I recently attended an event, “Big Data for Engagement and Content Creation,” which was sponsored by the Atlanta Interactive Marketing Association (AiMA). While “big data” has recently become an industry buzzword among many working in the digital space and cursory discussions about the topic have become a dime a dozen, I was pleasantly surprised that the panel discussion I attended dove deeper and explored in greater detail the impact that big data will have on today’s market.

What Is Big Data?
As the name implies, “big data” refers to data sets that are large and complex, and require more than on-hand or traditional data management tools to parse and analyze. Increasingly, though, when people talk about big data, they are referring to the recording and collection of anything that can be quantified, not simply numbers. Most marketers are aware of this shift in perspective about big data and many are accessing and utilizing data to their advantage. But quantitative data isn’t what’s changing—it’s what can’t be (or traditionally wasn’t) counted that’s redefining the meaning of big data. No longer is big data limited to the collection of demographics, CPMs, and CTRs. Our understanding of big data has been refined to include a better understanding of the breadth of data sets being examined, rather than simply the pure size of data, and that breadth is being derived from the addition of qualitative data to the equation.

Layering Effect and Data Success
Through strategic layering of these two categories of data—qualitative and quantitative—marketers are starting to gain a clearer picture of campaign performance. An example of how layering adds resolution and context to traditional data is within the realm of social media. There may be hundreds of comments generated from a Facebook post; a community manager simply exports Facebook data, open the results in an Excel worksheet, and, like magic, the data is ready to be presented to a client. However, the number of comments can be a misleading success metric for a campaign, since the number alone omits the most important data point: tone. If 90 percent of comments are negative or sarcastic toward the brand, then the campaign is a failure. Therefore, it’s important to employ a measure of social listening to gauge the tone of the conversation and adjust the campaign’s social strategy accordingly. Success should be defined by the combination of hard numbers within the context of social listening.

Robots vs. Humans
There are many social listening tools that are designed to measure and quantify sentiment, but they are generally expensive and often misinterpret nuances. Alternatively, organizations in growing numbers are using human resources (like Community Managers) in conjunction with free online tools such as Hootsuite, TweetReach, Google Alerts and Analytics, Facebook Insights and Power Editor, Topsy, Social Mention, IceRocket, and IconoSquare. These resources make social listening an accessible strategic tool for any budget. The combination of machine-powered and human-powered intelligence can lead to a deeper understanding of what quantitative and qualitative data are expressing.

Leveraging Data Creatively
Content Cadence Testing involves live tests of several combinations of variables. Examples of variables include time of day, frequency, text length, inclusion of an image versus no image, user-generated content versus brand content. For example, if cadence testing revealed that user-generated images produced the best results, than creative teams should be briefed on this finding so that assets can be tailored to fit the data rather than pushed through social channels as is.

By interpreting and understanding numbers in the context of sentiment that may not be apparent in numbers alone, marketers will gain invaluable insights into the effectiveness of campaigns, and allow big data to better work for them.