Digital Marketing Magazine

The Better the Question, the Better the Answer

mickyates Big Data, Business, Customer, Digital, Ideas, Innovation, Leader, Leadership, Social Media, Strategy Leave a Comment

Was pleased to be featured in Digital Marketing Magazine this past week. Here is the article I wrote:

The way in which customers interact with products and services is changing.

Businesses can no longer simply “push” their products onto customers. Now the individual has the information and the autonomy to discriminate against the marketing strategies directed towards them. Customers “pull” products and services towards them – to suit their exact needs, preference and timings. In essence, customers increasingly expect personalisation in return for their personal data. They don’t want mass branding and marketing.

Technology, Big Data and Social Media are fuelling both this customer knowledge and also business’ ability to personalise quickly at scale. Organisations are being urged to invest in Big Data strategies, in an effort to remain one step ahead of their customers and ultimately to survive in the increasingly competitive marketplace.

However, understanding Big Data insight and how to use it effectively is a challenge that many businesses grapple with.

It is estimated that only 18 per cent of data that’s available to businesses is effectively utilised. Despite the constant rhetoric surrounding Big Data, the reality is that there is little practical advice available to help organisations navigate this unknown raw asset – and to decide what data is important and what isn’t.

The main obstacle that many firms face is not knowing how to turn data-driven insight into effective action, or how to embed this across all levels of the business. At the same time, few business leaders have sufficient understanding of data insight to allow them to implement the changes required throughout their organisation.

What’s needed is a fundamental shift in approach. Too many firms make the mistake of starting with the data; the real power comes from starting with a strategic question that needs answering. What problem are we trying to solve, and how can data insight and processes help? Then, if you don’t have the right data to solve the problem, how do you get it?

For marketers, they should ask the question: ‘How can I better understand and serve my customer?’ and then they must build an appropriate digital solution and communication strategy. Figuring out the right question is half the battle won.

Furthermore, in order to stand out from the crowded marketplace, brands and businesses would do well to adopt a forward-thinking strategy. What are the emerging aspirations, trends and influences for each customer? Today, such work is done en masse, with very little granularity.

Traditional marketing is based on a rear-view mirror approach, focused on what consumers have already done, or bought. Take Amazon, for example, which makes customer recommendations based on what has already been purchased. This can work in high volume retail, but is less helpful in “capital items”, luxury, fashion or innovative new products and services.

The real winners will be the businesses that try to work out what customers might do and aspire to, rather than focus their efforts on customers’ past actions.

We call this the ‘Did-ness’ and ‘Do-ness’ approach, combining what customers actually did, with what they are most likely to do. This provides a powerful launch-pad from which to build an effective and future-proof marketing strategy.

In today’s world, it is critical that businesses know what their customers will do, because ultimately, it is their aspirations, passions and motivations that will lead to purchases.

Knowing what your customers are going to want and need – not only from you but from your competitors – has the power to transform your business.

By Mick Yates, Director of Strategy and Partnerships at Starcount.

Social Science

On minorities and outliers: The case for making Big Data small – Brooke Foucault Welles

mickyates Academic, Big Data, Society Leave a Comment

In this essay, I make the case for choosing to examine small subsets of Big Data datasets—making big data small. Big Data allows us to produce summaries of human behavior at a scale never before possible. But in the push to produce these summaries, we risk losing sight of a secondary but equally important advantage of Big Data—the plentiful representation of minorities. Women, minorities and statistical outliers have historically been omitted from the scientific record, with problematic consequences. Big Data affords the opportunity to remedy those omissions. However, to do so, Big Data researchers must choose to examine very small subsets of otherwise large datasets. I encourage researchers to embrace an ethical, empirical and epistemological stance on Big Data that includes minorities and outliers as reference categories, rather than the exceptions to statistical norms.

Twenty thousand users, one hundred thousand users, ten million users. In the world of computational social science, Big Data has provoked an analytic arms race to work with more data, better data, bigger data in pursuit of discovering so-called truths about the social world. At meetings, it is not uncommon for computational social scientists to boast about the size of their datasets, as if millions of users are universally and self-evidently better than thousands or hundreds. Like many assistant professors, I struggle with the “imposter phenomenon,”—a feeling that my intellectual and technical skills do not quite measure up to those of my peers (Clance and Imes, 1978). So, it can be hard to suppress the anxiety that I feel when those questions come my way. “How big is your dataset?” they ask. “1500,” I say, “no bigger than a modest survey, but different in an important way.”

I study women—most recently, older women who play online games with such intensity that they distinguish themselves not only from their gender- and age-mates in the offline world but also from their game-playing peers in the online world as well. I have long been interested in how women’s lives shape and are shaped by technology, so when I began working with online game datasets in graduate school, it seemed natural to me to focus on women’s experiences. Like a growing number of my colleagues in Computational Social Science (Lazer et al., 2009), I am motivated by theoretical questions, and Big Data is the tool to answer those questions. In my case, against a backdrop of discussions about sexism and misogyny in online gaming (Fox and Tang, 2013), Big Data from online games promises to reveal patterns of behavior that can help women resist gendered aggression in male-dominated gaming communities and on the internet more broadly.

However, a large dataset quickly becomes small when you focus on a minority population. In my dataset of 10 million players from the virtual world Second Life, about a third are women. Of those, only one in 20 is over the age of 50. Among those, just the tiniest statistical minority—1%—has played for 1000 hours or more. So, what started as a dataset of 10 million players is reduced to just 1500 players with novel characteristics. This extreme minority would normally get lost in Big Data analytics, wiped away as noise among the statistically average masses. Some Big Data researchers might abandon projects that whittle datasets down so substantially, believing that focusing on 1500 players no longer “counts” as Big Data research. However, I argue that honoring the experiences of extreme statistical minorities represents one of Big Data’s most exciting scientific possibilities.

Choosing to work with a small sample drawn from Big Data represents an important empirical stance for Computational Social Science and Big Data analytics. Scholars have long critiqued the omission of women and minorities from the scientific literature. Even the most methodologically and epistemologically conservative of these critiques speaks to the challenges of using majority samples to generalize about minority experiences (Keller, 1995). When women and minorities are excluded as subjects of basic social science research, there is a tendency to identify majority experiences as “normal,” and discuss minority experiences in terms of how they deviate from those norms (Gilligan, 1982). In doing so, women, minorities, and the statistically underrepresented are problematically written into the margins of social science, discussed only in terms of their differences, or else excluded altogether (Smith, 1974).

Historically, in the pre-computational era, researchers may have made the case that it was simply too difficult to work with underrepresented populations and statistical outliers. These people are, by definition, less plentiful in the population. So, they can be harder to find, more expensive to recruit, and more time-consuming to work with. Although ethically and empirically inexcusable, researchers working with tight budgets and limited time frames may have felt that it was not viable to work with non-majority populations. However, Big Data changes all of that. In our datasets of millions, the minorities and statistical outliers are just as easy to access as the majorities and statistically average. We simply have to choose to look.

The reasons to make that choice are numerous. Ethically, we have a responsibility to include a diverse range of participants in our work so that the benefits of our scientific practice can be as widely applicable as possible. Empirically, focusing on minority experiences as reference categories, rather than as deviations from the majority reference, enables better, more accurate theory building and data modeling (Gilligan, 1982). And, epistemologically, choosing small foci within Big Data dismantles the problematic ethos emerging within computational social science and Big Data analytics of bigger data being “truer” data (Boyd and Crawford, 2012). Big Data are neither inherently true nor inherently comprehensive, but they do contain clues about populations long-overlooked in the social sciences.

As we enter a new age of Big Data-driven computational social science, we are poised to either replicate or remediate the mistakes of the past. One of the greatest advantages of Big Data in computational social science research is the breadth of experiences that it represents. Big Data allows us to produce summaries of human behavior at a scale never before possible. But in the push to produce these summaries, we risk losing sight of a secondary but equally important advantage of Big Data—the plentiful representation of minorities. Those who might otherwise be represented as a single outlier in a more traditional dataset can number hundreds or thousands in a Big Data dataset—hundreds or thousands whose experiences are currently absent from the scientific record. Rather than actively removing these voices through sampling and data cleaning, or passively silencing them through statistical aggregation, I choose to embrace the opportunity to examine the statistical outliers, and I encourage my colleagues to do the same.

By choosing to make Big Data small, we can rectify historical omissions and biases in social science research and build better, more comprehensive, bigger understandings of human behavior.

Read the full paper, and download the PDF from the SAGE Journals website

Brooke Foucault WellesAssistant Professor of Communication Studies, Northeastern University

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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