Following my analysis of the languages spoken by #Copa100 fans on Twitter, somebody asked me: Does it even make sense to have language destinations if most people flock to the major account anyways? In other words: My resources are limited – so why put effort into crafting language-specific content when the majority of fans does not seem to care?
The answer is: yes, language destinations make sense. A lot of sense.
And here is why: Although we don’t reach as many people with the additional accounts (the average “foreign language” account has about 63% fewer followers), the ones that we reach are usually more committed. And greater commitment means more engagement with our content — and ultimately a stronger bond with our brand. At least that’s the theory.
Are “international” fans really more engaged?
Take Bayern Munich, for example. Their main Twitter account (@FCBayern) has about 2,85 million followers. However, given the popularity and social significance of Bayern Munich in German society (games and player signings often serve as token for conversation), many followers are likely to be less committed (read: average sports fans that just want to stay up-to-date) and therefore consume information rather passively. For many followers, Bayern Munich might only be their 2nd or 3rd favorite club that they revert to when the club plays internationally. Following (the entertaining) @FCBayernUS, on the other hand, requires more commitment to soccer in general and Bayern in particular, as the sport and club are not “mainstream-topics” in the US. As a result, a more active audience should be expected. Similarly, fans of Chicharito Hernandez following the Spanish-language account of Bayer Leverkusen (@bayer04_es) should be more inclined to interact with content that is specifically tailored towards their interests.
But is the really the case? Testing my hypothesis, I compared a total of 14 language destinations — including those of two leagues (Bundesliga, MLS) and three clubs (Bayern Munich, Bayer Leverkusen, FC Barcelona). This is by no means a representative sample, but rather a purposive one. I chose Bayern mainly because of the “unusual” way they run @FCBayernUS. To engage fans in the US, the account features more entertaining content (informal language, GIFs, emojis, retweeting of user generated content) than most “traditional” team accounts. In theory, this should result in greater engagement. Similarly, the Spanish Leverkusen account (started in 2015 after signing Chicharito) provides content tailored to his fans. Furthermore, I chose the official Bundesliga accounts (German and English), to assess how the expanded international TV deals (especially in the US) affect engagement. Similarly, I was interested in potential differences between the English and Spanish accounts of the @MLS. Finally, I added three @FCBarcelona accounts — just because the club is probably the most extreme example of creating language destinations (see Figure 2). Also: The club’s main account is in English rather than Spanish (all other clubs and leagues in the sample use their “native” language for the main account). And: In contrast to most other entities, all Barcelona accounts tweet the exact same content (with very few exceptions). In other words: They do not tailor content towards specific audience segments, which might reduce the benefit of language destinations. Here is what I found:
Language destinations show more engagement
- Teams get more engagement than leagues. Fans identify with their favorite club – not necessarily the league the club plays in.
- Language destinations out-perform the “original” account. For all entities in the sample, the language-specific accounts received more favorites and retweets per 10,000 followers. The most impressive numbers come from @FCBayernUS (7 x more favorites; 10 x more retweets than @FCBayern) and Leverkusen’s international destinations.
- It is easier to like than to share: All accounts received more favorites than retweets. This yields support for the argument that a retweet/share should be valued higher than a favorite/like when evaluating social media metrics. Favoriting a tweet involves lesser commitment and effort than retweeting and thereby endorsing a tweet and might be done for a different reason (e.g., archiving function, social token).
- Content matters: Language-specific channels yield the biggest benefits when their content is specifically tailored towards the targeted audience segment. In other words, simply translating the “original” content is not enough. Language destinations designed around a specific purpose (e.g., a player, cultural engagement) tend to generate the most engagement.
Method: Some detail on the analysis
Data Collection: I accessed the Twitter API using the userTimeline function of the twitteR package in “R” to call up the timelines of the selected accounts. Using this method, Twitter limits the search to a relatively short period of time (usually between 1 – 3 weeks. However, I was able to go back until November 2015 for @Bayer04_es). Other methods (such as Pablo Barbera’s getTimeline function) allow downloading up to 3200 tweets, but showed inconsistencies for key variables during data collection. Therefore, I chose data-quality over sample size and defer the larger-scale analysis until later. Overall, I collected 6556 tweets across 14 accounts. The number of tweets per account ranged from a low of 88 (@MLS) to a high of 1639 (@FCBarcelona).
Analysis: Twitter provides two metrics that are commonly used as a proxy for user engagement by both industry and academia: favorites and retweets. Despite questions about the validity of these measures (e.g., does a favorite on Twitter really mean somebody engaged with your tweet – or is it a social currency acknowledging your relationship?) and uncertainties about their value (how much is a favorite worth – and how much more value should be attached to a retweet that actually increases your audience?), they a) still seem to be accepted as the industry standard, and b) are the ones I can easily measure automatically. To allow for direct comparison of all analyzed accounts, I normalized both engagement measures as averages per 10,000 followers. By doing so, @Bayer_EN (18k followers) and @FCBarcelona (17,8m followers) have a level playing field to compete on.
You can find some descriptive statistics here.