The desire to harness Big Data continues to occupy the minds of most marketers. As more and more connected devices generate even greater volumes of user data, the development of data-driven marketing strategies that leverage Big Data to improve targeting has become an industry obsession. But is this fixation on Big Data actually a bit of a red herring? 

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Teradata’s 2015 Global Data-driven Marketing Survey reveals that 90% of marketers regard personalisation as a current priority, with 78% claiming to use data systematically to target their customers. The apparent Holy Grail is to move beyond segmentation to ‘true one-to-one personalisation in a real-time context.’ But how?

The Present Focus

The present focus on data mining to identify trends and deepen customer understanding is understandable. The development of the Internet of Things is just one of the latest things to provide renewed catalysts for data capture 75% of global data created by consumers, marketers can be forgiven for becoming excited by the number of data points open to them for targeting. But bigger is not necessarily better. More data does not mean more insights, but it does mean more processing. If marketers have a real-time remit, the likelihood is that the bigger your data set the slower your output will be. And by the time you get there, the world may have changed.

Effective Targeting

Effective targeting is not just about the depth of understanding that can be achieved through analysis of all your many data points. It’s about understanding how your audience changes through time – and focusing only on the data that’s relevant to you, your users and your product.

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Marketers need to be able to analyse trends in real time. What are your users’ core values? Do they interact with your product in specific geo-locations, use a consistent device or engage at a certain time? By building up a dynamic profile of your users you’ll be able to target them more accurately. The challenge is to establish the core data that actually contributes to their browsing and purchasing preferences – and respond to it.

Dynamic Model

Ensuring your model has a dynamic element is essential. It’s unlikely that you’ll be targeting only one broad audience – your brand will have sub-demographics that are equally as important. And since you’re likely to be targeting customers who frequently change their minds about products, it’s important to strike while the iron is hot and deliver responsive real-time messaging. 

The most effective means of achieving this is through programmatic advertising and machine learning. This allows brands to identify and engage with dynamic audiences at speed and scale. Machine learning platforms recognise prospects from ads served on the system and automatically identify new and subtle trends as they emerge. The approach accelerates customer profiling, meaning prospects can be identified, tracked and targeted quickly. 

it’s all too easy to get side-tracked by the sheer volume of available data

In the current environment it’s all too easy to get side-tracked by the sheer volume of available data. Big Data opportunities may appear exciting, but the subsequent analytics will only slow you down. By the time you try to apply that demographic data, the chances are your audience will have shifted. If personalisation really is your goal, logistically there’s no other way to deliver relevant real-time targeting than via dynamic targeting systems.

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