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Making Sense of Big Data

Date: December, 2012 --

Big Data – It’s the latest buzz word in direct and data-driven marketing and has largely replaced social media as the headline topic at industry events, webinars, expert articles and case studies. Big Data is even playing a part in changing direct marketing nomenclature as evidenced down under with the Australian Direct Marketing Association’s recent rebrand as the Association for Data-Driven Marketing & Advertising (ADMA).

Big Data means lots and lots of data, from which companies attempt to draw ideas, conclusions, and above all predictions of future human behavior. It now has meaning because data capture tools, computing capability and analytical programs have reached such an elevated level of sophistication that we are now able to perform increasingly accurate predictive exercises on more and more massive quantities of data. That data can be anything, and often is. Climate Corporation takes data about soil, weather, seeds and many other subjects to help insurance companies set rates for crop insurance in the US.

Predictions using Big Data are increasingly accurate, and deployable in nearly every aspect of life, and, of course, the privacy implications are massive. Add Facebook, LinkedIn, general browsing behavior and similar online behavioral data to increasingly robust personal information data of the legacy kind, mix with powerful analytical tools, and you have a huge Big Data versus personal privacy clash.

Lots of data, smart analysts, and cutting edge technology together have applications in a wide diversity of fields, from national security to predicting heart attacks, to finding love. For an interesting exploration of some of those fields, the recent best seller The Numerati, by Stephen Baker, is an interesting introduction. 

Closest to our discipline of marketing, we learn that supermarkets have developed the ability to target their weekly offers by many different variables, such as religious observance of households, to “bargain hunter shoppers”. As one might guess, they endeavor to avoid sending circulars to customers who only purchase items on sale. The maker of a barbecue cleaning product sold in Walmart focuses its mobile and online spend within 5 miles of the stores, in neighborhoods in which rain is not expected over the weekend, and on the two days before the weekend.

Big Data is also all the data about us that has been collected over time, or is being collected in real time. In the latter case predominantly but not exclusively on the internet. The scope of data, especially but not exclusively in the US market, is mind-boggling: purchase history, of course, property (real estate, second home, car, investments), education, religion, children, marital status, employment record, etc. And real time data is what we express about ourselves through our on-line behavior. 

During and after Hurricane Sandy, we were not at all surprised to find that following our searches for “generators” and “chain saws” on Monday morning before the storm, that our other shopping searches and weather site browsing brought us new display ads from a multitude of generator and chain saw vendors through the afternoon, some of them localized to stores close to us. This lasted until about a week following the storm. Big Data analysis using the new technologies and algorithms available enable us to make offers to customers before they even know they need the offers. Lowe’s conveniently reminded us we would need a gas can with our chain saw.

Amongst online retailers, we’ve seen Big Data play a role in customizing the online experience to fit a given user based on data elements attached to that user. As an example a retailer might target the images, copy and products showcased on its homepage based on the users IP address, e.g., coats and mittens for a visitor in Buffalo and shorts and t-shirts for another in Phoenix or something as simple as ensuring prices are listed in the visitor’s local currency.

Social Media data can also be leveraged here. At a recent industry event we saw how the luxury watch retailer Rolex is able to utilize social media data from those who “like” their brand on Facebook, which gives the company access to all the profile information for those users, to customize web experiences based on this data. For example if, due to a user liking Rolex on Facebook, Rolex, who are a major sponsor of many athletes and sporting events, knows a user likes “golf”, the Rolex homepage will prominently feature their involvement with/sponsorship of golfers, sponsorship of golf events, etc. and feature watch models in sport categories.

Whether the tools are indeed that sophisticated; whether the data available to you is robust enough; and whether you, or your customers, are comfortable with the process, are all the subjects you will need to consider and will, at least for the time being, likely vary from one vertical to the next. 

In many respects, “Big Data” is merely the point at which companies and professionals in the direct/data-driven and interactive marketing world, and indeed in the broader business community, recognize that an inflection point in the sophistication of the subject has been reached.  

No longer do we need to wait for orders on the catalogue mailing to peak five weeks out to know whether we need to restock. In theory, provided we have a set of stable data, such as an identified and trackable customer base, we’ll know much earlier whether to reorder based on some collection of other data categories which are informative. Perhaps those points will be what is purchased, or where purchasers are coming from, or maybe even the colors ordered, or event what the weather is like. Like any other form of knowledge, the more data we have, the more we can plan.

Do beer sales in South Boston go up when the Boston Red Sox win? Do sales of high end golf clubs increase after the Masters? Do calls to golf courses to make tee-time reservations go up when Tiger Woods sinks a 50-footer on TV? Does Amtrak experience more ticket cancellations on Monday trips if it rains on the week-end? Can they fill up the seats with discount offers?  (Some of the airlines are able to do price changes “on the fly” literally up to the moment the plane leaves the gate.) 

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In closing, we also must face the reality that is the corporate culture and skill set. Using Big Data to market in real time requires a different mindset and visualization capability through the entire organization. Decisions now must be made in minutes, not weeks. Risks will be more numerous, mistakes more immediately visible. Much of our marketing exercises will be “real time experiments”! Everyone will have to adapt.

There are also pitfalls that can come with too much data that can lead to inefficiency and gridlock. Therefore marketers should always be smart about the data they gather and only gather data for which they have an actionable purpose in mind. Data Services helps marketers better leverage their customer data through advanced online segmentation, analytics and intelligence tools within our MarketView database platform. Contact your Data Services, Inc. representative today to view an online demo!