Advantages and Disadvantages of Filtering Personalization Content

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You probably have a local business that you love. Perhaps it’s a barber or stylist who greets you by name or maybe you frequent a coffee shop where you don’t have to tell them that you’re having a “soya latte one shot only”. Maybe it’s a real estate agent with a great newsletter about the going-on in your neighbourhood.

What those all have in common? They treat you like the individual you are. (Yates, 2013)

 Treating the individual as they are is the reason why Content Personalisation is becoming the future for personalised advertising (Henkin, 2012). Having individual insight is a priority for marketers as it helps users cope with abundance of available information (Lavie et al, 2010). Users also state that they want personalised services. ChoiceStream (2008), showed that 76% of consumers would like to receive personalised content. Consumers were particularly interested in personalised recommendations concerning music, books and films. From a psychology and communication perspective, research has shown that people prefer objects or experiences that are closely related to themselves compared to objects or experiences that are not related to them (Petty et al, 2000).

 A frustrated consumer is never good for business. Three out of four consumers become frustrated when website content isn’t relevant to their interests. In fact, if your content doesn’t resonate with your target audience, you might as well ask them to ignore you (Soojian, 2015).

 According to Mummert (2014), marketers see an average sales increase of 20% when employing content personalization. In addition, a survey conducted by Econsultancy in association with Monerate shows that 94% marketers and 90% of agencies agree that personalization of the we experience is critical to current and future success. A good example showing that facts don’t lie is of Amazon.

You must be familiar with’s recommendations feature “Customers who bought this item also bought”. See example below.

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 Amazon says that recommendations are hotter than many people realise as 35% of product sales result from recommendations (Marshall, 2006).

 Another great example regarding news is the “Toggle” button on The Guardian website. From time to time events happen that are important to some but irrelevant to others. The “Toggle” button lets readers switch off Royal Baby news. Also The Guardian has buttons that toggle new stories based on where you live and political views. It even has a button marked “ Republican” that will show you news stories that are relevant to Republicans.

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 Netflix has had, for a while now, a feature that allowed users to give star-ratings to content they did or didn’t like. This allowed Netflix to recommend content that the user was most likely to be interested in. The Netflix system had one fatal flaw, however: if more than one person had access to the account and each had different tastes in movies, the algorithm would be less likely to select things that either liked(Henkin, 2012).

This was fixed recently when Netflix rolled out the ability to add profiles to your Netflix account. Before, only one person was allowed to stream at a time, now multiple users can stream, and what’s more, Netflix can deliver content that’s relevant to individual users. That means that if you hate horror movies, you won’t see them in your suggestions queue, but your teenage son still will. And if you like quirky comedies, Netflix is likely to recommend some you’ve never even heard of, but will love (Henkin, 2012).

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Tumblr network is another great example of marketing personalization as it tracks the tags peope use most often in their posts. When they go to check out their newsfeed or to search a tag, Tumblr can recommend new tags that might be interest heir user base. Tumblr also bases its user experience on the following of other users (Murmmert, 2014). This gives the website a great deal of personalization, because it means each user only sees posts from people they like. Want to see cute animals? Follow a blog about cute animals and tumblr is likely to recommend even more blogs about cute animals, and it can result you turning your newsfeed into wall-to-wall puppy pictures if you wanted to (Murmmert, 2014).

 Personalisation isn’t just for websites, it has made an impact on apps too. The Essential Wine App from Delectable Wines. It allows you to take a picture of your favourite wine and it will remember your choices and recommended related wines based on your tastes. The app will also let you know which wines your friends are drinking, and get their perspective on the wines they love(Soojian, 2015).

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 Examples of filters include:

 -FaceBook “Top Stories” News Feed Raking

-Twitter “Top Tweets” in Search

-Twitter “No Replies” setting for brands

-Google’s personalized Search Results

-Itunes “Top [Apps, Books, Songs, Movies etc]

-Netflix “Popular Queue and other personalized queues

 The examples above clearly show the benefits to filtering personalization content for both business and for web publishers as relevant content and ads means more clicks and ultimately more money (Soojian, 2015). For users, relevance means less time spent finding content they will enjoy.

Who really has time for to sift through the 161 million results Google sends back for a search about Miley Cyrus anyway? If the search engine knows you are probably looking for her latest music video or her tour schedule, what’s the harm in showing you those results on the first page?

 According to Eli Pariser board president of , there are dangerous, unintended consequences to filtering. In his book Filter Bubble, Pariser argues that all this filtering and personalization is starting to isolate us. When websites show us only what we like, we get cut off from diverse points of view that can enrich our understand of the world (Catoni, 2011). That might be relatively harmless when you’re searching for Ms. Cyrus’ latest single, but what about when you’re trying to find information about pending legislation in Congress or news about revolution in another country? (Catoni,2011)

 He goes on stating that the trend toward personalization impedes the fulfilment of Internet promise. The promise of Internet is that it can connect people from different backgrounds, with different beliefs and across disparate locations. But with personalization there is a problem because it means you’re less likely than ever to be confronted with information that challenges your views, or get you out of your comfort zone (Catone, 2011). A lot of the personalization that exists today just serves up information “junk food”. It might be delicious, but it doesn’t feed the soul. Pariser argued that his biggest fear is that important but un-sexy problems –from homelessness to the war in Afghanistan fall our of view entirely (Pariser, 2011).

 Watch this vide of Pariser where he explains the problem of filter bubble in vivid detail.

 While most newspaper readers read the internal sections (sports, Home and Garden, etc), at least they had to flip by the front page which let them know if something important was going on that they should know about. However, as seen previously on the example of The Guardian Toggle button people can still select it and if they are fans of the Royal Family they can just follow these type of news and ignore important news. Now is possible to live in a bubble where that stuff doesn’t ever show up and you would never know it’s happening (Catoni, 2011).

 Most personalization on the web is algorithmically driven and we implicitly inform the algorithms based on the choices we have previously made interacting with content and it is now really hard to trick the algorithm(Pariser, 2011). For instance, if you are completely logged out of Google, on a new computer, the company can track 57 signals about you:

  • what kind of laptop you are using.
  • what your IP address is.
  • What font size in your browser is

Already, that gives a lot of important clues about age, income and demographics.

It’s ironic, the promise of personalization is that it gives us our own personal view of the world. But the challenge is that a lot of the time, it’s actually pushing us toward a stereotyped, simplified version of ourselves. “ This person is male, so we will show him more gadget and car news” (Pariser, 2011)

 Pariser suggests that that the solution is to include irrelevant signals to equally rank content that is uncomfortable, challenging and timely into or online experience. Companies that are doing the filtering have a huge responsibility. They must educate their users about how the filtering works, to give them some control, and to build algorithms that have a sense of civic purpose embedded in them.

 How would it be possible to balance the advantages and disadvantages of filter personalization?

 The common thread is that they both have a lot to do with personal data. Personalization couldn’t exist without the massive dossiers of personal data being collected by big companies online these days. And it’s a problem because consumers don’t have much control over that. The current laws around personal data just don’t contemplate a world in which a click on one website changes what you see on an entirely different one (or indeed, websites at all.) (Catoni, 2011)

 Final thoughts:

 There are many benefits that can be taken from personalization content especially to marketers that want to increase sales profit out of recommendations. However, this can have a significant impact on people that are searching for relevant/important information. Information that feeds the soul and not junkie food information as put by Pariser. Today companies responsible for the filtering dictate how we discover and act on information.

It can be argued that personalization content is still at its infancy and perhaps it will develop in the future allowing us to have control of access of information by developing algorithms that have encoded in them a sense of public life, a sense of civic responsibility as stated by Pariser.


 Catone, J. (2011) ‘ Why Web Personalization May Be Damaging Our World View? Available at:

 Lavie et al. (2010) ‘User attitudes towards news content Personalization. International Journal of Human Computer Studies. Vol 68 issue 8 pages 483-495: Available at:

 Henkin, A. ‘How Content Marketing is the Future for Personalised Advertising’ Wall Blog (2012) Available at:

 Marshall, M (2006). ‘Aggregate Knowledge raises $5M from Kleiner, on a roll’. Available at:

 Mummert, H. ‘The Tipping Point for Personalised Website Experiences. (2014) Available at:

 Petty et al (2000) ‘Attitude Functions and Persuasion; an elaboration likelihood approach to matched mismatched messages. GR Maio (Eds)

Soojian, C (2015) ‘Content Personalisation It’s What Consumers Want!’. SocialMedia Today. Available at:

Yates, S (2013). ‘6 Kick-Ass Examples of Marketing Personalization’. Available at:







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