Using Big Data to Minimize the Risks of User-Generated Content
Use big data to pay attention to your user-generated content, monitor your reviews and stave off the reputation management.
User-generated content has been a boon for marketers for the past couple of years. Research has found that consumers are far more responsive to user-generated content than content created by brands. Why? Third-party credibility. Your customers and clients can sell you better then you can ever do yourself.
One marketing study funded by Gap discovered that 85% of customers are more influenced by visual user generated content than photos or videos from brands. A poll of millennials found that 84% of respondents made purchases that were influenced by user-generated content. User-generated content is also great for driving long-term followers on social media. One survey found that 52% of customers return to a website after engaging with user-generated content. This is around two and a half times the rate that people return to websites that don’t have-user generated content.
Despite the many benefits of user generated content, there are also some risks. The good news is that big data helps mitigate these risks while also leveraging its benefits. Remember: You must pay attention to user-generated content regarding your business – and with website reviews, Google rankings and SEO traffic numbers to keep in mind, it’s much more work than humans can handle.
Here are some of the biggest risks of user-generated content that big data can help with.
Liability risks for negligent followers
There are some liability risks when customers share content on your website or social network. These risks are small, but they do exist.
In theory, brands are isolated from the legal risks of customer behavior on their site. The Communications Decency Act states that brands are not responsible for the content anybody else shares on their site. However, the protections under this law are rather vague. If brands are aware of the content users post on their site, they might be held liable. It is easier to prove this standard if they have previously censored offensive content.
This suggests that brands need to take either an all or nothing strategy. They should either take a heavy hand towards censoring offensive content or refrain from moderation altogether. The latter approach can be reckless if the brand attracts a lot of spammers or people that post hateful or defamatory content. And harassment and hate speech can become a big problem for a brand. Oftentimes, companies can be subject to discrimination if a comment that is deemed hateful or discriminatory is taken down and another is not.
Fortunately, big data can help brands censor content more effectively. They can use sophisticated data mining tools to identify damaging content on their platforms and promptly remove it.
Verifying accuracy of user-generated content before using it in ads
Brands are not usually liable for user-generated content if they don’t share or endorse it. Their liability is much higher after they start actively promoting it.
This is an issue that Quiznos ran into when they solicited videos from customers comparing them to Subway. Some of the videos customers shared had in accurate information, which Subway deemed slanderous. Since Quiznos used the videos in its own ad campaign, Subway argued that their competitor was culpable for defamation.
Big data makes it much easier for brands to vet claims made in user-generated content. This helps them sanitize the content before using it in their own ads. It helps bolster their own credibility and minimizes the risk of civil defamation lawsuits.
Keeping bitter customers from hijacking campaigns
Brands can’t always control the direction of user-generated content campaigns. This is a lesson that McDonald’s had to face three years ago when they asked customers to share their stories on Twitter with the hashtag #MeetTheFarmers. Customers began sharing unflattering stories about McDonald’s even after they killed the campaign.
Big data can help brands avoid these fiascoes by teaching public sentiment beforehand. If the public perception is negative, they can focus on building more rapport before giving customers influence over a user-generated content strategy.
User-generated content might seem to be the way to go to build a great following. But understanding the risks involved as pointed out in this article is essential in keeping up a strong brand and reputation. Use big data to assist you in this ongoing endeavor.
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