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FaceApp Challenge, Neural Networks, and Implications for Digital Marketing
August 28, 2019 AroundtheWeb

FaceApp is the latest social media sensation. The app allows its users to transform images into highly realistic depictions of how they would look then they’re older.

The magic behind this transformation primarily comes from neural networks, a complex machine learning algorithm. Even though FaceApp claims that it does not use or sell your data, a closer reading of its terms and conditions reveals that FaceApp relies on location data and app usage statistics to send personalised ads to its users. Depending upon the permissions given by the user, FaceApp can also access the internal storage of smartphones to review stored images; this is done to improve the neural network function. If you’re feeling overwhelmed by the wave of the latest machine learning innovation, don’t worry! This next article focuses on how the data collected through mobile apps could be used to power your digital marketing efforts

Brands Take up the FaceApp Challenge

FaceApp was an instant hit with customers all over the world. The FaceApp boom flooded the social media with images under the tags ‘#AgeChallenge’ and ‘#FaceAppChallenge’. However, the AgeChallenge also extended to the corporate world. Many popular brands have responded to the FaceApp challenge and used FaceApp to increase brand awareness and brand impressions. Using FaceApp, many businesses posted the transformed images of their products on their blogs and social media pages to create a buzz around their brands. Lego, the popular maker of children’s toys, used the FaceApp challenge on Twitter to show how its toys can provide timeless entertainment. The Danish maker of toys posted an image generated through the recently improved ‘old age filter’ of FaceApp with the caption ‘Not even #FaceApp can age us’.

FaceApp Challenge, Neural Networks, and Implications for Digital Marketing

Source: https://unsplash.com/photos/hpjSkU2UYSU

Using Algorithms to Collect and Sort Data

Applications like FaceApp provide businesses with a great platform to collect personal data from their current and prospective customers. The company’s privacy policy says ‘Any information or content that you voluntarily process with the Service, such as User Content, becomes available to the FaceApp anonymously’. A clear implication of this sentence in the privacy policy is that the firm can use the images of the customers in any way that can be beneficial to FaceApp and its business partners. Neural networks require a large amount of data to improve their features. While this was a major benefit, firms would need to include privacy clauses in your policies and user agreements when using machine learning or similar technologies. Even though FaceApp says that it does not directly sell its customer data to other companies, the machine learning algorithms that are trained and improved with this private data could be sold or licensed to other companies. 

Machine Learning and Neural Networks

Machine learning has become the buzzword in digital marketing. While they may seem like magic, the explanation of how they work is pretty simple. Algorithms like neural networks mimic the working of a human brain and recognise patterns in text and images. Just like a person, a neural network starts learning more once it gains access to new knowledge. FaceApp creates stunning images of people using neural networks denoted as Generative Adversarial Networks (GANs). The performance of these algorithms will keep increasing as time goes by and as the feedback loops embedded in the algorithms correct any errors that could be made by the software. Other machine learning algorithms are used by businesses like Amazon and Netflix to give suggestions to clients based on their past purchase patterns. In other words, you could benefit from the evolution of machine learning algorithms; the main result is improving the returns on investment and gathering valuable market data. 

Implications for Digital Marketing

Digital marketers rely on customer data, namely their past purchasing behaviour, age, and income levels. The success of apps like FaceApp and the technologies powering them such as neural networks has significantly increased the number of opportunities for digital marketers. Many customers are willing to share their personal data with companies so that they can get access to higher quality services. In turn, businesses could use this knowledge to create personalised ads and other promotional messages. For example, airlines and retailers could quickly eliminate low-value customers from their mailing lists and loyalty programmes. The intention of consumers to share their personal data online can produce a positive effect on the coverage of British retailers’ loyalty programmes and more frequent purchasing of consumer goods. The success of apps like FaceApp also makes it possible for companies to purchase scarce personal data of customers without relying on the advertising platforms offered by big technology companies. 

The rise of a new generation of social media services poses a number of new risks to people and companies concerning privacy and security. However, when used properly, such apps could be used by digital marketers to increase marketing value. Sure, getting access to new knowledge is great but personal data should also be protected to avoid cyber-risks. Facebook and Yahoo have recently learned this the hard way when the data of their millions of users was accessed by hackers. It is time digital marketers embrace the new data and better quality algorithms that are available to them and reach their customers more effectively.

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