|By Emily Mayfield||
|February 18, 2013 07:00 AM EST||
In an amusing article titled Thank Hillary Clinton (and leave your data too), authors Alexander Burns and Maggie Haberman reveal at what lengths some organizations will go to collect and expand their data. In recent weeks, advocacy organizations have bombarded voters with requests to send Hilary Clinton a virtual thank you card for her position as Secretary of State.
Underneath all the sincerity, these “thank you cards” are actually a means of grooming and growing data on target audiences. “The groups ‘thanking’ her this week say there’s nothing insincere about their applause for Clinton,” writes Burns and Haberman. “If they happen to be building their email lists and databases at the same time, that takes nothing away from [Clinton’s] status as a national political symbol…”
Big Data is slowly seeping into every facet of the digital (and political) marketing world. Marketers know they need data. They may even have the ability to collect the data, but the biggest obstacle marketers meet when confronting big data is: how do you leverage the data once you have it?
Last week, the Sales Engine marketing team sat through three days of meetings on goals, numbers, objectives, outcomes, actions, MQLs and SALs—all components driven and affected by data. At one point, it dawned on my boss to pose the question to the group: why don’t more marketing teams do this? Through recent research on Big Data, our team found a statistic claiming that 25% of retail marketing executives who were aware of Big Data and its relevance to retail had never considered the use of big data in a marketing organization (survey conducted by Edgell Knowledge Network). How can it be that top level executives are simply disregarding Big Data and its potential to advance their lead to revenue performance?
The team answered my boss with: “because it’s difficult”, “it’s time consuming”, and “people just don’t know where to begin”. Even here at Sales Engine, we—our corporate marketing team—depend on the same program managers who coordinate and run demand generation efforts for our clients. Furthermore, without our marketing automation platform (formerly Manticore Technology) we would not have the ability to track and obtain the data we need to make valuable decisions.
That is the hard truth about leveraging Big Data for marketing. It requires a significant change and upheaval to a traditionally intuitive program. “[Big Data] requires new tools, new skills and new ways of visualizing information,” writes Sharon Crost in her blog post, Unlocking marketing’s dark data: The Year of Big Data. “All this data needs to be crunched, and quickly.” With a growing need for the technology, skills and processes necessary to accommodate the invasion of data, marketing is getting seriously complicated. For those not willing or able to make the adjustment, competition is likely to surpass.
So, how do companies meet the demands of big data? Paul Rafferty, Founding Partner and CEO of Sales Engine International, provides his insight on this issue in his recent article, The Data that Binds. “More and more companies are recognizing the need for at least one marketer who approaches decisions from the perspective of a statistician,” Rafferty states. “If the existing budget does not provide for this type of talent, a review of resource allocation could be necessary.” Creativity and intuition alone can no longer suffice. Companies now require marketing efforts to prove value to the bottom line. To do that, marketers must be capable of using data to show a measurable process tracing marketing actions and resources all the way to revenue dollars. Big Data is at the core of that lead to revenue process. Companies must simply learn how to wield it.
Paul Rafferty, Founder and CEO of Sales Engine, was recently published in the January/ February edition of Texas CEO Magazine. View his entire article, The Data the Bind: Understanding the Intersection between the CMO and Big Data.
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[guide] Cloud Analytics for Dummies | @CloudExpo @Informatica #API #Cloud #Analytics #BusinessIntelligence
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