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It can be time-consuming for brands to fight counterfeits in a guerilla war that spans e-commerce, social media, meta-marketplaces, and peer-to-peer networks. The web offers speed and anonymity to people who take advantage of others’ success. But what if brands could use an army of robot-brains to assist them?
The thought isn’t as sci-fi as it sounds—a few new technology trends promise to give brands an edge in the fight against counterfeits. According to experts surveyed for Red Points’ Brand Intelligence Index, many expect to find relief through blockchain and the following AI trends in 2020.
Check out other IP trends from the Brand Intelligence Index:
Blockchain represents a categorical shift in the way that data can be kept secure, and 46% of survey respondents said they expect the technology to impact brand protection this year.
Blockchain is already famous for enabling cryptocurrency, but it goes much further than that. In a nutshell, blockchain is a decentralized and anonymized ledger: multiple computer systems owned by different people track transactions. Each new transaction is recorded with algorithmic keys that connect the systems, or blocks, in the chain to one another. Each block has a unique record of the transaction. It is impossible to return later and tamper with the recorded data in one block since it wouldn’t match the others.
Blockchain also removes the need for human verification, and it’s often described as a trustless system. By using blockchain, a brand could create new distribution partnerships very quickly. The brand and the distributor would only need to trust the shared record on the blockchain between them, rather than trust the other business itself. Each product leaving the manufacturer could be tracked on the blockchain in real-time, and it would be easy to see if a shipment veered off course. A brand would know if an authorized dealer sold some stock to a gray market dealer, for example.
While the technology is still new, one could imagine a scenario where a company records its whole authorized distribution network on the blockchain. Codes or sensors at the point of purchase could alert the customer if they are holding a gray market item. Of course, making customers aware of this system (and the drawbacks of the gray market) would take time and resources.
Down the road, e-commerce marketplaces could also use blockchain to verify the authenticity of third-party sellers. At the moment, platforms require brands to seek out and report counterfeiters, and then platforms remove them manually. In the future, a brand could connect its distribution blockchain directly to an e-commerce platform. The platform could then automatically check the origin of a new seller’s items against the brand’s official record and flag suspicious accounts. This could all be done securely and without any man-hours required from the platform.
Finally, blockchain networks could also simplify the process of obtaining and registering IP, both for rights holders and for governmental agencies. This technology trend is especially suited to safeguard important information from tampering and corruption.
The internet is littered with ways for people to make a few extra bucks while sitting at home on the couch. Taking surveys, shopping for rebates, and playing online poker promise to help penny pinchers pinch a few more pennies. Unfortunately, writing positive reviews for money is another one of these couch-hustles.
It takes just a few seconds to find Facebook groups where people can write positive reviews for money. Sometimes, group rules forbid this—on the surface. But as soon as you contact a seller, you’ll be asked to leave a favorable review without mentioning that you got an item for free or were paid a few dollars.
Sellers may say that this is a necessary evil to compete on Amazon, but these sellers are usually trying to promote fakes. Now, an official brand wouldn’t pay for reviews, but one of its third-party sellers might. Since incentivized reviews are strictly forbidden on Amazon, it can reflect poorly on a brand if their sellers use this tactic.
It’s possible to sift out some incentivized reviews by looking at the way that they are written, but it would take many hours to do this manually. That’s where NLP, or neuro-linguistic processing, comes in. Fourteen percent of the experts surveyed said they expect NLP to be an influential technology trend. NLP programs can read through huge amounts of reviews and categorize them on things like sentiment, duplication, and the likelihood of a review being fake.
NLP can also help brands find out what their customers are saying and respond to them efficiently. Sorting out the negative reviews, and learning what the problems are, can give customer service reps a jump start on damage control.
Brands that sell more affordable items can also be at risk for review sabotage. Some counterfeiters will pay bad actors to buy a brand’s items and then leave negative reviews. Sometimes, a slew of negative reviews could be posted on the same day, which is further evidence that they are fake. NLP and monitoring programs can alert the business if something like this happens.
When you look at a picture of a Louis Vuitton bag, you don’t need to read the caption to recognize the brand. However, until recently, computer systems had to rely on written words to understand what was in a photo. That’s why some clever counterfeit sellers on social media channels moved away from using a brand’s text to using something generic. In this example, a shopper looking for an LV bag might find fakes under the keywords “branded bag.” The platform’s algorithm would recognize that the item was some type of bag from the text, but it wouldn’t know it had anything to do with that brand.
However, things are different today. The first tool to come around was a reverse image search. Using this type of search, brands can see if their images are being used by unauthorized channels. The search algorithm will return results that are on the web and that are similar to the input photo. Since the search doesn’t rely on keywords, it can return pages that are written in other languages, as well.
While a reverse image search is useful, it’s still limited to returning pictures that are fairly similar to the input photo. But one technology trend takes things to the next level: image recognition software. In our survey, 37% of respondents said they expect image recognition software to impact brand protection.
With this tool, a brand can teach a neural network to look for a specific image, i.e. a logo, inside other images. The more data and images you give to the software, the smarter it gets. After sufficient training, a neural network could retrieve any public page that has the LV logo. At that point, the brand could filter for posts with contact information or prices. The brand could also monitor how customers and fans share their images in this way.
Finally, image recognition software can also scan web pages for the presence of a brand’s logo whether it has been directly copied or altered in some way. Doing so can uncover web-based sellers that aren’t on e-commerce or social media platforms.
From securing the supply chain to scanning the market with robotic eyes, hi-tech advancements can offer brands hope in the coming months. When combined in a brand protection strategy, these technology trends can tip the scales in favor of IP rights holders.