Maurizia

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I first heard of the term filter bubble a few years ago during one of my lectures during my bachelor. I found it an interesting topic as filter bubbles are everywhere, and I believe even more widespread during the pandemic. For those of you who don’t exactly know yet what a filter bubble is: you find yourself in a filter bubble, every time that you are surrounded by news and opinions that are in line with your opinion (Farnam Street, n.d.). This probably does ring a bell now, as for our generation we can easily link it to the algorithms that our favorite social media apps use. One well-known example of filter bubbles is when Trump (suddenly) won the presidential elections in 2016. A lot of people didn’t see it coming that Trump actually had won the elections as they were almost sure that Hillary Clinton would win (Baer, 2016). This happened because many people actually ‘lived’ in a filter bubble. This happened because the algorithms that are used by social media platforms like Facebook, generate a personalized timeline that is more adjusted to your preferences every time you open or like an article. The algorithm personalizes the content, which results in only content that is in line with your opinion showing up on your timeline at some point. The fact that it is so easy to surround yourself with other people who share the same opinions also reinforces the creation of filter bubbles. A lot of people tend to believe they are well-educated on certain topics because they read content all day. But the problem is that this content is so tailored to their beliefs, that it often only tells one side of the story (Baer, 2016).

Another example of when filter bubbles were a hot topic was during the peak of the pandemic when polarization occurred in society due to the different beliefs of people. Two groups were created, and more people started believing that vaccines were only causing harm and that the pandemic was a hoax. In the United States, 42% of Americans have seen a lot or some news about the coronavirus outbreak that seemed completely made up (Mitchell & Oliphant, 2020). This number is alarming, as this can be caused by people living in filter bubbles. People who questions the pandemic started clicking on some articles that agreed with their doubts, causing the algorithm to show them more and more similar articles that are in line with their opinions. This causes people to believe that what they think is true because that’s the only news they see at some point. However, the problem is that they only see a very small fraction of the actual news on the pandemic and thus barely have an idea that there are other facts that can be true.

Personally, I think this is a serious problem and one of the downsides of social media. People can start believing in their own reality and not listen to others anymore, because all they see is news that is in line with their believes. What do you guys think of it? And do you believe that there is a clear solution for the problem of filter bubbles?

References

Baer, D. (2016, November 9). The ‘Filter bubble’ explains why Trump won and you didn’t see it coming. The Cut. Available at: https://www.thecut.com/2016/11/how-facebook-and-the-filter-bubble-pushed-trump-to-victory.html (Accessed: October 13 2022)

Farnam Street (2019, November 14). How filter bubbles distort reality: Everything you need to know. Farnam Street. Available at: https://fs.blog/filter-bubbles/ (Accessed: October 13 2022)

Mitchell, A. & Oliphan, J. (2020, March 18). Americans Immersed in COVID-19 News; Most
Think Media Are Doing Fairly Well Covering It. Pew Research Center [Blog Post]. Available at:
https://www.journalism.org/2020/03/18/americans-immersed-in-covid-19-news-mostthink-media-are-doing-fairly-well-covering-it/ (Accessed: October 13 2022)

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Imagine you’re walking in the city center past one of your favorite stores, and right then a notification pops up on your phone saying that there is a discount on the jeans you searched for online last week. This is called proximity marketing (Gartner, n.d.), better known as location-based advertising. It uses user data on previous purchases, preferences, and search queries to offer the customer a personalized discount, at the right time and at the right location. This can be done by making use of different technologies, such as in-store WiFi networks, GPS, QR codes, NFC tags, and BLE beacons (AVSystem, 2022).

With consumers demanding more personalization in their shopping experience (Epsilon, 2018), location-based advertising can be useful to achieve that. It allows retailers to target their advertisements to specific customers, which results in higher profits for the retailer and in a more personalized experience for the customer. Another benefit that arises from its application is the collection of customer data for retailers. As the technology uses the customers’ location, retailers know exactly where a customer is located in the store and what shelves he or she passes and stops at.

Successful implementations have been achieved already. McDonalds in Turkey installed beacons in 15 McDonalds cafes, offering customers who were close by a coupon for a free coffee from their new drink line for free (Mittal, 2022). Starbucks also experimented with location-based advertising, already back in 2014. They send app-users advertisements when they were close to one of their stores, offering them a 50% discount on a drink (Simpson, 2016).

However, there are some concerns with regard to the privacy of customers (Inman and Nikolova, 2017). As their exact location is being tracked, retailers have a lot of information on their customers. Therefore it is important to explicitly send a notification to an app user that their location will be tracked to send them personalized advertisements, so they can opt-in first. Despite the possible privacy concerns, several studies showed that customers have a positive attitude toward location-based advertising. Banerjee and Dholakia (2008) found that personalized advertisements are more welcome in public environments than in private environments. This can be explained that customers benefit more from targeted advertisements in public locations, for example in front of the store they are about to enter to buy the item they wanted for a long time already. An advertisement at that moment is logically more welcome than the same offer when that person is making a phone call to his boss. Another study by Gazely et al. (2015) showed that location-based advertisements have a positive effect on purchase intent, adding that it is important that the advertisements are not perceived as intrusive. This shows the potential of location-based marketing. Retailers can better target customers, resulting in a higher conversion rate and higher revenues. Customers are provided with offers at the right time, and at the right location. This will benefit them more, as the offers are personalized and also based on their previous purchases and preferences, and as they are actually able to use the offers right away.

I personally strongly believe in the potential of this type of marketing. Both retailers and customers can greatly benefit from it, in terms of data and personalization. I am definitely willing to share my location to receive hyper-personalized offers on products that I am actually interested in, and I am very curious about what you think of it! Let me know below in the comments 🙂

Note: there are many more applications of location-based advertising than the one I discussed in this post. I hoped this sparked your interest at least!

References

AVSystem (February 3, 2022). 5 proximity marketing technologies you need to know. AVSystem – Shaping The World of Connected Devices. Available at: https://www.avsystem.com/blog/proximity-marketing/ (Accessed: 6 October 2022)

Banerjee, S., & Dholakia, R. R. (2008). Mobile advertising: Does location based advertising work? International Journal of Mobile Marketing.

Epsilon. (2018, January 9). New Epsilon research indicates 80% of consumers are more likely to make
a purchase when brands offer personalized experiences. Retrieved from www.epsilon.com:
https://www.epsilon.com/us/about-us/pressroom/new-epsilon-research-indicates-80-ofconsumers-are-more-likely-to-make-a-purchase-when-brands-offer-personalizedexperiences

Gartner (n.d.). Definition of proximity marketing – Gartner marketing glossary. Gartner. Available at: https://www.gartner.com/en/marketing/glossary/proximity-marketing (Accessed: 6 October 2022)

Gazley, A., Hunt, A., & McLaren, L. (2015). The effects of location-based services on consumer purchase intention at point of purchase. European Journal of Marketing, 1686-1708.

Inman, J., & Nikolova, H. (2017). Shopper-Facing Retail Technology: A Retailer Adoption Decision Framework Incorporating Shopper Attitudes and Privacy Concerns. Journal of Retailing, 7-28.

Mittal, S. (May 10, 2022). Proximity marketing examples: 28 retail companies nailing it with their campaigns. Available at: https://blog.beaconstac.com/2016/02/25-retailers-nailing-it-with-their-proximity-marketing-campaigns/ (Accessed: 6 October 2022).

Simpson, J. (January 18, 2016). What is location-based advertising & why is it the next big thing?. Econsultancy. Available at: https://econsultancy.com/what-is-location-based-advertising-why-is-it-the-next-big-thing/ (Accessed: 6 October 2022).

Just for you: location-based advertising in retail

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