Read the articles linked above and then answer the following questions: https://
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Question
Read the articles linked above and then answer the following questions:
https://www.forbes.com/sites/onmarketing/2012/06/28/social-media-and-the-big-data-explosion/#1e244df26aa7
https://www.technologyreview.com/s/532551/driving-marketing-results-with-big-data/
Big Data is a relatively new term that corresponds with the wealth of information available in our technologically advanced world. Information can be found on almost any topic in real time. This has changed the way we do business forever.
1. How would having access to Big Data impact your market research?
2. Apply one of these concepts in this article: Predictive Analytics, Top-Down campaign planning, Bottom-Up campaign planning.
3.How does it relate to your research and how is it impacted by Big Data?
Explanation / Answer
How would having access to Big Data impact your market research?
Big Data vs. Marketing Research
Innovation and speed are keys to leveraging big data to create actionable, useful information from massive collections of data. While social media is one source of big data, other sources include logistics data, data from RFID tags, retail scanner data, and even data on things like weather and traffic patterns. The promise of big data is that it can integrate non-structured data collections from multiple sources, to combine analytics in new and innovative ways.
Big data concentrates on squeezing valid insight from massive, heterogeneous data collections.
Traditional market research, in contrast, focuses more on data collection. Before data was so abundant, market researchers had to concentrate on data collection, or they might not have enough information to lead to valuable insights. Big data takes the focus off collection and puts it on what is done with the data.
The market research of yesterday had trouble gathering data that was representative of markets. Focus groups and pen-and-paper surveys simply aren’t adequate to the marketing needs of today’s world of commerce, and they take too long to produce meaningful results that inspire confidence. Near real-time speed is necessary today, and old school market research methods simply aren’t relevant to many of today’s consumers.
Detail is another aspect of big data research that is advantageous. Searching for and identifying patterns in large, disparate data sets is what big data does, and it can do this even in the presence of data elements and trends that may appear to conflict. Researchers new to big data may be nonplussed by the idea of using data sets that appear to conflict, but it is the new normal that researchers must get used to, because contradictions are going to exist in data sets of the size that are analyzed today.
Apply one of these concepts in this article: Predictive Analytics, Top-Down campaign planning, Bottom-Up campaign planning.
In the digital world, predictive analytics based on big data holds the promise of creating a detailed view of what works, providing guidance that has never been available before for the fine tuning of advertising campaigns.
The promise of big data analytics is that marketers can analyze thousands of points of information about the digital activity of the purchaser—stripped of personally identifiable information—and combine it with their knowledge of television, radio, billboard, and print campaigns to tailor marketing messages and, ultimately, improve return on investment (ROI). With analysis, the numbers show how much lift each data point provided for each ad in each channel. With that data, marketers can make better decisions about how to allocate their ad budgets. Indeed, the analytics themselves will identify the smart choices.
A key challenge for any marketer is deciding what mix of media—TV, Internet, direct mail, radio, print—will best promote a product or service. “We can do media-mix modeling using big data and machine learning,” says Madan Bharadwaj, product marketing chief of Visual IQ, an analytics firm based in Needham, Massachusetts. “There are a lot of micro-efficiencies we can tap into. If you move a few thousand dollars here and there, you can get much more marketing efficiency,” in terms of ROI.
Historically, the most sophisticated marketers have relied on top-down ad campaign planning. They develop econometric models by looking at the distribution of the whole advertising budget. They analyze changes in allocation and one-time promotions and see how those changes affect their key performance indicators (KPIs), which may be making an in-store purchase, or opening a new account.
That paradigm is flipped in the digital world. Marketers rely on digital scoring of actions, starting from the bottom up with the KPI. “You try to work backwards to see the touch points along the consumers digital journey,” says Kim Riedell, senior vice president of product and marketing at Digilant, a customized programmatic media solutions company in Boston. Thanks to technologies such as cookies and browser pixels, marketers can now tell exactly where a specific buyer saw their ads. The data even shows how long that buyer watched a video or lingered on a page carrying the ad. It’s all found by backwards tracking from the point of sale of the product the person ultimately bought.
The world of perfect knowledge that was promised in the early days of digital advertising has proved illusory. Paying search engines for stimulating clicks that led to purchases was fine, but most consumers take a more circuitous route to their final decisions. The marketing funnel can be long, especially for big purchases such as automobiles, where people may do research for nine months before taking a test drive.
Advanced predictive analytics can now figure out what audiences have been most responsive to an ad. Then the same algorithms can find similar audiences on other websites and present the ads to them. With enough data, and a good algorithm, the analytics companies say they can determine just which ads made a difference.
Predictive analytics can’t incorporate everything. A favorable product review in Consumer Reports or a celebrity endorsement at the Oscars falls outside the algorithm. So does a plane crash that may hurt travel bookings. Sometimes, though, such events will cause a spike in discussion on social media, here they are monitored and even calculated into the equation.
How does it relate to your research and how is it impacted by Big Data?
Complex analytics tasks have become commonplace for a wide range of users. This paper specifically identified main applications which depends thoroughly on big data predictive analytics solutions and already adopts themselves as one of the big data entities. However, instead of targeting the use cases and computing resources of the typical user, existing analytics frameworks are designed primarily for working with huge datasets in various applications. Therefore the future implications will be based on pattern predictions and different evolutionary techniques from various data
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