Smith, a 30-year retail technology veteran with many years of experience in online marketing, has worked with a number of major brands over the years, including Apple, Amazon, and GM. The author of "Big Measurement," an eBook that explores the relationship between big data and its impact on online advertisers, Smith believes that marketers need to measure data more effectively and accurately--an essential step to getting the most out of a big data management platform.
So what is "big measurement," exactly? According to Smith, it's a way to bridge the gap between big data technology and the advertiser's business, to prove that the former has a positive impact (such as increased profits) on the latter. This process involves matching customer relationship data with measurement metrics to create positive results.
[ Cisco has acquired ThinkSmart, developer of location data analytics tools that help retailers better understand where their customers are and what interests them. Read more at Cisco Acquires ThinkSmart For Location-Based Customer Analytics. ]
"Big Data without a corresponding focus on Big Measurement provides no value. Marketers need to spend as much thought on what to measure as on how they plan to store the data. Therein lies the competitive advantage," writes Smith in his eBook.
Businesses that don't react quickly to technological change often die off, and big data represents a major shift in information management. In a phone interview with InformationWeek, Smith recalled a meeting at Apple about a decade ago, when Apple executive Eddy Cue, who played a major role in the development of the iTunes Store, discussed the company's new digital music platform.
"We had suddenly gone from selling $1,500 units to selling 99-cent songs," said Smith. "The shift was significant."
Big data is just as significant to today's enterprises. Here's a quick summary of Smith's 8 principles, each designed to help advertisers and marketers profit from their big data investments.
1. Accuracy. Bad data results in bad decisions. "To ensure the quality of your data you need to investigate it all the way to the source," Smith writes. Step out of your office and mingle with the folks on the big data front lines. Consider "rolling with the junior analytics folks or challenging the ad ops person."
2. Getting the Data Hierarchy Right. "Not all elements of data are created equal. People aren't paying enough attention to how they structure and organize the hierarchy of the data," Smith told InformationWeek.
3. Aligning Key Performance Indicators (KPIs) with Measurement. Are your KPIs aligned with what you're measuring? It's often easy to lose sight of the big picture.
Smith writes, "Interactive marketers are often guilty of this when debating whether a fractional improvement in the click-through rate was important, while missing the fact that the overall return on the investment was poor. The debate should instead be about outcomes. For example, did it generate increased sales or did it attract new customers? Did it improve brand awareness or did it reach a net-new audience?"
4. Action/Communication. In many organizations, acquisition and retention of data are often two separate "fiefdoms." This structure makes it difficult to benefit from big data's ability to provide actionable intelligence in real time. "Action can only happen when you align the organization correctly and create a shared language that can drive results," Smith writes.
5. Integration. Disputes about the accuracy and usefulness of data are common between multiple vendors, or among groups within an enterprise. To avoid these battles, organizations must ensure that various metrics are properly organized. Writes Smith: "The further distanced you are from your data, the more prone to measurement errors your solution becomes."
6. Stewardship. Organizations must take care of their data, particularly with growing concerns over consumer privacy. With the proliferation of Internet-connected mobile devices, as well as increased requests by third parties to access consumer information, stewardship of company data is essential.
7. Systems Right-Sizing. Each organizations has different advertising needs, and Smith's big data/big measurement model must be tailored for each company. Proper planning and controlled investment are very important.
8. Human Capital Alignment. A big measurement framework requires workers with specific skills. "To frame the skill set required, we suggest you look for quantitative marketers. In other words, people who can demonstrate agility around numbers," Smith writes.
A free copy of "Big Measurement" is available on the TruEffect site.
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