Michelle Accardi is Vice President of Marketing for CA Technologies, Inc. She is responsible for CA Technologies’ entire online marketing capability and technical architecture across all business units.
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Feature Article: February 2013
Check out Craig S. Mullins’ blog on data + database technology. more>
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Tweets, posts, blogs, clips, responses, comments, reviews and more create content across a variety of social media sites like Twitter, Facebook, Foursquare, YouTube, and many more. Not only is there a diversity of content in type, but diversity is also found in sites and sources. This content can range from conversational exchanges between friends, broad public remarks about an experience, and reviews of products and services to a whole range of other online communications. What this has meant for companies – both big and small – is a need to understand what is being said about their company, products, services, competitors and industries.
For some time, tools that simply monitored social media sites were enough for some companies: they could keyword search on their brand, for example, and track/trend volume, keyword popularity, and even Basic English sentiment polarity. Companies could use negative sentiment to drill down into social posts that talked negatively about their brand, and could trend these percentages and volume over time. Companies could reverse this process to understand the more favorable conversation around their brand and trend that as well. But what happens when the volume of conversation – positive or negative – grows to a volume that is simply impossible to review individually, and a volume that obscures the opportunities the data holds?
This is precisely what is happening with social data, and it is affecting all brands, both large and small. The volume of social conversation happening since the start of social media has grown exponentially. Salesforce.com says they have Radian6 indexing 3.6 billion social posts every month in order to uncover relevant insights to help drive their business. Every business’s share of this conversation is of a different size, but the problems of social Big Data exist for all: large consumer brands could see hundreds of thousands of posts per month about their brands and products – far too many posts to sort through by hand. Even keyword analysis proves a challenging method of extracting the insights that could fuel agile marketing. Smaller, regional brands have a quantifiably lesser volume, but still too many posts to sort through without personnel and resources to dedicate the time needed to read every post, organize, classify, and decide on proper actions.
Every company has a need to unmask social Big Data to reveal the insights that can drive agile marketing – promoting their brands, products and services, understanding the motivations behind selecting competitors, converting social leads into sales opportunities and more. In short, Social Big Data can fuel agile marketing.
Let’s look at what some of the agile marketing opportunities from social media can look like.
Agile is about putting conversations and interaction before processes and technology. Some of these conversations are about brands, and it’s these conversations that can inform the processes around managing that brand. Large brands, like a popular television show for example, could receive hundreds of thousands of posts every month. People that help market and promote the television show would be hard pressed to review each of the hundreds of thousands of posts to understand what’s being said about the show – it’s our social Big Data problem rearing its ugly head – but assuming we could review them all, we might see the things people love about the show. This can include specific episodes, guest stars, show themes, and so on. The opportunity to manage the show’s brand becomes clearer with the content – creating and responding to content that they know is already favorable gives them the best chance at taking part in the brand messaging.
More opportunities exist beyond brand management. Companies can also find opportunities around their product conversation. Imagine crafting marketing messages and communications that leverage the features and qualities guaranteed to resonate with prospects. Social media conversations hold the keys to agile product marketing. Conversations about the very features that drove buying decisions are being tweeted, posted to Facebook, and raved about in reviews. Imagine you sell a wide variety of laptop computers, and there are tens of thousands of posts every month on each of your major laptop lines with a large percentage discussing purchasing decisions. Again, too many to review by hand, but if you could, you start to see in the conversations features like screen resolution, battery life, and weight driving discussion and buying. The key to unlocking the potential is cutting through the volume to understand which features are most powerful for driving purchasing decisions.
We can turn these same lenses on any competitor of a brand and use social media content to understand what’s working well for competitors, and then use this information to position against them. Just as people are volunteering all the things they like about a brand and its products, they are also volunteering the things they do not like. And this can offer many valuable insights for positioning a brand against competing brands – and products against competing products.
Some of the highest revenue-driving examples of agile marketing in social media can come from industry chatter. Let’s take that same laptop example and envision monitoring social media conversations around laptops, divorced of any particular brand reference. Again, we would see the volume of this particular example exceed both our brand and competitor examples combined. If we experienced a challenging volume for brands and competitors, the volume around industry social conversations could be nearly insurmountable. But again, if we did have a method of making sense of the posts and cutting through the volume, we could unmask a variety of opportunities – including those previously mentioned – but also opportunities to create a new social sales channel from people expressing interest around buying a particular product or service. Such an opportunity could look like a tweet asking for laptop recommendations, a post complaining about a broken one, or maybe a comment on a blog offering reviews of popular brands.
Social Big Data Hides Opportunities
So we’ve seen a variety of opportunities that may exist in social media content for agile marketing – opportunities that can inform or even transform how a business represents itself, its products and services, its position in the market and more. But the key need to leverage any of it was cutting through the volume of data – the social Big Data problem – to get to the insights and content that mattered most. It’s a need to add a reasonable structure to what is relatively unstructured. And the challenge becomes even more complex when the static preventing insight is other potentially valuable insight. When monitoring something like laptops for example, we struggle to separate which of the hundreds of thousands of monthly posts:
Social Insights is the Key to Unlocking Social Big Data
Since social listening platforms are often keyword-based, it puts the challenge on the user to create complex keyword queries to attempt to find the desired data. Furthermore, it requires the user to know what combinations of keywords would produce which data sets. The result of these efforts can be hours of frustrating configuration changes and a questionable reliability on the data produced.
As social media content has shifted to create a social Big Data problem, so too has social monitoring shifted with the introduction of advanced analytics to social media content. Providers of these services can span several categories like insights on social talkers, demographics and interests, and also insights on social content, such as sentiment, emotion, intention and more.
Let’s revisit some of our earlier agile marketing examples to help illustrate this. If we recall Brand Management, the challenge was cutting through large volumes of conversation to understand which posts were helping or hurting the brand. By combining the coverage from Radian6 with the power of named entity extraction and advocacy and intention insights, show producers could, with a single click, instantly understand things like:
We can take the volume of data surrounding something like a popular television show by eliminating painful and laborious keyword querying and instead adding structure to what was formerly unstructured data. Radian6 Insights’ partner ecosystem allows for a way for show producers to know right away which TV Shows they’re competing with in social chatter, without having to know that list in advance. Similarly they could use advocacy analysis to understand the themes and topics that were talked about with the greatest positive language, without having to guess at the infinite combinations of words and phrases that indicate said advocacy.
The same holds true for our Product Marketing, Competitive Research, and Industry Research. Using a tool that allow them to filter they can become more agile by taking large volumes of social content and having it organized to any set of keyword queries to instantly give the marketers insights such as:
- Which features of products are getting rave reviews
- Which features cause the most problems and complaints
- What people hate and love about competitors
- What’s driving buying decisions online
- Identify social talkers ready to buy
- Identify social talkers in need of customer case
It is this collision of large, unstructured volumes of social content and leading analytics providers that have the greatest capacity to enable agile marketing drive through insights from social content. And in this case, Salesforce.com & Radian6 have made that process even easier by performing all of the analysis needed in a single platform, with a growing ecosystem of partners giving marketers the tools to unlock the power of the insights in that Big Data to give them competitive advantage.
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