Marketers Must Mine Big Data’s Predictive Powers For Success in 2013
Many a marketer has salivated at Big Data’s promise of unprecedented insight on behavioral and sentiment trends gleaned from digital and social media. This situational awareness would help marketers shape content to captivate consumers, track new opportunities, monitor competitors and understand new markets like never before.
Unfortunately, the technology and techniques leveraging Big Data typically have been backward-looking and therefore reactive, rather than forward-thinking and actionable. Big Data typically has focused on descriptive analytics, looking at past performance to understand the reasons and factors behind past success or failure. While multidimensional statistical analysis of historical data and customer segmentation is valuable, these approaches do not take full advantage of Big Data’s power for marketers, who need to influence behavior in the here and now.
Similarly, Big Data still remains largely trapped in siloes, limiting its effective use across channels. Despite the promise of better cross-channel pollination, in practice, data from one channel seldom informs better decisions in another. Paid search data typically is confined to paid search decisions, while data gleaned from display and social are similarly confined to their respective channels. As a result, cross-channel impact continues to be argued largely on instinct. And marketers continue to fly somewhat blind when it comes to forecasting the performance of a given media mix versus another and understanding the optimal channel mix to meet business objectives.
To take full advantage of Big Data in 2013, marketers must leverage technology wielding predictive analytics, data mining techniques and algorithms that can actually help predict future outcomes. They must employ solutions that embrace data from multiple channels and sources and facilitate cross-pollination between these myriad sources. This use of Big Data will help solve the complex pricing and placement issues surrounding everything from paid search to display advertising.
Digital marketing has never been more complicated and requires that marketers change their approach to programs and the technology used to support them. First and foremost, marketers must understand the shift from an attention-based economy to an intention-based economy to effectively market to consumers in 2013. Customers today are accustomed to receiving tailored content, such as personalized deals and targeted advertisements, which raises the bar for maintaining customer engagement and loyalty. More than ever, customers will ignore all but the most relevant-to-them messages — messages that get to the heart of what they are looking for, perhaps even before they are aware of it. This dynamic demonstrates the shift from an economy based on vying for attention to one based instead on understanding customer intention. To effectively mine this intention-based economy requires applying predictive analytics to Big Data for better marketing practices and decisions.
Fortunately, unlike ever before, marketers can arm themselves with the technology to do precisely that. Moving into 2013, solutions using predictive modeling will play a leading role in program optimization, using Big Data to forecast customer and marketplace behavior. Unlike backward-looking forms of Big Data analysis and applications, predictive analytics enable marketers to anticipate customer behavior and serve up the right products, services or offers at the right time. Instead of analyzing “what happened,” predictive analytics arm marketers with the ammunition to drive future successes by optimizing individual campaigns and their overall media mix; adjusting marketing offers quickly to take advantage of changes in consumer behavior; and getting the optimal price and placement for ads in an ever-changing marketplace. With predictive analytics in their toolkits, marketers will learn from existing customers how to best acquire new ones. They will deliver relevant recommendations and discounts at the right time to the customer that is ready to spend.
To fully realize the predictive promise of Big Data, it must be deployed on the front lines, not just the C-suite; the everyday decision-making critical to advertising, in particular, demands this. It is the only way for advertisers to stay abreast and even ahead of an ever-more dynamic and competitive marketplace. Predictive analytics on the front lines assist marketers with high-impact decisions such as pricing, helping to ensure not only that they are advertising at the right time, but also that they are paying the right price to maximize their return and overall financial performance. Take a specialty retailer whose highly seasonal business means wild swings in costs and conversions depending on the time of year. Using predictive analytics, this type of retailer is able not only to maximize financial results during peak season, but also capture otherwise-missed sales opportunities in the off-season. Increasingly, high-impact decisions such as these can and must also be informed by a data-driven understanding of cross-channel impacts — for example, the value of a particular display impression on paid search results.
In 2013, digital marketers will turn to solutions leveraging predictive analytics to solve complex issues across digital marketing. Only when predictive analytics are applied to Big Data will digital marketers understand and stay ahead of the marketplace. Fortunately for marketers, there are now solutions available using predictive analytics to help make the better decisions described above. For this reason, 2013 will be the year when Big Data delivers on its promise and data starts driving real-time decisions for digital campaigns.
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