Upon returning from the 5th annual Defrag conference in Denver, it became evident very quickly that the major theme at the conference was “big data”. Although the notion of big data has been around for some time in other industries, social media has created a whole new and exponentially larger deluge of data for businesses to leverage within the last couple of years.
To be exact:
- IBM just completed a study and found that more than 90% of the data in the world today was created in the last two years.
- Facebook allows users to share 30 billion pieces of content each month.
- Twitter’s active user base generates 250 million tweets per day.
- Combined, these sites create 17 terabytes of data every day.
- In total, every day we create 2.5 quintillion bytes of data.
What are the dimensions and challenges of big data? Roger Ehrenberg broke it down really well. Big data is:
- Complex
- Large
- Unstructured
- Real Time
Likewise, Jive’s David Gutelius described big data similarly using– the Four V’s analogy:
- Velocity
- Variety
- Volume
- Volatility
But let’s be clear – big data is in its infancy and growing at an exponentially high rate.
Analytics is More than Counting
This new data deluge promises to provide us the opportunity to turn information overload into an asset for better decision making by applying analytics. Even within analytics, it is more than just counting total volumes of activity – it is about mining intelligence from disparate data sources – looking for the patterns and relationships in the data itself.
Think of big data as the words of a story – by themselves they have limited meaning. When you add grammar and structure, you begin to link together these words and can develop many different meaning and insights from the collection of words. The science of big data is similar – data stored in information systems are the words and the emerging data analytics is the grammar which allows analysts and decision makers to derive meaning from data.
There are some challenges in doing it the old way in collecting these huge volumes of data and analyzing it. It can be inefficient and cost prohibitive and difficult to scale. People and resources are being wasted. It is estimated that employees spend up to two hours a day looking for the right information, such as analyzing tweet streams.
Collecting smaller subsets of data and analyzing it for patterns over time will provide better predictive capability that just storing and counting total volumes.
Your Customers Are Engaging in Real Time
And businesses are finding that they have to move quickly. Their customers are multiplying like rabbits online.
Did you know that socially engaged consumers spend more on brands than those people who don’t interact? That’s according to a new Bain & Company report, which studied social media and its role in marketing.
Apparently, the study found that people who talk to brands on networks like Twitter and Facebook spend 20 to 40 percent more money on their products and services compared to those who don’t. They also show a ‘deeper emotional commitment’ to companies who use social media – 33 per cent higher than the common measure for customer loyalty.
Due to the speed and access of information at their fingertips, consumers expect the same quick response if they talk to brands online. For example, if someone ‘tweets’ a brand, they expect an instant reply. If they don’t get this ‘real-time customer service’, they’ll feel ignored and your reputation could be at stake.
Looking at these fascinating facts and figures, it’s clear. Engagement adds real value these days, a crucial aspect of movement marketing. But how does engagement lead to more sales?
Big Data is About Connecting the Dots
With this extremely large and unstructured data set that consists of qualitative, quantitative and the social graph, there is a smaller very relevant subset of data that represent opportunities for sales and marketing to capitalize on. When this data set is combined and correlated with an organization’s internal data set, it becomes a lead-gen pipeline for and a more efficient and effective method of attracting, engaging and supporting customers – in real time.

External data when collected and analyzed can provide awareness, intent and conversions and when combined with customer relationship management systems, provide opportunity for customer support and engagement.
Connecting the dots between external and internal data presents a new opportunity for marketing to directly impact sales and customer support.
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