“Big Data” is one of the next big things when it comes to analytics and demographic information. But when it comes to predicting how potential customers might use your site, buy your products, and share their experience on social media, having all that information doesn’t necessarily mean you’ll be able to put it to good use. In this article, learn how you can use Big Data to generate big ROI – as long as you don’t get lost in over-generalizations.
What Is Big Data, Anyway?
It’s a term we’re seeing thrown around a lot these days, but it’s not particularly descriptive. Large amounts of data? Big pieces of data? Important data? What’s little data, then? And how is it any different from the data that businesses have been collecting for years?
It turns out that there’s no solid definition. Essentially, “Big Data” refers to the massive amounts of information that are pouring in as our lives become more and more linked with our technology. Big Data is the integration of data from social, local, and mobile media into one huge set of information points. And businesses are starting to use this data to influence their marketing, generally by gathering demographic and behavioral data about customers and transmuting it into predictions about who will buy what, and how they can best be influenced to do so.
And this is all incredibly useful, of course, but all of that information can be overwhelming. Even worse, having all of that information and the technology to process it means that many marketing departments are starting to rely a little bit too much on the Big Data to make their decisions, losing the human element that is so much a part of crafting a solid ad campaign or developing a target market.
Is Big Data Valuable?
In short, yes, but only if you’re using it as part of a larger intelligence strategy.
According to self-defined “digital analyst, sociologist, and futurist” Brian Solis, one of the problems with Big Data is that businesses are relying too much on the data itself, and are leaving out what he calls the “human algorithm.” Big Data can be hugely overwhelming, with information constantly pouring in and data analysis constantly trying to put it all together. Yet, even with all of that data, it can be hard to get meaningful insight into patterns.
After all, your customers and clients are human beings, and we all know that humans are not the most predictable people in the world. Big data itself can’t figure out what questions to ask, or what shifts in data patterns mean at a meta level. Big data can give you the tools to work with, but you still need to put those tools in human hands.
Another factor in how useful big data might be to you is what kind of data you’re collecting. It seems that, at least when it comes to predicting buying behavior, not all data is created equal. Social and demographic can be useful to collect, as they can help you determine a target market, but when it comes to predicting client behavior, those types of data don’t go very far.
The results of Facebook’s recent foray into real-time ad bidding highlight this discrepancy. Before mid-2012, Facebook ads drew demographic and social data from users’ profile pages and activity on the site – and they honestly weren’t generating much in terms of sales. However, once the FBX exchange added behavioral data into the mix with third-party data sets, the new ads out performed the socially-driven ones, and adspace value skyrocketed. Which, of course, just goes to show the need for that human eye on big data to determine what’s actually useful out of a giant heap of information.
Using Big Data Well
If you want to generate big ROI from your analysis of big data, you know that you need to keep that human element involved in the process. Otherwise, your data is essentially all dressed up with nowhere to go. Clickz recently posted two great articles that offer a few pointers on not just using big data to generate ROI and sales, but using it well. Here’s the gist of what they had to say.
1. Ask the right questions.
Your data can tell you a lot of things, but your outcomes all depend on how you frame your analysis and investigation. If you stop at “what demographic is spending the most money on our product,” you’re not going to get very far. Instead, figure out the modes of analysis that will really drive your ROI, like asking questions about how to get your customers to spend more.
2. Focus on the future.
Historical data is important, but figuring out what your customers have done in the past is only half the equation – and if you’re just using big data to look at what’s already happened, you’re missing out on its real potential. Trends, competition, and even customers themselves are always changing, so don’t get stuck in the past. Keep up with changes in real time, and apply them to reaching your future goals.
3. Respect privacy.
Building trust with customers is one of the biggest ways of getting their business, and breaking that trust is one of the quickest ways to lose it. Big data gives you access to huge amounts of information about clients and customers, and many businesses forget that it’s a big responsibility. Make sure you have clients’ consent to use their information. Better yet, keep customers informed about how your analysis can improve their experience.
4. Step away from the data.
Here’s the kicker. Big data is an important and useful tool, but it’s only one tool that you have in your sales and marketing arsenal. Talk to members of your company and get their input on their perceptions of who your target market should be, and who your most valuable customers are. Talk to customers and get their input, and most importantly, focus on their experience. Ultimately, a good experience for customers is going to drive up your sales more than any data set will.
The Bottom Line
Big data is a hugely valuable tool for driving up sales and improving your marketing ROI. However, it’s important to use it wisely. Remember that data by itself isn’t going to get you very far, but rather needs to be coupled with human insight and intuition.
So collect that data, analyze it, and make predictions about who will best drive your profit, but make sure you’re asking the right questions, staying in the present, respecting privacy, and leveraging time spent with your own company and with employers.
Is your company using big data to develop marketing strategies? How do you bring the human element to big data analysis?
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