Is your brand getting any value out of Big Data?

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By: Wise Marketer Staff |

Posted on June 16, 2014

Is your brand getting any value out of Big Data?

Of all the data currently created by mankind, it has been estimated that 98% of it is now held digitally, and 90% of it is less than two years old - which might help explain why so many businesses, brands, analysts and marketers are struggling to capitalise on and use this 'Big Data' in a way that adds value both for the business and for the customer, according to Adam Lee, data and CRM consultant for Amaze.

In this context, the term 'Big Data' simply refers to the practice of finding correlations in massive data collections and using them to make informed business decisions. Though still in its infancy, and not yet widely adopted, the business benefits will be huge - for example, allowing businesses to respond to consumers in real-time and personalising offers based on their behaviour patterns, stock levels, purchasing history, and so on.

It's partly a trust issue Google's driverless car technology has generated huge media coverage over the recent weeks. One of the by-products of this coverage has been a single question, often repeated: "Is this just another way for Google to track me?"

That statement itself tells us a lot about how, if not in name then in general awareness, the concept of Big Data has become mainstream. But at the moment, it hasn't brought with it a real understanding of the capabilities, limitations and necessary discipline that is required for effective use, both within business and within society.

Big Data for the Greater Good? The UK's recent NHS scare around 'Open Data' highlighted both sides of the Big Data debate. First, the advantages: As with medical meta-analysis (effectively, an analysis of lots of analyses - finding overarching patterns or proofs) lots of clinically meaningful data aggregated into one place could support powerful treatment breakthroughs or highlight emergent health or disease trends ahead of time, saving lives and improving welfare. At least, that is the hope.

As a generalisation, this is true of most Big Data and Open Data projects - at its heart these projects emphasise the taking of existing, siloed or unconnected knowledge, blending that knowledge together and as a result being able to improve and personalise a commercial provision or social service.

This has led to the rise in initiatives such as Smart Citizen, where tech-savvy citizens across the globe are monitoring their environment to "share instantly and compare with other places in a city, in real time".

How can this information help to improve environment quality? From a commercial perspective, customers are more likely than ever before to use this easy access of data to deal-hunt. "Showrooming" is now a recognised (although slowing) threat in the bricks and mortar environment.

The opposing argument, however, is that there is a failure by experts to appreciate fears that people have around this approach to data and knowledge, built on the cynicism and anxiety of the Big Brother culture that has grown significantly over the past decade.

The immediate backlash against the NHS was a distrust over genuine anonymisation of data, and a fear that, somewhere in the near future, the data will be used to the detriment of those in the database (initial concerns were focused around a perceived negative impact on health insurance premiums, for instance).

Historically, businesses have failed to deliver when it comes to data security and management. This one example outlines the challenges data practitioners face in the Big Data era: providing the benefit of "bigger data" but mitigating fears of your audience or participants.

Big Data principles in practice Fundamentally, to translate a Big Data concept from theory to reality requires lots of data and lightning fast management and processing infrastructure. As a result, one area where Big Data is beginning to set itself apart is the integration with, and delivery of, real time communications.

Proximity marketing has already begun to be implemented by huge retail and leisure brands in the US. Major League Baseball teams have begun to implement technologies that provide "point of interest information, concessions, shopping, and loyalty and rewards programmes, in addition to mapping services to guide people around large stadiums." They are looking to take advantage of location information, buying histories, ticket types, and perhaps even historic individual fan site behaviour to push personalised messages and offers to individuals in the stadium.

Apple launched its HomeKit at the recent WorldWide Developers Conference, functionality that begins to bring the theory of the "Internet of Things" to life. It may start with the basics, such as remote door locks and light controls, but underpinning this will be certification and API connectivity that will start to be baked into washing machines, ovens, fridge-freezers, heating systems - any service device in the home.

With this capability comes data. Consumers can currently sign in to websites with Facebook, and as a result see recommendations based on products friends may have bought in the past. Soon, people may be logging in to complete online grocery shopping and see recommendations based explicitly on what is currently in the fridge and, if they're like mine, probably well past the use-by date.

But there is a word of warning about the pervasive use of Big Data to influence peoples' lives: it is a fine line that marketers have to walk when using Big Data to generate commercial gains.

The technology now exists for brands to influence or support a customer's journey across every platform that they will be searching. If businesses provide the expectation that they are an always-on, high-tech, dynamic, personalised retailer, they need to make sure that they are being intelligent when using this insight. If a business is slow to respond to complaints, or makes errors in the delivery process, then be careful - if the bar is set high, then customers can be much quicker to complain.

Easy to do, but hard to do well Cloud computing, parallel processing, machine learning and smart algorithms aren't really much use if, a business does not really understand its best customers, what they buy and how frequently. A very simple preparedness questionnaire could look something like this:

On a scale of 1 to 5, where 1 is "easily" and 5 is "with great difficulty", how able is your business to support the following:

  1. Understand and enable data capture across all channels;  
  2. Understand and maintain data hygiene, quality and enrichment across all channels;  
  3. Use data to support strategic internal business decisions and performance evaluation;  
  4. Use data to support strategic customer insights (customer segmentation, campaign analysis, communications planning).

If any company answers '1' to all four questions, it is probably Amazon! And that means it's ready for the final stage: to integrate all disparate data sets with a technical infrastructure that supports real-time decisioning and personalisation, and do this in a secure environment.

The chances are that there are gaps within business processes that need to be addressed; by doing so, they may be providing surprising commercial benefits and greater ROI initially. Businesses can also comfort themselves with the knowledge that by making incremental change, it is possible to get closer and closer to becoming an organisation than can one day really benefit from a bigger data infrastructure.

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