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Marketing Teams Lacking Confidence In Big Data Analysis

  • Jed Mole

    Jed Mole

Created at November 18th, 2013

Marketing Teams Lacking Confidence In Big Data Analysis

Effective big data analysis has substantial benefits. It is an integral aspect of any organisation’s competitive success and its abilities to retain consumer loyalty, gain valuable new customers and increase revenue.

But while the possibilities are great, many marketing teams lack the confidence, experience and trust when it comes to big data analysis. Put bluntly, big data can leave marketers blank. But that shouldn’t be the case, though it’s easy to see why:

Confidence shaker 1 – The issue of defining data.

The reason big data is scary to some, is just that – it is big! Velocity, volume and variety – the speed, size and forms of it, make data a seemly difficult creature to define, let alone analyse and utilise quickly and effectively. Even the name itself – big data – seems set to morph. In another year or so we may be calling it something else entirely.

Confidence shaker 2 – The issue of knowing how to use data.

Even those marketers who think they know when they’re dealing with big data may not be. The three V’s must always apply for data to be considered big. Additionally, a fourth ‘V’ should be considered during data analytics – validityData must be valid, current and relevant to be of worth. Confident marketers are those will well audited data, who can be certain of responsible, appropriate use – the foundation for knowing how to best use data.

Confidence shaker 3 – The issue of organisation (and keeping end goals in focus).

Perfect marketing teams combine the data they have access to with with current, well-curated datasets, and put it all to best focussed (customer focussed and personalised) use. But one marketing team’s definition of ‘best use’ is not another’s. There is no ‘one-use-fits-all’ approach. Data goals must continually be referenced to be achieved, and successful organisations are those who keep the consumer at the heart of everything they do. Analytics must keep a consumer-oriented focus to achieve the aforementioned benefits – retention of loyalty, and enhanced custom and revenue.

As a result, organisation in analysis is crucial. Data curation, management and monetisation are processes that must be carefully strategized, rapidly enacted, and confidently tested and invested in.

Like data application, organisation should be tailored to unique use. One trialled method of how marketers may better organise data analysis, is to employ an analyst in every department of a business to analyse relevant customer data for their area.

This may be just one small step for an organisation, and not an ultimate solution for all, but it’s a giant leap for data analysis and marketing. Departmentalising your analysis can help to answer questions such as “How can I best monetise my data?” or “What’s the most relevant way to use it?” “Which areas should be prioritised?”

Confidence shaker 4 – The issue of taking a complete, immediate view.

Knowing your focus and being organised to act on analysed data is one thing – actually utilising it quickly and effectively is another.

One piece of analysed information alone will not be valuable. Marketers must take a complete perspective of consumers to achieve a true consumer focus. Customers must be viewed as individuals, not data statistics – data must be personalised for marketing to work in all situations, in the moment and beyond.

For example, immediate intent data from searches allows a view into what a consumer wants at that moment. But without contextual consumer centric information, a brand cannot take a true consumer focus, and cannot recognise or personalise to suit that consumer later on. What is that person’s household situation? Might they be looking to purchase product for a child? What have they recently bought; would other relevant products appeal?

Remember the people within the data – personalise statistics – to take a complete and confident view.