3 Steps to Adopting Big Data for Marketing Innovation

Big Data Marketing

The single most important aspect of Big Data for brands is now social data. After all, it’s consumer data that is growing exponentially, providing an ever-expanding and real-time window into candid consumer conversations. The bigger that window gets, the more value brands are capable of receiving.

Just how much are consumers contributing to the pool of Big Data? Every 60 seconds, more than 300,000 Tweets are generated, 72 hours of video are uploaded to YouTube, and 1.8 million “Likes” appear on Facebook.

That’s important because millions of conversations about products and experiences help us learn new things about consumers, such as consumption patterns, use cases, consumer needs, anxiety states, how consumers engage with products, and how they engage each other. For marketing professionals, the social aspect of Big Data helps them understand what influences purchase decisions, how people respond to marketing, what drives behavior, and what inspires action.

How brands can use Big Social Data for innovation

The social web is massive part of Big Data. Many companies try to treat it as a data pool to measure performance such as brand mentions, “Likes,” or the number of retweets. Not many companies are treating its Big Data as a source of consumer insights or competitive intelligence for informing business decisions.

Brands interested in using the social aspect of Big Data for innovation should take these steps:

1) Segment data

Move beyond treating the massive Big Data pool as a singular entity for consumer insights by segmenting data. Instead, brands should focus on using the social aspect of Big Data to create focused interest groups and audiences that can reveal more about who their target consumers are, where to reach them, what they like, and the messages to convey. These groups still hold a massive amount of information, but because they are segmented, they can more easily provide immediate and reoccurring value for brands. Immediate value is derived from the ability to better understand a particular consumer segment, while recurring value is derived from having these audiences pre-segmented to run future analysis against.

2) Discover themes

Ensure that the massive Big Data pool is easier to manage and sort through. Traditionally, brands have relied on search-based tools for managing Big Data, a methodology adequate for learning more about a specific segment of the data and focusing in on a singular, known theme. In order to truly innovate, brands need to use Big Data as a starting point for understanding more about consumers. The best way to do that is by treating the process of consumer insights with a discovery-oriented methodology, which uncovers themes as they become real-time trends. This approach to understanding Big Data lets brands capitalize on the unexpected needs and desires of their target consumer.

3) Speed up

Ensure that access to the social aspect of Big Data happens in real-time. Standard listening technologies treat social as a resource for brands to look back on, to gauge campaign effectiveness and validate past actions. This does not enable them to innovate. Consumer conversations on the social web are an incredible resource because they are happening in the present. Every second, millions of unbiased and unfiltered opinions are being expressed, which give brands insights they can use to optimize campaigns in real-time. The ability to act on Big Data immediately gives them a competitive advantage.

As the social aspect of Big Data continues to grow, brands will no doubt continue to turn to it to better connect with consumers. The determining factor for those that want to adopt an innovative approach to marketing—one that provides an incredible competitive advantage—is the ability to use the data in sophisticated way.