of  COVID-19 lockdowns and social distancing measures,  e-commerce retail sales accelerated globally from 13% in 2019 to 21% in 2022. While brands pivoted their focus towards omnichannel sales, their risk profile also pivoted, with there being a boom in innovative counterfeit activity.

One such entrant in this ever-growing counterfeit risk landscape is the social media influencer. Social media influencers are often known to lead their followers directly into the hands of counterfeit gangs. And detecting, monitoring and curbing this has become harder than ever. 

The growth of counterfeit products and the influence of social media 

In 2019, the Organisation for Economic Co-operation and Development (OECD), EU’s Intellectual Property Office, calculated that 3.3% of global trade comprised counterfeit goods.  In 2022, this equates to more than $250bn. As the popularity of e-commerce continues to rise, more so since the lockdown, the percentage of counterfeit sales through e-commerce has increased significantly. In fact, the boom in e-commerce is not the only driver of online counterfeit trade. Social media is also becoming the tool of choice for counterfeit advertising. It is  effective, ever changing, and with the advent of new platforms, reaching larger and larger audiences.  

There has been a marked increase in collaboration between social media influencers (SMIs) and counterfeit organized crime groups. A study, released in December 2022 by the Intellectual Property Office (UK) shows that up to 10% of counterfeit sales were prompted by influencer posts. This number continues to grow as influencers are constantly changing tactics to evade platform guidelines and brand risk monitors.

Apparel and electronic goods remain the most affected. This is no surprise; with an increase in counterfeit activity partly driven by SMIs, brands and products that are synonymous with younger generations are likely to be at a higher risk than before. Consumers from younger demographics are also more likely to engage with social media. According to a 2021 Harris Poll, 35% of social media users say they make purchases based on what they see advertised on social media. This is almost certain to increase in the coming years.

Understanding social media risk 

In the last 2.5 years, there has been a marked increase in the promotion of counterfeit goods on social media (#dupes, #fake, #inspired). Using our propriatory hashtag glossary, Control Risks has been monitoring social media platforms for instances of SMIs promoting counterfeit goods. The peaks have corresponded to pandemic waves when consumers took to shopping online.

Social Media Influencers: The Newest Players in Counterfeit Risk

Defeating current detection technology 

The collaboration of SMIs and counterfeit criminals is problematic in two ways: 1) it preys on the trust between the influencer and their followers and 2) it circumvents traditional counterfeit detection technologies. Trusting followers are openly told to follow links posted by SMIs or even those stated in videos, to purchase counterfeit goods. This renders traditional detection by e-commerce sites useless, as the item advertised is often generic and therefore not detected as potentially counterfeit. Here is how the scheme works:

Social Media Influencers: The Newest Players in Counterfeit Risk

Taking an outside-in approach

In recent years, online counterfeit detection has taken an inside-out approach, where e-commerce sites detect the presence of counterfeit products on their sites using internal point-of-sale data. This has proven to be an effective approach until now. 

For businesses to continue to protect themselves from this emerging risk, brands and e-commerce sites alike need to reverse their approach to counterfeit detection and take an outside-in approach. This involves identifying social media influencers and others who are actively promoting counterfeit products and mapping them back to the point of sale. While this may seem like a mammoth task, leveraging advanced analytics and machine learning can considerably reduce the effort required, remove false positives, and help brand managers understand who is targeting them and how they are operating.