Humanizing Big Data: The Smart Guide to Tracking Customers


Humanizing Big Data invites readers to approach collecting Big Data with a more human-centered approach.

Humanizing Big Data

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Data has always been an important part of running a business. It helps managers and owners determine how to use limited resources (time, investment, etc.) to make profits.

What hasn’t always been a big part of business is Big Data, the incredibly large and complex data arising from human behavior (online and off). There has never been any point in history where owners had access to so much information about the consumer. Business owners can tell within days, weeks, or even minutes (in some cases seconds) whether their messages are making an impression or not. They can analyze comments in real-time about their brand and make preparations for future trends.

But are businesses making the proper use of all this data?

If you are a big business owner or manager, the answer is increasingly, yes. Companies are spending big bucks on tracking their customer’s every move from the time they see a product to purchase — and after. This has paid off exceedingly well for companies like Apple, Amazon and Google who have the capital to go even further.

These companies have the ability to invest in facial recognition technology, drones, “blinkwashing” (Virgin Mobile’s advertisement that changes when a person blinks) or facial movement recognition (Nike’s “Free Face”). These achievements are just the tip of the iceberg as companies develop more skill.

But where does this leave the small business?

A Customer is More Than a Data Point

Colin Strong, marketing researcher and author of “”Humanizing Big Data: Marketing at the Meeting of Data, Social Science, and Consumer Insight,” argues that any size business can use Big Data for better business decisions if they focus on the often neglected source of all this information, individual human beings. Humans and human behavior are the engine behind Big Data. And, of course, understanding humans and human behavior has always been a necessary part of succeeding in small business.

“Humanizing Big Data” contends that every business recognizes the power of collecting and learning from data. But Strong insists the problem has to do with where some businesses focus when getting this information. More and more, businesses may be exclusively focusing on technology to bring in customers only to forget the customer in the process. But placing too much emphasis on technology without considering its impact on human behavior can have implications that affect the bottom line of a business now and in the future, Strong says.

In other words, humans are more than a collection of clicks, Likes, mentions and Pins.

Choose the Right Ruler to Measure Your Marketing

In “Humanizing Big Data”, Strong suggests that companies using (or considering) Big Data should take a step back and consider the planning and implications of doing so.

Many businesses budget from their metrics, meaning they look at their Facebook Shares or retweets when choosing a marketing direction.

Strong says this is taking the wrong approach. Instead, businesses should look deeper into the metrics that affect their bottom line. Are they getting new customers, engaging current customers, and resolving angry customers’ complaints?

Choosing metrics that really matter (sales, volume, etc.) helps you focus your marketing on the right objectives.

The right metrics can help you correctly measure your business’s progress and where you’re headed. The wrong metrics can lead you to focus your marketing and other efforts in the right direction. It all begins with what you measure and how you choose to measure it.

Consider the Context, Not Just the Data

Strong is quick to point out that measuring data alone will not provide answers for your business or tell you which direction to head next.

Instead, business owners need to add context and understanding to the numbers that make up Big Data. He invites readers to consider the humans behind these numbers. Consider several qualifiers when looking at your data, such as “herd mentality.” For example, humans consider purchase decisions their friends have made when shopping, consult social networks, and consider the “trust factor.” In short, people buy with their emotions not simply their brains.

Strong also invites readers to consider their customers when implementing Big Data solutions.

For example, companies large and small should reflect on the potential negative implications of collecting Big Data. How might customers react depending how much data you collect. How far down the data collection path can you go before it becomes a liability?

Can “Humanizing Big Data” Work For All Businesses?

Strong seems to suggest his recommendations in “Humanizing Big Data” can work for all businesses. While most of his book focuses on big-name companies, he argues that any business can benefit from the ideas found there. His suggestion for creating more comprehensive reporting of business and consumer data or his discussion of “framing” marketing decisions cost little to implement.

So 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.

About the Author

Colin Strong is a market researcher, writer, and UK business executive. He can be found at his website and on Twitter (@colinstrong). “Humanizing Big Data” will be available on Amazon on March 28, 2015. This review was based on an electronic copy provided for review purposes.

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Charles Franklin Charles Franklin is a Book Reviewer for Small Business Trends. He has a background as a professional reviewer, and is also a content provider and customer relations professional.

4 Reactions
  1. There is no Big Data. I discovered that Language has its own Internal parsing, indexing and statistics. For instance, there are two sentences:
    a) ‘Fire!’
    b) ‘In this amazing city of Rome some people sometimes may cry in agony: ‘Fire!’’
    Evidently, that the phrase ‘Fire!’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrase weights: the first has 1, the second –0.12; the greater weight signifies stronger emotional ‘acuteness’.
    First you need to parse obtaining phrases from clauses, for sentences and paragraphs. Next, you calculate Internal statistics, weights; where the weight refers to the frequency that a context phrase occurs in relation to other context phrases.
    After that, you index each word from each phrase by dictionary, annotate it by subtexts.

  2. I guess the weakness of data is that it generalizes the needs of the customers when each of them are indeed different from each other. I guess it is more of a quantitative vs. qualitative battle.

    • Charles Franklin

      That is definitely a big issue! Humans are more complicated and complex than a simple click! On the other hand, clicks do provide clues as to what those humans are thinking.

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