Written by: Julija Lukaityte

Personalised advertising is considered to be further enabled by the internet technology as a tool to build more personal relationships between brands and consumers. Personal preferences disclosed by consumers online can help advertisers to create more targeted selling communication which is perceived as more appealing and therefore, enhancing positive attitude and purchase intention towards particular products or brands. In this paper personalisation will adopt the meaning that organisations recognise consumers as individuals through personal communication, targeted banner ads, special promotions and other personal interactions (McMillan, 2004). Taking and studying the phenomenon from consumers’ perspective enables an understanding of what makes an effective personalised advertising online. Moreover, to understand the counter aspects the resistance and avoidance variables will be studied too. As a result, the paper will provide enabling and disabling forces that have to be considered when creating a personal brand experience online.

What makes a personalised ad effective?

Perceived Personalisation

Previous research has indicated that personalisation online has a positive impact to advertising effectiveness (Tam and Ho, 2005; Yu and Cude, 2009). Usually, studies would focus on comparing personalised and non-personalised advertising while not taking into consideration that the difference between actual personalisation and perceived personalisation are important factors determining effectiveness. In this case, actual personalisation means the meaning adopted by researchers and perceived personalisation refers to whether a consumer sees a match between the message and oneself. According to Keyzer et al. (2015), before any behavioural effect can take place it is important that consumers adopt the communication as suited to their needs. The idea of personal relevance as one of the main variables shaping effectiveness of internet advertising can be aligned with the elaboration likelihood model and self-referencing criteria. When a message uses personal information, consumer relates to it which induces self-referencing. Self-referencing can have positive effects on attitudes under central as well as peripheral processing. For example, self-referencing can guide a decision as “it is good for me because it is personally addressed” in a peripheral way or encourage central processing and higher elaboration by at first just grabbing attention. Though, in terms of advertising personalisation and ELM the processing seems to be central for higher involvement products, which is naturally perceived as more relevant. When looking at anti-smoking advertising those with higher personalisation, times of quitting or cultural references seem to increase personal relevance and persuasion (Keyzer et al., 2015). Moreover, personal relevance seems to be particularly important when advertising on social networks as one of the most important reasons for avoiding ads is lack of perceived relevance. If the prior studies are correct, personalising ads online and strengthening the perceived relevance will have an impact on positive consumer responses (Kelly et al. 2010; Keyzer et al., 2015.)

Timing is everything

What is more, timing and placement factors are considered to be important variables shaping the effectiveness of personalised advertising online. When considering timing the stages of purchase decision process should be taken into account. According to Bleier and Eisenbeiss (2015), the effectiveness of personalised ads changes over time, they are the most effective when consumer does not have any strong preferences and browse online for information. Then, the effectiveness of personalised ads decreases as consumer progresses through different stages and formulates stable preferences. At first, when entering a particular online store customer has a broad idea about the brand and category specifications. By getting familiar with the assortment they start to create opinions which can still be easily altered. These sort of customers are easily convinced that personalised recommendations and offers really suit their needs. The second group of customers are more resistant as they have really compared different possibilities and maybe even created wishlists including specific items. They are more stable and therefore have more defined preferences. Final group has already made the purchase and therefore does not pay attention to organisation’s recommendation (Bleier and Eisenbeiss, 2015; Simonson, 2005). With time unstable preferences are more likely to change than stable. It is interesting that for consumers with unstable preferences ‘overpersonalisation’ may occur, this means that ads featuring the exact same products or categories from the browsing history will be valued as irrelevant due to the changed preferences. Therefore, marketers should focus on more settled personalised messages and less intense advertising at early stages of consumer journey. However, when targeting the consumer at the post purchase stage high intensity personalisation seems to work best. This means that featuring the product, which is very similar to the purchased one, will evoke curiosity to see the ad and learn more (Bleier and Eisenbeiss, 2015).

Placement and congruence

The placement factor studied by the targeted online advertising research notes that congruency between on where the ad appears and the purpose of the ad is important influencer of the effectiveness. Personalisation affects perceived informativeness and intrusiveness. Consumers perceive personalised ads as more relevant and informative but at the same time they acknowledge how off-putting they might be. If consumer’s goal is fulfilled by the appeared personalised ad the congruency is reached, and the ad is not viewed as intrusive. Therefore, it is important to take into consideration two modes of browsing, experiential and information seeking. As noted by Bleier and Eisenbeiss (2015), click- through rate is higher when consumers are in the experiential browsing mode. While, personalised ads are viewed as more intrusive when there is a browsing goal set by the user. Overall, it means that under motive incongruence personalisation does not contribute towards information, but rather influences intrusiveness. Whereas, congruence adds to the personalisation by enhancing informativeness and reducing intrusiveness. As a consequence, this also leads to higher numbers of view-through advertising and engagement (Bleier and Eisenbeiss, 2015).

I personalised! Why do they resist?

Figure 1. Asos cross-device tracking (Bronto, 2015)

Privacy

While perceived relevance, time and placement are important factors driving effectiveness of personalised ads online, it is important to understand what main aspects influence resistance or avoidance of personalised advertising. One of the main reasons to resist or block a personalised ad is related to privacy concerns. Privacy concerns can be explained as the degree to which consumers feel worried about being invaded, not having the power to be left alone and not obtaining control over their personal information. In online context 95% of consumers stated the concern about their private information being accessible to companies (Baek and Morimoto, 2012). Resistance to advertising occurs when advertising aims to control one’s choices. Personalised advertising may create privacy threats and thus provoke objection to the practice that keeps track and stores personal information as well as recent online behaviour (Simonson, 2005). As a consequence, consumers may shift their trust towards other brands. Likewise, they are more likely to provide inaccurate personal information to web sites, opt out of emails or use ad blocking software. In order to avoid this, advertisers have to make sure that consumer information is protected. On the other hand, Baek and Morimoto (2012) notes that even if privacy concerns decrease effectiveness, personalisation is a stronger variable driving effectiveness to a much higher extent. This can be explained by younger generation being used to internet media and therefore having lower privacy concerns than the older one.

Irritation

Furthermore, perceived irritation is a strong moderator in effectiveness of personalised advertising. In this context, irritation can be defined as the degree to which consumers feel displeasure or impatience. Content and execution are often the main reasons causing resistance. For example, if an ad pops up too many times, the content is exaggerated or too many ads appear all at once consumers are likely to feel annoyed. Also, this will lead to advertising avoidance or scepticism. In relation to personalisation, consumers perceive irritation when ads conflict with their cognitive tasks even if they are addressed to them. Some will seek to regain control over the processing by avoiding them. Generally, there is a correlation between irritation and ad avoidance, therefore if a consumer feels irritated the lack of effectiveness in personalised advertising can be predicted (Baek and Morimoto, 2012).

Personal vs market relevance

Lastly, as discussed previously, perceived personalisation may influence resistance towards online advertising. Personalised ads are usually firstly consumed as a threat to consumer’s freedom. However, most of these ads have an ‘opt out’ option and if the consumer is aware of the ways to avoid or exclude the communication from his browsing radar the control is regained. Simply put, if consumer knows about the ways to avoid the ad, he views the ad more favourably. Moreover, personalisation is viewed positively if an ad comes with certain benefits. For instance, certain loyalty points or special discount will reduce the resistance even when the message is not accurately perceived. Well-made personalised ads will contain desirable information. However, the value of information is diminished when customer is sceptical about advertising. Many consumers are resistant to the set of promotional activities and the market’s practice as a whole. Related to anticonsumption, the resistance is visible when people do not engage in market’s rituals, such as valentine's day, in a particular market. Taken as a whole, perceived personalisation can really contribute towards effectiveness of the ad but if market resistance is not taken into account the execution of personalised ads might suffer (Baek and Morimoto, 2012).

Online personalisation and retail

Figure 2. Retargeting advertising (Adroll, 2016)

Personalised ads are very popular in retail industry. For example, retargeting ads (figure 2) are widely used to pursue the customer to come back to the online store. However, with customers being more conscious about the current technology and brands’ ability to customisation, the way personalisation works have to be considered. When looking at the phenomenon from the practical side it is interesting to think about Eli Pariser’s TED talk about ‘filter bubbles’ (Ted, 2011). This occurs when a company provides consumers with the content which is supposed to be relevant based on the past browsing activities, location and so on. As a consequence, consumers are missing out on the new information which may be interesting for them. For example, the changed sizes or tastes are not incorporated in advertising suggestions or google search excludes particular results which are deemed as irrelevant for a user. Asos is one of the most prominent retailers using personalisation in their advertising strategy. While, it works in most cases, the fact that a few people in a family can use the same browser is not considered. Therefore, personalisation can be perceived as irrelevant and intrusive causing resistance depending on the user. Moreover, in terms of Asos example the changed preferences or sizes can cause ad ressistance if the information is no longer accurate. Graham (2014) asks a question as to whether online retailers discourage purchasing and limit the choices for consumers by personalising their online experiences too much. While, there are many opinions about personalised advertising the new concepts discussed in terms of effectiveness include transparency and the choice to be tracked. While Asos informs about using cookies online, Coca Cola ‘share a coke’ campaign gives consumers a choice to be part of personalisation and engagement online through social media and other online platforms. Even if the two examples are different it is important to understand the extent as to which personalisation should be used. In order to do so, the enabling and disabling drivers for personalisation effectiveness should be considered and implemented in the execution as well as the overall strategy of online advertising. Figure 3 includes all of the discussed factors influencing effectiveness and resistance, the arrows show what effectiveness factor should be implemented in personalisation in order to disable the resistance driver.

Figure 3. Enabling and disabling forces driving effectiveness in personalised advertising online

All things considered, there is no one answer as to whether personalised online advertising always works. The simple principle that the ad will be perceived as relevant just because it is based on consumer’s prior browsing activity is not right anymore. Internet users are aware of being tracked and they can critically evaluate the content which is pushed upon them. While advertisers are enabled to more power when individually approaching consumers online, the audience has control to avoid or block the sent messages all together. The effectiveness of personalised advertising online varies depending on the number of discussed factors. In order to get positive consumers’ responses, the advertising has to be personalised in a right way emphasizing the perceived relevance, timing and placement. Moreover, the typical resistance factors such as privacy concerns, irritation and overall market resistance have to be incorporated when thinking about the execution of the ad and the extent to which personalisation is acceptable. As digital landscape is becoming more and more personal, consumers have to feel the freedom and control over their personal information and decision making. In this case, both parties, marketeers and customers, should answer the question of what degree of personalisation online is actually enough.

References

Adroll, (2016). How does retargeting work? Available online : https://www.adroll.com/getting-started/retargeting [Accessed 16 February 2016].

Baek, T. & Morimoto, M. (2012). Stay Away From Me. Journal Of Advertising, [e-journal] vol. 41, no. 1, Available through : LUSEM Library website http://www.lusem.lu.se/biblioteket [Accessed 16 February 2016].

Bleier, A. & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, [e-journal] vol.34, no. 5, Available through : LUSEM Library website http://www.lusem.lu.se/biblioteket [Accessed 16 February 2016].

Bronto (2015). Smart ways to market smartphone users. Available online : http://blog.bronto.com/commercemarketing/smart-ways-to-market-to-smartphone-users/ [Accessed 16 February 2016].

Graham, C.(2014). Personalisation in retail : how to get it right, web blog post available at : http://www.latitudegroup.com/blog/personalisation-in-retail-how-to-get-it-right [Accessed 16 February 2016].

Kelly, L., Kerr, G. & Drennan, J. (2010). Avoidance of advertising in social networking sites: the teenage perspective. Journal Of Interactive Advertising [e-journal], vol. 10, no. 2, Available through : LUSEM Library website http://www.lusem.lu.se/biblioteket [Accessed 16 February 2016].

Keyzer, F., Dens, N. & De Pelsmacker, P. (2015). Is this for me? How Consumers Respond to Personalized Advertising on Social Network Sites. Journal Of Interactive Advertising, [e-journal] vol. 15, no. 2, Available through : LUSEM Library website http://www.lusem.lu.se/biblioteket [Accessed 16 February 2016].

McMillan, S.J. (2004). One face or many? Internet Advertising : Theory and Research, 2nd ed., Available online : http://web.utk.edu/~sjmcmill/Research/McMillan%20Chapter.pdf

Simonson, I. (2005). Determinants of Customers' Responses to Customized Offers: Conceptual Framework and Research Propositions. Journal Of Marketing [e-journal],vol. 69, no. 1, Available through : LUSEM Library website http://www.lusem.lu.se/biblioteket [Accessed 16 February 2016].

Tam, K. & Ho, S n.d. (2005). Understanding the impact of web personalization on user information processing and decision outcomes. Mis Quarterly [e-journal], vol. 30, no. 4, Available through : LUSEM Library website http://www.lusem.lu.se/biblioteket [Accessed 16 February 2016].

Ted (2011). Beware online : “filter bubbles”. Available online :https://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles?language=en [Accessed 16 February 2016].

Yu, J. & Cude, B. (2009). Hello, Mrs. Sarah Jones! We recommend this product!' Consumers' perceptions about personalized advertising: comparisons across advertisements delivered via three different types of media. International Journal of Consumer Studies, [e-journal] vol.33, no.4, Available through : LUSEM Library website http://www.lusem.lu.se/biblioteket [Accessed 16 February 2016].