- Paperback: 224 pages
- Publisher: Kogan Page; 1 edition (March 28, 2015)
- Language: English
- ISBN-10: 9780749472115
- ISBN-13: 978-0749472115
- ASIN: 0749472111
- Product Dimensions: 6.1 x 0.5 x 9.2 inches
- Shipping Weight: 11.4 ounces (View shipping rates and policies)
- Average Customer Review: 7 customer reviews
- Amazon Best Sellers Rank: #845,830 in Books (See Top 100 in Books)
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Humanizing Big Data: Marketing at the Meeting of Data, Social Science and Consumer Insight Paperback – March 28, 2015
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"Manages to take the reader gently by the hand and give them an informed introduction to the world of big data, showing them how it can be utilized in business through a marketing-led perspective. Despite this being a very complex, inter-connected subject, this is a fairly light, open and jargon-free read. A pleasurable, thought-provoking book." (Darren Ingram Darren Ingram Media)
“[A] practical guide to the new marketing opportunities created by big data - but also to its perils, pitfalls and limitations. Strong is an experienced and accomplished consumer researcher able to steer a path between the techies’ hype and the skeptics’ underestimation of the potential. He aims to 'humanize' big data by showing how analytics need to be combined with marketers’ understanding of customers as real people as well as with an implicit or explicit conceptual framework, ideally grounded in solid behavioral insights.” (Patrick Barwise, Professor of Management and Marketing London Business School)
"In this brave new world, companies are pulled in two directions - do they make money from the vast quantities of data they are able to gain from interactions with their customers, or focus on preserving the intangible benefits of goodwill and positive brand orientation by respecting customers' autonomy and privacy preferences? Colin Strong's Humanizing Big Data marches into this minefield with cogent analysis and thoughtful advice for management and marketing about the new breed of information-aware consumer." (Dr. Kieron O'Hara, Senior Research Fellow University of Southampton)
"[I]nvites readers to approach collecting Big Data with a more human-centered approach. ...The principles behind Humanizing Big Data can be implemented by a business of any size. After all, Big Data is everywhere: online, on social media, in CRMs, in email newsletter services, etc. The key is understanding how to use data that you have instead of the technology that you don’t." (Charles Franklin Small Business Trends)
About the Author
Colin Strong is a leading consumer researcher who has worked with a wide range of global brands to help shape their consumer strategies. He uses consumer data to drive insight that was once the preserve of surveys, and to advise on ways to shape new consumer brand relationships. Behavioral science runs throughout his research practice, not only to design experimental approaches, but also to guide data analytics. Currently, he is Managing Director of Verve Ventures, a research consulting firm. He is also a regular speaker at conferences and a contributor to publications including The Huffington Post, Wired, Medialine, AdMap and Market Leader.
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p.s. Below please find some favorite passages of mine for your reference.
Our ancestors are associated with caves because the data still exists, not because they necessarily lived most of their lives in caves. The data held by mobile network operators is a reflection of this.....In essence, the "Caveman Effect" is the big data equivalent of the survey’s questionnaire bias. Size is not everything, nor does it mean we get better cut through into the truth. It is not an objective process. There are decisions to be made and those decisions will introduce bias. All approaches have bias. It’s unavoidable. The challenge is identifying the nature of this bias and then either correcting it or allowing for it in the interpretation of the data. Pg24
Total objectivity can, at times, be illusory. There are always trade-offs to be made when conducting research. It is less about collecting data that has no bias but understanding which biases you are willing to accept in the data. Pg27
But just because this data is available, there is little guidance on: which metric to track; what you need to be measuring; what the landscape looks like; where to start; how to order your thinking. The actual task of selecting your metrics is thus anything but straightforward….By ignoring the reasons why we collect these statistics, misunderstanding the context, or not figuring out what questions we want answered, metrics can often prove meaningless. This propensity to measure the wrong things has become even more of an issue with the advent of web analytics. Pg30
Scorecards monitor the progress towards accomplishing strategic objectives. It offers regular snapshots of performance associated with an organization’s strategic objective and plans. There are two main characteristics. Each KPI has to have a predefined target measure. They should include project based measures such as milestones, progress towards completion and degree of planned vs accomplished outcomes, as well as metrics such as customer satisfaction and delivery. Dashboards monitor and measure processes and outputs. It is operational and reports information typically more frequently than scorecards. Like a car’s dashboard, which let drivers check their current speed, fuel level and engine temperature at a glance, they offer more real time metrics but say little about he overall direction. – Gary Cokins Pg42
The recipient of any story should realize that this is an interpretation of the data and should remain open to other perspectives…….how are they framing the issue? Are there implicit assumptions running through it that we should be questioning? What have they decided is relevant and irrelevant? pg51
We will be attracted to stories that reference a framework of thinking we are familiar with even when more complex interpretations have greater explanatory value. The more readily something is brought to mind the more likely it is to influence us. So we worry more about dying in a plane crash than a car accident, despite the odds, simply because plane accidents are report more often. We are all aware of the power of an anecdote from personal experience or the recounting of a tale from a market research focus group. So, again, more colorful stories can win out at the expense of the more worthy but possibly more valid tale. In the process of simply listening to the story we are being primed to see the world in a different way. So, to give a simplistic example, a person who sees the word “yellow” will recognize the word “banana” more quickly. The lasting legacy of a tale being told well is that we can then tend to only seek out information that is consistent with the story. This “confirmation bias” means that we seek out data that confirms, rather than tests, our interpretation. And when we get new information we tend to interpret it in a way that is self-serving. So, we will often fix our stories alarmingly quickly. Pg51-2
We try to make sense of all the data around us because there are costs attached to information storage. ...We will try to attribute causality to events so that we can explain and understand, rather than leaving us to deal with the complexity and randomness of the world. And the purpose of imposing a narrative is that it can generate a sense of chronology, so both move in a single direction….But our tendency is not to think of ourselves operating in this way. We tend to think that we are much more objective and rational beings. Pg52
Raw data is an oxymoron. Analytics necessarily involves making decisions; about which data to look at, what composite variables to generate, what constitutes an outlier, and so on. These decisions involve human judgment, often well intentioned, but guided by assumptions or hypotheses concerning what is important and why. The point is that the data does not speak for itself, as Nate Silver says: We speak for them. We imbue them with meaning. So as such we cannot avoid theories, it’s just that much of the time they are implicit rather than explicit theories of human behavior which are driving our analysis behaviors. But nevertheless theoretical frameworks are driving our approaches. Pg55
Aristotle created the idea of deductive reasoning, that is, developing a hypothesis and then testing it….Francis Bacon argued that true scientific knowledge should instead be based on collecting facts and then drawing conclusions; inductive reasoning. That is, science could discover truths about nature only by empirical testing of all possible explanations for all observed phenomena. . Pg55
Humans are wired for inertia, with our cognitive rules of thumb, the means by which we effectively navigate the world, focusing us on short term, loss averse behaviors. This does not always work in our favor, particularly when the benefits operate over the longer term and are less salient than the shorter term task. E.g. include: “Hyperbolic discounting”. This is the tendency to overemphasize short term costs over long term benefits. “Regret aversion”. The concern is not only with what they have but how it compares to what they might have had. “Social effects”. “Choice overload”. Pg170
This book manages to take the reader gently by the hand and give them an informed introduction to the world of big data, showing them how it can be utilised in business through a marketing-led perspective. Of course, not every company will generate or have a need for big data, yet for those who do it can be a goldmine of opportunities and potential.
The amount of data being generated can be astounding - especially if a company utilises externally produced data sources – and it can feel as one is drowning in a sea of bits and bytes. The author helps identify possible situations where big data sources can be exploited for marketing and customer relationship management purposes, although the harder task of implementation is left to the reader.
Naturally important issues concerning privacy, data protection and industry best practice are also considered so you hopefully won’t shoot yourself in the foot with your first foray into big data exploitation. In some ways big data might even be a bit of a leveller for companies, allowing the smaller and more agile player to gain market share through their use of big data and intelligent analysis. You just have to be innovative in thought, approach and your use of data-led activities.
Big data does not just refer to the physical amount of data being gathered; it also can relate to the speed it is generated, the range of data being collected and its scope. A lot of the benefits can be realised by sifting through an often fine-grained, relational and flexible series of data for the special nuggets of information that possibly nobody else has yet found to tailor-make your marketing and sales propositions. It might not be a licence to print money, yet it can provide an intelligent, data-led approach to servicing existing customer relationships and attracting new customers without recourse to the old-fashioned shotgun-style approach.
Yet there is resistance, as the author notes: “There is a huge opportunity for brands to make use of big data but it requires a change of mindset. There are many vested interests that have talked about the potential of big data but in a way that maintains a simplistic approach to consumer understanding: allowing the data to ‘speak for itself' rather than thinking about what it means; accepting reductionist views of human behaviour rather than recognizing that a higher-level order of explanation is often needed; using a range of data-derived metrics simply because you can, not because they mean anything; implementing customer management programmes that are efficient because they are data-mediated but not considering the impact on the brand.”
This is a very open, clear book that gives you a lot to think about, even if you have no specific plans or needs to exploit big data. After reading it, you probably will be thinking differently if you had not already been sold on the idea! Looking at the methodologies and structures behind big data usage can still yield benefits in “little data” or “no data” environments. Like panning for gold, you still need to shake the tray and put some effort in…
One good example of the disadvantages of data mining given is relevant in every business situation. Just because the information given says X, it doesn’t mean you have to act on it. The author recounts a business class flight between two European capitals where insufficient catering was loaded so someone had to go hungry. The staff blindly took the data-led approach and decided that the traveller with the lesser status (frequent flyer miles) should receive a downgraded service: a pregnant lady who was gallantly offered the meal of a fellow traveller (with much higher status) when he discovered this brilliant example of customer service, all based on data!
“It is clear that one of the key challenges is for brands to take an intelligent approach to the way in which they critically examine their data assets. I don't think that many organizations have yet properly adjusted their processes for big data sets – a more coherent approach is often required. Once a brand has processes in place that undertake the ‘due diligence' on the way in which data assets are being handled, we can focus on the actions required to drive insight from the data,” notes the author.
Despite this being a very complex, inter-connected subject, this is a fairly light, open and jargon-free read. A pleasurable, thought-provoking book that might be a little shocking for those companies that need a bash around the corporate head with a “clue stick.”