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The 5 most common big data screw-ups

The 5 most common big data screw-ups Amy Masters

Data keeps piling up. What is a marketer to do? With 2 million Google searches per minute and 35,000 Facebook "likes" for brands per minute, there are mounds of data being created each day. The challenge is how to dig through it all and actually use it to sell your products.

The 5 most common big data screw-ups

That's really the objective -- selling your products. That's where marketers come in -- or should I say, that's where they should come in. But lately, too many marketers are getting caught up in the obsession with big data, and they're failing to leverage the trend in a way that truly benefits their brands. 

There are many ways that marketers are screwing up when it comes to big data. Some marketers are not leveraging big data at all. That's a mistake in itself. But many others are using it in ineffective, illogical, or incomplete ways. It's those inefficiencies and misuses that we'll examine in this article.

Forgetting to have an objective

It is utterly vital to fully spell out and understand your objectives before diving into the big data realm. What do you really hope to accomplish? In some cases, marketers might have their own personal objectives, but those objectives are not aligned with the overall strategy of the company. This is also a sure way to sink your efforts.

Aligning objectives with strategy will save you time in the long run. Sometimes the easiest way to ensure that you have the right objective is to ask yourself a series of questions: What are you trying to do with the data? How are you going to map that out? What are your goals? Who is your audience? How are you going to define success with your marketing campaign? What ROI do you want to achieve? Of course, not all of these questions are helpful for every situation, but you need to map out your end result so you can move forward with a great plan.

Bottom line: Once you answer the above questions, you will gain a clearer sense of how to move forward.

Getting buried in social data

Don't get buried under the mounds of data. Keep your head up. You need to separate the useless information from the much-needed information. This is especially true in social media.

As mentioned earlier, Facebook receives nearly 35,000 "likes" per minute for brands and organizations. These users also share 685,000 pieces of content per minute. What consumers "like" and share tells marketers a lot about those people, and those data points can help you predict who is likely to have the greatest interest in your brand and products.  But with so many "likes" and shares flying around Facebook -- not to mention other social platforms -- marketers need to apply some filters.

Develop a strategy, make a list of the relevant data points, and draw those out. You don't need to discard all other data -- but you don't need to apply every layer of insights to every equation.

Bottom line: There is a lot of data out there. Don't let it keep you down. Try to understand what you have and how you can use it. Don't be afraid to let certain pieces of data go unused.

Ignoring mobile data

Yes, I am focusing on one channel, but it is a huge one. If you ignore mobile data, it will ultimately sink your brand. By 2014, mobile commerce will reach $52 billion -- about 18 percent of total e-commerce sales. This illustrates the importance of mobile, both as a way to sell your products and as a tool to deliver your marketing messages.

Mobile is not just used to make purchases. Many users access the mobile web to find information as they are shopping in retail locations. More than 38 percent are "showrooming," meaning they are using their mobile devices to compare prices and products while in stores. Many consumers will walk out of a store and purchase elsewhere if the price and value don't match up. In other words, consumers are using their mobile devices as their personal shoppers.

So is your data strategy capturing this activity? Do you know who is searching for your brand on mobile devices and what percentage is following through with purchases? Do you know how many are walking away after finding your brand on a mobile device?

Bottom line: Mobile activity is a rich piece of the big data puzzle, and too many marketers are ignoring it. Make sure your strategy encompasses this vital channel.

Failing to deliver offers at the right time and place

Ninety-seven percent of coupons are never redeemed. Wow! Imagine the last time you used a coupon. I am talking about the paper coupons -- like the coupons in the Sunday paper (remember newspapers?). These coupons are not targeted to you. They are the same coupons that everyone receives.

If you're still using the Sunday paper method of getting your offers out there, you're miserably under-using your data. Imagine if you used your data to determine who, what, where, and when. Who would appreciate your offer? What should that offer be? Where should the offer be delivered? And when? Delivering special coupons to specific users is a powerful strategy that too many marketers are failing to employ.

I recently received a postcard from a local gardening center that provided a special offer if I made a purchase. I happen to like to garden. It's gardening season. And the center was in my neighborhood. But I won't use the coupon because I already made my needed purchases elsewhere. So the effort, which leveraged some data correctly, is ultimately wasted.

Take that example, and add a location layer of data using a service like Foursquare. There are more than 2,000 check-ins on Foursquare per minute. This takes location-based marketing to the next level. If that local gardening center had tied its data and special offer into a location-based system, it could have delivered its offer to the right people at the right time and place -- and at a lower cost.

The supermarket industry is another example where companies are failing to use their data properly. Thanks to loyalty cards, most supermarkets know every item customers have purchased over the years. But very rarely do they leverage that information to send out offers. Yet, every time a person checks out at the supermarket, that person is handed a stack of coupons with the receipts. It is too bad that customer didn't have that coupon as he was walking through the aisles. It probably would have prompted him to make a purchase or two that he wouldn't have otherwise. But instead, that coupon is likely to get dropped in the trash, right along with the receipt.

Bottom line: Leverage big data to deliver the right message, right product, with the right offer, at the right time.


This is the flip-side to the previous point. Yes, you need to leverage data in order to target your offers and messages. But you need to draw a line somewhere. So before you start slicing and dicing your data, decide how granular you will go.

Just because you have a ton of information doesn't mean that you need to use it all. Too much filtering leaves you with no one to target. Too many marketers are dissecting their data too many ways.

Bottom line: Be smart -- and judicious -- about how you are dissecting your data.

Forty percent of consumers will purchase from a retailer that knows their preferences. How are you going to use big data to understand those preferences? How are you going to act on those preferences? In short, will you use your data to your advantage?

In the end, big data isn't about how much you know about someone. It is about how you use what you know to better market your product.

Amy Masters is the VP of marketing at Payfone.

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"Various retro vintage number and typography collection" image and "Business failure and depression graph representing bankrupt" image via Shutterstock.

Amy Masters is the Principal of Dante Consulting Group. She provides provides marketing consulting services for mobile, technology and payments - all working within B2B for Fortune 500 companies and start-ups. Amy has an innate curiosity to...

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