Big Data Flashcards
Bens and Dubois
2014
SOCIAL MEDIA LISTENING
Marketing 2.0 “pull challenge”: identifying and analyzing quantitative and qualitative information from the internet
L’Oreal used it to understand the Ombre trend
Berger and Milkman
2012
Content that evokes high-arousal positive (awe) or negative (anger or anxiety) emotions is more viral. Content that evokes low-arousal, or deactivating, emotions (e.g., sadness) is less viral. To go viral, marketers need to evoke high-arousal emotions.
Boyd and Crawford definition of big data
2012
We define Big Data as a cultural, technological, and scholarly phenomenon that rests on the interplay of:
(1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets.
(2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims.
(3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.
Gartner definition of big data
Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization
Boyd and Crawford
2012
Claims to objectivity and accuracy are misleading (methodological issues).
Bigger data are not always better data
Just because it is accessible does not make it ethical
Limited access to Big Data creates new digital divides
Slater and Narver
1998
The customer-led business: focuses on understanding the expressed desires of customers.
The market-led business:
Committed to understanding the expressed and latent needs of their customers
Is a counter to Christensen and Bower 1996.
Big data allows us to discover latent needs
EXAMPLE?
Moral and ethical issues of big data
UBER EXAMPLE
Uber uses big data to track authroities, comeptitors, and drivers who were driving for both Lyft and Uber. Although competitive, how ethical?
Before big data, firms only had information on its most loyal customers. With big data, firms can have information on everyone
EXAMPLE?
Need an exampel of excellent prediction on low l
Spotify uses machine learning to understand music tastes. A new customer can instantly have music reccomended based on the music itself (content based reccs), despite being a new customer.
NEED MORE EXAMPLES
Keiningham et al.
2003
Net Promoter, like any measure of customer intentions, is inherently unreliable. Customer intentions may point in the right direction for some behaviors, but they will never provide all of the information needed to understand the complete picture.
Second, managers must be willing to do whatever level of analysis is required to understand their customers and their particular market opportunities.
Reichheld
2003
Introduces the NPS score, a 1-10 score for “How likely is it that you would recommend [company] to your friend?”|
In an industry, highest NPS have 2.5x growth rates.
Claims that everything needed to predict growth can be explained with NPS, and asserts that other survey-based mertics such as customer satisfaction have no link to growth
Keiningham et al.
2009
Contrary to Reichheld, says that NPS is not a good predictor of growth, and is inferior to customer satisfaction and previous spending.
Any measure of customer intentions (like NPS) is inherently unreliable.
Managers need to understand the entire picture, and do whatever level of analysis needed to do so.
Types of big data
Prescriptive: What actions should be taken
Predictive: What might happen. Uber demand forecasts.
Diagnostic: What happened and why. GA, assesing social media campaigns.
Descrptive: What is happening now. Twitter live reports: news using twitter to understand.
Volume
Doesn’t sample, takes observes and tracks everything.
Variety
Draws from text, images, audio, video, IOT sensors.
