We’ve all heard the possibilities, benefits and promises associated with big data. Yet, despite all the claims and expert opinions, only about 29 percent of organizations are adopting big data for predictive analysis and improving operations, according to a report by Accenture and General Electric.
Why is that? Maybe too many organizations assume big data is just another buzzword that over-promises and under-delivers. That just isn’t the case. Without a doubt, data analytics aren’t some magic eight ball that holds all the answers. However, using the right analytical processes with the right information will help organizations discover important insights that can greatly improve marketing efforts.
Falling Behind Means Staying Behind
While big data implementation might be slow, that doesn’t mean it’ll stay that way. With new technology like the Internet of Things (IoT), and better analytical platforms, big data is on the verge of going mainstream. That means the first companies to get on board will be miles ahead of those that drag their feet. Because technology advances exponentially, those who don’t find a way to implement new trends now will find it difficult to ever catch up.
If you’re debating whether or not to adopt a big data focus, or looking for ways to convince leadership that now’s the time to get involved, perhaps the following big data use cases will serve as some inspiration. While there are many cases where data can help, the following are three must-haves for any marketer looking to succeed.
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Big Data Use Cases
You may have heard of big data’s three V’s: volume, variety and velocity. Of these three, velocity is too often overlooked. People want lots of data, and different kinds, but they often pay little attention to how fast they can get it and process it. Our world moves at remarkable speeds, which means what might be relevant today could be stale tomorrow. The faster companies decipher information, the faster they’ll find actionable results and outsmart the competition.
When preparing a marketing campaign, marketers decide on messaging and communication tools based on audience information and preferences. However, as things begin, variables come into play that sway opinion. The ability to see how people are responding to a company’s tactics, in real-time, offers the benefit of making changes on the fly. This could mean the difference between a major success, or a huge flop.
Did you know that 50 percent of sales at physical retail locations are influenced by digital programs? In our world of mobile devices and e-commerce, it’s vital to understand how consumers are making decisions. Purchases aren’t made by simple trips to the store. Consumers visit company websites, social media pages, catalogues, and brick and mortar stores while switching between multiple devices. With so many different options, it’s hard to predict consumer behavior and create seamless marketing and buying processes. Multi-channel marketing analytics allow marketers to understand the path consumers travel before buying what they need, and then providing insights on how companies can influence decisions along the way.
For example, Canadian Tire, a major Canadian retailer, learned that most Canadians live within 15 minutes of their stores. It paired its online store with physical locations so consumers could browse and purchase inventory online, but if they needed it immediately, could pick it up right from their local store. Another great example is PacSun. It launched an iPhone app that contains its entire online inventory available for purchase. The app offers outfit builders and QR scanners, which get codes from magazines, in-store displays and other print ads.
Customer segmentation isn’t anything revolutionary. Basic marketing classes in college teach the importance of segmenting markets based on demographics and psychographics. What’s new is the ability for big data to further breaking down segments based on more granular information. Information from social media platforms, IoT, mobile devices and many other data sources has the ability to help marketers learn even more about their audiences. More information allows marketers to micro-segment and create even more targeted messaging and campaigns. The more customized the approach, the higher its potential for success.
Time Warner is an excellent example of a company that benefits from data and micro-segmentation. Time Warner has millions of customers who generate 0.6 terabytes of data a day. This information has allowed Time Warner to create segments based on demographic data sets, local viewing habits, and even real estate records and voter registration. All of this information helps reach customers on a more individualistic level, whether through targeted content or mediums specific to each group.