US vs. South Asia: A Comparative Analysis of Cultural Influence on Social Media Brand Advertising
Analyzing existing research, adaptive marketing strategies including ‘glocalization’ and research findings from a case study.
By Sarah Fahim, Gender and Cultural Strategist, ARGONAUT Inc., [email protected].
This is an independent analysis conducted by the author using publicly available data.The author or the member agency bear no affiliation to the brands included in this case study.
Background: Brand Strategy in Web 2.0
As the era of web 2.0 is maturing, a key area of modern marketing research focuses on social media. Particularly of interest to brand strategists is the influence of cultural characteristics on consumer behavior. In this article, comparative social media messaging strategies from a mature consumer market and an emerging one are discussed.
A case study of Coca-Cola’s Twitter messaging will be included from the United States and Pakistan to demonstrate the effects of culture on brand advertising. Through qualitative data, n-gram frequency analysis and clustered topic genres, this article offers research-based ground for future practice.
Before we jump into the case study, three golden concepts from existing research provide necessary context. The findings of these research studies are summarized and included for general understanding for marketers and brand strategists.
Hofstede’s Cultural Dimensions
Individualism / collectivism, uncertainty avoidance, power distance index (PDI), masculinity / femininity, and long / short term orientation (See appendix for definitions).
Hofstede’s cultural model holds eminent significance in scholarly cultural communication research. Formulated by five variables, this model can be applied to marketing communications in the present day context. Figure 1 depicts where several countries lie on the Hofstede quadrants based on a study1 of 55 countries, ‘How social are social media? A cross-cultural comparison of online and offline purchase decision influences.’
The study1 found that members of individualistic cultures tend to have higher uncertainty avoidance, hence place lesser trust in people and institutions when they seek information; whereas collectivistic cultures tend to have lower uncertainty avoidance and include opinion seekers as the source of information and social subcultures. More individualistic cultures have a lower power distance index (PDI) compared to collectivistic cultures, which tend to have members that are more opinion seeking than information seeking and place more trust in opinion leaders compared to individualistic cultures.
According to this model, the US is a highly individualistic, low power distance and high uncertainty avoidance culture whereas Pakistan falls on the more collectivistic, high power distance and low uncertainty avoidance end of the spectrum. Interestingly, Pakistan is slightly more long-term oriented than the US but the two cultures aren’t very far apart on the ‘long vs. short-term orientation’ dimension.
Standardization vs. Adaptation of Creative Brand Strategy
In the context of global vs. ‘glocal’ messaging strategies of the case study included below, standardization refers to a message design strategy that leads a brand message that is adaptable and applicable to multiple cultures and consumer markets. Adaptation of execution refers to message design that is either ‘glocal’, global or local in relevance to a consumer market2.
Figure 2: Standardization vs. adaptation of creative brand strategy
A Research-supported 101 on ‘Glocalization’
Roland Robertson’s definitions3 of ‘glocalization’ originate from the concept of ‘modernization’, which is not restricted to a mere society, rather the entire world. He defines ‘glocalization’ in culture as the occurrence of global and local events that influence consumers simultaneously.
Additionally, two axioms from a study4 are particularly relevant to modern brand strategy: a) “local events rarely remain local”, and b) “global events are (re)interpreted locally.”
Connecting ‘glocalization’ to the most influenced consumer segment globally, we summarized some key takeaways for brand strategists:
- Globalization and localization coexist and are co-dependent in the context of consumerism
- Young adults are more exposed to global media and global cultural influence than any other consumer segment; they are often challenged to combine global and local beliefs and behaviors when in the process of forming their ‘glocal’ identities
- Global brands are deeply embedded in cultural identity formation; any changes in the face of globalization that influence the “brandscape” also influence the glocal cultural identity of young adults.
A ‘Glocal’ Case Study: Coca-Cola USA vs. Coca-Cola Pakistan
Coca-Cola’s mass media and social media advertising in Pakistan recently demonstrated highly local adaptation and execution. This was a noteworthy shift from a ‘glocalized’ execution of the global campaign, “Taste the feeling.” The new messaging strategy capitalizes on a deeply embedded, cultural phenomenon of tea consumption in the South Asian market. It portrays Coca-Cola as the beverage of choice instead of tea. The campaign generated considerable conversation when a thematic video with follow up executions were posted on Coca-Cola Pakistan branded social platforms.
In four months, Coca-Cola has associated itself with short term events in the US market (National Grilled Cheese Day, Valentine’s Day, International Women’s Day) under the umbrella of the international ad campaign, “Taste the feeling.”
For this analysis, data has been collected through Twitter from the two official pages @CokePK (66.9 K followers) for Pakistan and @CocaCola (3.36 million followers) for the US to analyze if there is a difference in their messaging strategies, resultant topic genres, and the topics’ sentiment valence. We hypothesized that culture influences the way multinational brands portray themselves and advertise on social media in different markets.
Data Collection & Methodology
Over a period of one week, tweets mentioning the official Twitter handles of Coca-Cola USA and Coca-Cola Pakistan were collected through the social media analysis software, Netlytic. A set of 94 tweets and retweets were collected for @CokePK and approximately 9,000 for @CocaCola. A qualitative n-gram analysis was used for text mining and summarizing “opinions” or “topics” from the US Twitter dataset. (See appendix for details on n-gram analysis).
Findings
@CocaCola (the US)
From the @CocaCola US dataset, 28 unique topics were identified based on the highest number of bigram iterations in tweets, uniqueness and frequency. These topics were then manually assigned to one of the four relevant topic genre categories. The four topic genres that emerged from these unique topics included Plastic pollution in oceans, Coca-Cola, Disney magic and a few topics that did not fall under any of the three were classified as “other” – these topics were generic expressions like “I love”, “to get”, and “Thank you” as listed in Table 1.
Table 1
Topic Genres | n-gram | Count | Frequency | Sentiment Coding | Sentiment |
Plastic pollution in oceans | to stop | 59 | 0.11359042 | 0 | Neutral |
Coca-Cola | from @CocaCola | 58 | 0.111665159 | 0 | Neutral |
Plastic pollution in oceans | with plastic | 54 | 0.103964113 | 0 | Neutral |
Plastic pollution in oceans | oceans with | 52 | 0.10011359 | 0 | Neutral |
Coca-Cola | Tell @CocaCola | 52 | 0.10011359 | 0 | Neutral |
Plastic pollution in oceans | plastic pollution | 52 | 0.10011359 | 2 | Negative |
Plastic pollution in oceans | choking our | 50 | 0.096263068 | 2 | Negative |
Plastic pollution in oceans | stop choking | 50 | 0.096263068 | 2 | Negative |
Coca-Cola | @CocaCola #ShareTheMagicSweepstakes |
50 | 0.096263068 | 1 | Positive |
Plastic pollution in oceans | our oceans | 49 | 0.094337806 | 0 | Neutral |
Plastic pollution in oceans | pollution #EndOceanPlastics | 48 | 0.092412545 | 2 | Negative |
Disney magic | year-round #Disney | 30 | 0.057757841 | 1 | Positive |
Disney magic | #Disney magic | 30 | 0.057757841 | 1 | Positive |
Disney magic | magic from | 30 | 0.057757841 | 1 | Positive |
Disney magic | could win | 29 | 0.055832579 | 1 | Positive |
Disney magic | win year-round | 28 | 0.053907318 | 1 | Positive |
Coca-Cola | a Coke | 27 | 0.051982057 | 0 | Neutral |
Other | to cut | 22 | 0.04235575 | 0 | Neutral |
Other | Thank you | 22 | 0.04235575 | 1 | Positive |
Other | to get | 22 | 0.04235575 | 1 | Positive |
Other | I love | 21 | 0.040430488 | 1 | Positive |
Other | get a | 20 | 0.038505227 | 1 | Positive |
Coca-Cola | 1,200 jobs | 20 | 0.038505227 | 1 | Positive |
Other | by switching | 18 | 0.034654704 | 0 | Neutral |
Other | six-pack rings | 18 | 0.034654704 | 0 | Neutral |
Other | edible six-pack | 18 | 0.034654704 | 0 | Neutral |
Other | to edible | 18 | 0.034654704 | 0 | Neutral |
Other | Save wildlife | 18 | 0.034654704 | 1 | Positive |
A pivot table revealed (Table 2) that the categorized topics have a neutral or positive sentiment valence, cumulatively 17.86% for each. Negative sentiment was only associated with one topic genre, ‘plastic pollution in the oceans’.
Table 2
Column Labels | |||||||||
Negative | Neutral | Positive | Total % of Sentiment Valence | Total Sum of n count | |||||
Row Labels | % of Sentiment Valence | Sum of n count | % of Sentiment Valence | Sum of n count | % of Sentiment Valence | Sum of n count | |||
Coca-Cola | 0.00% | 10.71% | 137 | 7.14% | 70 | 17.86% | 207 | ||
Disney magic | 0.00% | 0.00% | 17.86% | 147 | 17.86% | 147 | |||
Plastic pollution in oceans | 14.29% | 200 | 4.29% | 214 | 0.00% | 28.57% | 414 | ||
Other | 0.00% | 17.86% | 94 | 17.86% | 103 | 35.71% | 197 | ||
Grand Total | 14.29% | 200 | 42.86% | 445 | 42.86% | 320 | 100.00% | 965 |
Tea is a beverage deeply embedded in the Pakistani consumer culture and generally in the South Asian region. A behavior change campaign for increasing Coca-Cola’s consumption through replacement of tea consumption is a highly localized messaging strategy, with low standardization as compared to the message design adopted in the US market, which was more ‘glocal’ in nature.
In a relatively small dataset, the positive sentiment stands at 13% compared to 35% negative sentiments correlated with the topic genre “Coca-Cola vs. tea ads”. This topic genre emerged after the tweets were analyzed and coded manually. Coca-Cola’s ad campaign claiming to be a replacement for tea generated 35% negative sentiments (Table 3).
Table 3
Column Labels | ||||||||
Negative | Neutral | Positive | Total % of Sentiment Valence | Total Sum of n Count | ||||
Row Labels | % of Sentiment Valence | Sum of n Count | % of Sentiment Valence | Sum of n Count | % of Sentiment Valence | Sum of n Count | ||
Coca-Cola vs. tea ads | 35.11% | 33 | 23.40% | 22 | 13.83% | 13 | 72.34% | 68 |
Coca-Cola compared to Shan’s ad | 0.00% | 0.00% | 4.26% | 4 | 4.26% | 4 | ||
Cola Next | 0.00% | 2.13% | 2 | 0.00% | 2.13% | 2 | ||
Other | 3.19% | 3 | 13.83% | 13 | 4.26% | 4 | 21.28% | 20 |
Grand Total | 38.30% | 36 | 39.36% | 37 | 22.34% | 21 | 100.00% | 94 |
The analysis proves that cultural characteristics have an observable and measurable effect on how brands portray themselves on social media. The message design (standardized and ‘glocal’ in the US, adapted and localized in Pakistan) and the impact created in two different cultures is evident through the difference in topic genres and sentiment valence percentage for each topic genre (Tables 2 and 3).
To conclude, consumer sentiment is quantifiable and critical to campaign performance, quantified via this case study. The purpose of this analysis is to provide basis for insights and evaluation for ‘glocalization’, globalization and localization strategies in brand advertising. To leave fellow practitioners with some key takeaways:
- US brands and audience are more adaptable to standardized and ‘glocalized’ messaging. The balance in the social media sentiment valence indicates this.
- Emerging South Asian markets are more likely to apply a localized messaging strategy to brand advertising on social media platforms.
For US brands to better reach South Asian markets and engage their audience through social media, a localized messaging strategy promises to be most effective. It can live under a global campaign, where a local taste gives it an extra pull for the culturally distinctive audiences in South Asia.
Appendix A: Hofstede’s Cultural Dimensions5
Masculinity / femininity
An independent cultural variable measuring what motivates people in a culture: wanting to be the best (masculine) or liking what you do (feminine).
Individualism / collectivism
Individualistic cultures refer to when people tend to look after themselves and their families, remain centered around a nuclear structure and don’t tend to rely on bigger groups of people. Collectivism, on the other hand, refers to people who belong to in-groups and look after each other in exchange for more loyalty; members of these cultures tend to be more “we-conscious”.
Uncertainty avoidance (UAI)
When members of a culture have low trust toward institutions when seeking information and avoid ambiguous situations, the UAI score is higher, and vice versa.
Long-term (vs. short term orientation)
This dimension describes how every society has to maintain some links with its past while evaluating challenges of the present and future. A pragmatic, future-oriented perspective among the members of the culture; individualistic cultures are more likely to be long-term oriented as compared to collectivistic cultures.
Power Distance Index (PDI)
This variable represents the extent to which the less power / influential members of a culture tend to expect and accept unequal distribution of power in the culture.
Appendix B: N-gram Frequency Analysis Method
N-grams are sets of co-occurring, adjacent words within a window of text. Depending on the value of ‘n’, the groups of words can be unigrams (comprising of n=1 word), bigrams (n=2), and so on. The web-based tool used for this paper (N-gram Analyzer) allows up to 5-grams. For the purpose of this analysis, the frequency was set at 2 (each group of adjacent words that occurs at least twice in the dataset will be counted in the frequency analysis) and bigrams were used for topic analysis (n=2).
References
- Kendall Goodrich and Marieke de Mooij (2014), ‘How ‘social’ are social media? A cross-cultural comparison of online and offline purchase decision influences,’ Journal of Marketing Communications, 2014 Vol. 20, Nos. 1–2, 103–116
- Jing Jiang, Ran Wei, (2012) “Influences of culture and market convergence on the international advertising strategies of multinational corporations in North America, Europe and Asia”, International Marketing Review, Vol. 29 Issue: 6,pp. 597-622, doi: 10.1108/02651331211277964
- Robertson, R. (2014). Roland Robertson. Globalizations, 11(4), 447-459. doi:10.1080/14747731.2014.951203
- Pierre R. Berthon, Leyland F. Pitt, Kirk Plangger, Daniel Shapiro (2012), Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy. 2012 Kelley School of Business, Indiana University. All rights reserved.
- https://www.hofstede-insights.com