Stock prices move with Twitter mentions

It is becoming clear that both quantitative and qualitative analysis of social data can be useful for establishing relationships and in predicting real-world events. Stock performance for the brands in this analysis increased and decreased in line with the fluctuating volume of tweets about these brands. This is a remarkably high positive correlation of .91, meaning that high Twitter volume tends to coincide with high closing price, and vice versa. The same things that make stocks move upward tend to make social chatter spike (such as well-received product announcements and high profile executive hires). But there’s a piece of this finding that’s counterintuitive. Shouldn’t the same factors that send a stock downward (such as a lawsuit, product flop, or poor earnings call) be just as likely to trigger a bump in conversations about the brand? Apparently not. It seems that Twitter users buzz more about brands as they perform well on the exchanges, but quiet down a bit as their stocks fall.

Other analyses have shown social data to reflect economic factors. Product reviews mention price more when consumer confidence is low (a -.66 correlation). In February 2009, when consumer confidence hit its lowest point, mentions of price hit their highest point, accounting for 11.5% of all US reviews. When we map these price references to the Dow Jones Industrial Average, an even stronger negative correlation (-.68) is revealed. Price mentions fall as the Dow average rises, and they rise as the Dow falls. And a 2010 study by academic researchers at Indiana University and University of Manchester found that measuring select dimensions of “Twitter mood” can be 86.7% accurate in “predicting the up and down changes in the closing values” of the Dow Jones Industrial Average. As more of these predictive relationships are discovered and subject to rigorous testing, social data will be incorporated into things like demand forecasting and product release timing.

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Twitter evolves from portal to destination

As Twitter grows, it is becoming a destination rather than a portal or midpoint. People are increasingly going to Twitter for the experience it delivers — conversation and timely information — not as an intermediate step between them and what they’re really after. And they’re staying longer. But Twitter is used much differently than other social and web channels, and its data should be used differently, too.

For example, we compared online search terms that include “Adidas” to mentions of “Adidas” on Twitter. Since search takes place when someone is looking for specific information, the terms that appear during a search vary greatly from other types of content. Top “Adidas” search terms tend to be at the level of product lines and categories, and of the top 20 searches, only three are for specific products. The top 20 search terms associated with “Adidas” also included the names of two competing brands, indicating that comparison shopping begins in the search box.

Twitter users tend to reveal their personal interactions with or in relation to brands. When we dig into what people on Twitter are saying about Adidas, they mention what “new” Adidas products they’re wearing “today,” and use words such as “my” and “I.” Some of the top Twitter terms associated with Adidas reference specific campaigns, like the hashtag “#branch309adidasday.” Businesses interested in doing their own text analysis on tweets should think of it as a way to roughly gauge consumer response to news, events, campaigns, and as a method of identifying enthusiastic customer advocates. But they will have to develop repeatable methods of separating relevant, authentic tweets from the abundance of noise.

In contrast to search and Twitter language, reviews tend to focus on specific product qualities (“light,” “looks,” “fits,” “comfortable”), adjectives (“awesome,” “great,” “different,” “perfect”), and other expressions of sentiment (“love,” “disappointed,” “happy,” “recommend”). Since every single review is tied to a specific product, they are a rich social data source for product-level insights. In fact, 12% of all reviews include product suggestions, and a fifth of all four-star reviews provide this type of feedback.

Use search patterns to optimize your brand’s search engine marketing and optimization strategies.

And use the words you find in tweets about your brand when you tweet about your brand. Optimize your marketing copy by using the language of your top reviewers—or better yet, quote them. When you reflect what consumers say online in your own social efforts, you’ll create content that’s more shareable. Quantify this over time by testing optimized tweets, UGCrich copy, and SEM strategies derived from the actual terms people use when searching for your brand and products, and compare each to the non-optimized, marketing-derived alternative.

Tweets that mention brands are using fewer links over time. In the last half of 2010, 68% of tweets that mentioned brands also had links in them. In all of 2011, the number dropped to 55%. In the first half of 2012, the number drops further to 51%, signaling a clear downturn in link usage. This means that content about brands on Twitter is becoming increasingly conversational, and less transactional. Users are talking about brands instead of just pointing to what they bought or want to buy with a link to external sites.

[Download PDF to see Graph]

External data confirms this. Twitter users are spending more time on Twitter, and visiting many more pages within Twitter.com while they’re there. According to data from Compete.com, from 2010 to 2011, there was a 19.8% increase in average time on site; in 2011 to 2012, so far, there is a 19.7% increase in time on site. And average pages per visit decreased 9% from 2010 to 2011, but increased by an incredible 58.7% from 2011 to 2012.

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Brands get more tweets, but less of the conversation is about them

The volume of tweets per day has grown 143% from 2011 to 2012; however, mentions of brands on Twitter have only grown 113% in the same period. To maintain and improve Twitter share of conversation, brands should analyze their data to find which tweets are generating positive conversation about them, emulate these tweets, and continuously optimize and add fresh content.

Original tweets about brands are declining over time, as retweeted brand mentions are rising.

In other words, more and more content is simply repeated verbatim or with little alteration from the original source. In 2010, 85% of brand mentions on Twitter were original, and 15% were retweets. In 2011, 18% of brand mentions were retweets. So far in 2012, 22% of all brand mentions on Twitter have been retweets, and only 78% of brand mentions are original.

There’s good news and bad news for brands in this data. The increase of brand mentions overall means there is more data to learn your customers’ thoughts about you, but as the retweet analysis shows, that data is increasingly redundant. Retweets are becoming a bigger part of the Twitter brand story, but a retweet is a weaker social signal than an original tweet from, say, an advocate or detractor. Retweets also contain less original data, and may not represent the users behind them as much as a wholly original tweet from the same user. Our research also found that some of the most retweeted content is the work of automated bots (nonhuman scripts) and spammers that have set up networks of auto-retweeting accounts to spread their inauthentic messages across the social web as quickly as possible before Twitter shuts them down. Altogether, this means that businesses need to apply more scrutiny to Twitter data. Perform spot checks, weight and filter your metrics to place less emphasis on retweets about your brand if you find they are far more noise than signal.

[Download PDF to see Graphs]

The increase in retweets also illustrates that news travels faster than ever before—and that a single piece of content can have major consequences for the companies involved. In fact, many of the most retweeted messages about brands in our analysis were highly negative in sentiment, and concerned things like scandals, lawsuits, and negative press coverage. Now is the time to prepare social crisis communications plans if you haven’t already.

It’s also important for brands to get to know the real people that are creating the ripple effect for their brand across the network (and, as this Index shows, beyond) by creating consistently retweetable content—they are the greatest distributors of social currency. Reach them, highlight, and promote them if they are advocates, and address their concerns if they are detractors. Determine whether they are influential in other channels: Are they a top reviewer as well? Give them exclusive access: insider news, early product testing, event invitations, and the like. Make them feel like a part of your brand instead of a spectator, and in all cases, locate them as soon as possible.

Search interest doesn’t correlate to Twitter mentions, stock performance, or TV and radio coverage

While it may seem that people tweet what’s top of mind, they’re not tweeting about what they’re searching for. While we saw this across the board, we’ll use Clinique as an example. Twitter mentions for “Clinique” spike in April 2011, August 2011, and March through June 2012. During these Twitter peaks, however, we saw either no correlation with search interest or a decline in search interest (search interest is Google’s normalized indicator of “the likelihood of a random user to search for a particular search term” on a 0-100 scale). When we compared the stock performance of the brands in this analysis to search interest for the same period in time, we found no correlation.

[Download PDF to see Graph]

We analyzed 8,000 brand mentions in closed captioning data from television broadcasts (most TV ads are not closedcaptioned, so ad mentions are not reflected in this data), and radio transcripts (this data does include ads) to determine whether brands being mentioned in traditional media saw a corresponding bump in search interest. Surprisingly, they do not. This suggests that unpaid coverage (news pieces, etc.) doesn’t drive much search activity, but findings from a study by Efficient Frontier show television ad campaigns correlating to a 60%-80% bump in brand-name search during the life of the campaign. So, while unpaid coverage in traditional media may be a great awareness mechanism, it’s not driving the consumer search behavior many businesses are craving. For that, television ads still seem to do the trick.

The bottom line? Social and “the real world” are becoming inextricable

The borders between “social” and “the real world” are difficult to pinpoint, but they’re being redrawn in some places, and eliminated in others. Consider this: Just a few years ago, the terms “in-store” and “online” gave us a clear, differentiated way to talk about channels. But as channels converged, and consumers began to use the mobile web while in the physical aisles, the terms no longer accurately described the way that people actually shop. The same is true of social—and everything it touches.

Convergence is a new concept for many companies, but it’s actually nothing new in practice. In fact, it’s the “place” we’ve called home since 2005. Reviews, Q&A, and stories are all forms of earned social content that live on owned digital real estate. And while we were helping clients across the globe integrate owned and earned, Twitter launched, Facebook opened to the public, and search became more and more social. Channels blossomed, and are now converging. Data exploded in volume and then fragmented, and is now coming together again. Convergence will soon cease to be the exception, and will become the rule, just as product reviews on company websites were once the exception.

Social’s connection to the world around us is has been established in some areas, cannot be found in others, and has yet to be discovered or quantified in most. But it’s far better for businesses to look for it everywhere and find it only in some areas, than for them to stumble over it where they least expected it.