Neuroscience, Big Data, and Data Science Is Impacting Big Ideas In The Creative World of Advertising

Moxie Group’s Creative Director Tina Chadwick makes the case that real-time data analytics “brings us tangible facts on how consumers actually react to almost anything.” She makes light of the “notion that 10 people in a room, who volunteered to be there because they got paid and fed,” could truly represent consumer behaviors (psychographics) is a thing of the past. Sadly though, for many advertising companies, this is still the mainstay of their advertising-oriented evaluative methodology.

New capabilities based on neuroscience, integrating machine learning with human intuition, and data science/big data is leading to a new creative processes, which many call NeuroMarketing, the direct measurement of consumer thoughts about advertising through neuroscience. The persuasive effects of an advertising campaign (psychographic response) are contingent upon the emotional alignment of the viewer (targeted demographic); that is, the campaigns buying call to action has a higher likelihood of succeeding when the viewer has a positive emotional response to the material. Through neuroscience we can not directly measure emotional alignment without inducing a Hawthorne Effect. 

This is new field of marketing research, founded in neuroscience, that studies consumers’ sensorimotor, cognitive, and affective response to marketing stimuli. It explores how consumer’s brain responses to ads (broadcast, print, digital) and measures how well and how often media engages the areas for attention/emotion/memory/and personal meaning – measures of emotional response. From data science-driven analyses, we can determine:

• The effectiveness of the ad to cause a marketing call to action (e.g., buy product, inform, etc) 
• Components of the ad that are most/least effective (Ad Component Analysis) – identifying what elements make an ad great or not so great.
• Effectiveness of a transcreation process (language and culture migration) used to create adverting in different culturally centric markets.

One of the best and most entertaining case studies I have seen for NeuroMarketing was done by Neuro-Insight, a leader in the application of neuroscience for marketing and advertising. Top Gear used their technology to evaluate which cars attract women to which type of men. The results are pretty amazing.

While NeuroMarketing is an emergent field for advertising creation and evaluation, the fundamentals of neuroscience and data science make this an essential transformational capability.  For any advertising agency looking to leap frog those older, less agile companies that are stilled anchored in the practices of the 70s, neuromarketing might be the worth looking into.

POSTED FROM: Data Science Insights: Exploring The Darkest Places On Earth

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FIELD NOTE: Big Data and the Power of Three

A common data exploration came up while talking with a British colleague in the advertising industry on Friday, how many independent subject areas should be investigated (1, 10, 100, …, N) in order to have a statistically significant chance of making a discovery with the least amount of effort? An answer can be found in “The Power of Three (3),” an application of the Knowledge Singularity when N is very small, which defines meaningful returns on knowledge discovery costs.

As I discussed in the field note “FIELD NOTE: What Makes Big Data Big – Some Mathematics Behind Its Quantification,” perfect insight can be gained asymptotically as one systematically approaches the Knowledge Singularity (77 independent subject areas out of a N-dimensional universe where N >> 77).  While this convergence on infinite knowledge (insight) is theoretically interesting, it is preceded by a more practical application when N is three (3); that is when one explores the combinatorial space of only three subjects.

Let Insight 1 (I_1) represent insights implicit in the set of data 1 (Ds_1), insight 2 (I_2) represent the insights implicit in the set of data 2 (Ds_2), where union of data sets 1 and 2 are null (Ds_1 U Ds_2 = {}).  Further, let insight N (I_N) represent the insights implicit in the set of data N (I_N), where union of data set N and all previous data sets are null (Ds_1 U Ds_2 U … U Ds_N = {}). The total insight implicit in all data sets, 1 through N, therefore, is proportional to the insights gained by exploring total combinations of all data sets (from to previous field note). That is, 

In order to compute a big data ROI, we need quantify the cost of knowledge discovery. Using current knowledge exploration techniques, the cost of discovering insights in any data set is proportional to the size of the data:

          Discovery Cost (I_N) = Knowledge Exploration[Size of Data Set N]

Therefore, a big data ROI could be measured by:

         Big Data ROI = Total Insights [Ds_1 … Ds_N] / Total Discovery Cost [Ds_1 … Ds_N]

if we assume the explored data sets to be equal in size (which generally is not the case, but does not matter for this analysis), then:

         Discovery Cost (I_1) = Discovery Cost (I_2) = Discovery Cost (I_N)

or

         Total Discovery Cost [Ds_1 U Ds_2 U… U Ds_N] = N x Discovery Cost [Ds] = Big O(N) or proportional to N, where Ds is any data size

Thus,

and

 We can now plot Big Data ROI as a function of N, for small values of N,

That was fun, but  so what? The single biggest ROI in knowledge discovery comes when insights are looked for in and across the very first two combined independent data sets. However, while the total knowledge gained exponentially increases for for each additional independent data set added, the return of investment asymptotically approaches a finite limit as N approaches infinity.  One can therefore reasonably argue, that given a limited discovery investment (budget), a minimum of two subjects is needed, while three ensure some level of sufficiency.

Take the advertising market (McCann, Tag, Goodby, etc.), for example. Significant insight development can be gained by exploring the necessary combination of enterprise data (campaign specific data) and social data (how the market reacts) – two independent subject areas. However, to gains some level of assurance, or sufficiency, the addition of one more data set such as IT data (click throughs, induce hits, etc.), increases the overall ROI without materially increasing the costs.

This combination of at least three independent data sets to ensure insightful sufficiency in what is being called “The Power of Three.” While a bit of a mathematical and statistical journey, this intuitively should make sense. Think about the benefits that come from combining subjects like Psychology, Marketing, and Computer Science. While any one or two is great, all three provide the basis for a compelling ability to cause consumer behavior, not just to report on it (computer science) or correlate around it (computer science and market science).

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Big Idea – How Big is Big Data?

A very good visual introduction to Big Data that combines a comprehensive technical and business description with fun animated graphics. They dispel the myth that big data is only about data that is big. In fact, we know that big data has three characteristics: Volume, Velocity, and Variety.

 

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Big Data – An Infographic Perspective

CSC is one of the pioneers in the rapidly growing field of big data.As most of us already know, “big data” is changing dramatically right before our eyes – from the amount of data being produced to the way in which it’s structured (or not) and used. One million time as much data is lost each day than is consumed.  This trend of big data growth presents enormous challenges, but it also presents incredible business opportunities (Monetization of Data). This big data growth infographic helps you visualize some of the latest trends.

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Data Science: Beyond Intuition – The Movie

Data science is changing the way we look at business, innovation and intuition. It challenges our subconscious decisions, helps us find patterns and empowers us to ask better questions. Hear from thought leaders at the forefront including Growth Science, IBM, Intel, Inside-BigData.com and the National Center for Supercomputing Applications. This video is an excellent source of information for those that have struggled trying to understanding data science and its value.

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Big Data Landscape – A New Look

Dave Feinleib, a Forbes Technology contributor, released a new Big Data landscape point of view. While not all encompassing (e.g. missing technologies like Pneuron), it is a great start of making the complicated big data landscape understandable.

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Social Media Network Connections Among Twitter Users

While doing social media analytics research, I came across this interesting analysis by Marc Smith (no relationship).

Using NodeXL as one of the graphing tools, the graph shows the connections among the Twitter users who recently tweeted the word bigdata when queried on February 27, 2012, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another. The green lines are “follows” relationships, blue lines are “reply” or “mentions” relationships.

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The Jury Is In – CEO Choose Big Data Over Social Media

A new study by McKinsey & Company reveals that less risky and potentially more beneficial realm of Big Data software is a higher priority today than social media integration. The study consisted of 1,500 surveyed CEOs, CFOs and CIOs between April 3 and April 12, 2012.

Almost 50% of respondents stated that they are currently using Big Data to “understand their customers better”, whereas 32% stated they are using social media for “interaction and promotion purposes.” The survey also found:

— 13% did not consider Big Data a priority, so far as stating it was “not on the agenda”
— Over 50% state that flexible delivery platforms are a priority for the next 1-2 business years
— 19% of respondents have deployed digital marketing practices across the enterprise
— 4% used location-based software to target customer promotions

The study also found:

— 52% believe that organizational structures not designed to take advantage of either Big Data or social media priorities 
— 51% say that lack of technology infrastructure and IT systems are a significant challenge
— 43% and 31% are having difficulty in finding functional and IT talent, respectively 

Big Data and social media do not ave to be mutually exclusive. A number of businesses are beginning to integrate the two, using Big Data solutions to analyze business content based on their social media activity.

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Big Data – Exploring the Darkest Places on Earth

A lot of folks know that I have done some interesting things in my life. From flying jets off aircraft carriers to running nuclear reactors on submarines  to building artificial brains in boxes in laboratories. All fun stuff, none of which I could have ever done without the help and support of very talented friends and team mates. So it doesn’t surprise me too much when someone asks me to talk about some of the scariest places I been. My response often confounds the asker in ways that can be profound. What is my response, you ask?

I have only one reply, “The scariest place I have ever been is THE darkest place on earth. It is the place of neither happiness or misery. Nor is it a place of neither rich or poor, or even right or wrong. This is a place where nothing exists but the absence of nothing. It is that place you find yourself in where you begin to realize just how much you don’t know you don’t know.”

Usually at about this point I see in their face the “oh man, I shouldn’t have ask” kind of look. You know the one I am talking about. It is that one that people give when they realize they want to be some place else, but need to stay and listen out of respect. That’s the look.

Being a nice guy I usually let them off the “listening hook” by saying they can alway stop by later to talk about this in more detail if they want. But, before they go, I add that, “my death opened a door to this dark place just once and that is a story worth telling.” After that their faces change from “I need to get out” to “I want to know more.” From which we talk about the philosophy of death’s role in helping us all better understand our true limits.

So, why all this pontification around death, not knowing what we don’t know, life, the universe, and everything else? Because we are on the technological brink of being capable of systematically exploring this deep dark space in ways that where never possible just a few years ago. We are capable of discovering new endless opportunities by just looking through all that data (FaceBook, Twitter, CRM, etc.) we take for granted everyday. This is the world of Big Data and these profound outcomes are why this new capability is important to us all.

Ok, enough philosophy, for now. Back to the practical, technical, and business aspect of big data 🙂

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A Few Interesting Ways Big Data Is Being Used

Here are five interesting uses of big data that are happening every day:

1. Google now studies the timing and location of search-engine queries to predict flu outbreaks and unemployment trends before official government statistics come out. Interesting.

2. Credit card companies routinely pore over vast quantities of census, financial and personal information to try to detect fraud and identify consumer purchasing trends. While not new, the big data approach is improving accuracy and precision, as well as speeding up prediction times.

3. Medical researchers sift through the health records of thousands of people to try to identify useful correlations between medical treatments and health outcomes. I wonder if the healthcare insurance industry is taking advantage of this?

4. Companies running social-networking websites conduct “data mining” studies on huge stores of personal information in attempts to identify subtle consumer preferences and craft better marketing strategies. This is a subset of the Target case study.

5. A new class of “geo-location” data is emerging that lets companies analyze mobile device data to make intriguing inferences about people’s lives and the economy. It turns out, for example, that the length of time that consumers are willing to travel to shopping malls—data gathered from tracking the location of people’s cell phones—is an excellent proxy for measuring consumer demand in the economy.

These applications do beg the question about privacy, “When does now-casting – search through massive amounts of data to predict individual behavior – violate personal privacy?”

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