Using Data to challenge NZ’s Alpha Media

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By Sophie Radford, Sales Director NZ, Exponential: The announcement that NZME and Fairfax NZ are hoping to merge in order to build a media company to fight off the impact of Google and Facebook, threw the issue of scale back into the spotlight.

What stood out for me in their application to the antitrust regulator was that their figures show that the combined companies would still only command around 11.7% of the marketing which is dominated by Google and Facebook.

This reflects the wider global trend – Brian Nowak, a Morgan Stanley analyst, recently predicted that in the first quarter of 2016, a whopping 85 cents of every new dollar spent in online advertising in the US will go to Google or Facebook.

The domination of these two alpha publishers in NZ (who have 37% and 16% of the market respectively) compounds the challenge that many NZ marketers face when it comes to data.

First off, we are of course a small country so there isn’t too much data to play with. This is tricky enough but when you layer onto that, the fact that a good chunk of the available data is held behind the big walled gardens, then it can be incredibly hard to get your hands on good quality, recent and relevant data to drive consumer engagement.

Without doubt this is the single biggest challenge that brands and agencies I speak with day in and day out tell me they have.

The good news is that whilst we can’t (nor want to) change the size of our great nation, we can maximise the data we do have to better enable marketers to engage. And the key to treating your data well is to apply intelligent analysis to it.

At the end of the day it doesn’t matter how ‘big’ your data is if you don’t unlock its potential – something that many companies are beginning to realise – even MI5, the UK’s Secret Service!

Unlike MI5, we are a pretty transparent and open lot at Exponential, so we’ve listed our five best tips for making the most out of your data as follows:

1. Use human insight to frame the problem
Data doesn’t ask questions. In many ways, the first few steps of any inquiry are the most challenging. The wrong choice of variables, poor instrumentation and measurement, or an imprecise question come with a high cost. No amount of automation can correct these missteps, so make sure you are asking the right question of your data to start off with.

2. Remember that bigger is not always better
I’ve said this above but it is worth repeating (and of comfort for our market), massive amounts of data defy the limits of human analysis, which is why machines are essential to understanding large amounts of information. But increasing the volume of data is only useful if it serves to improve the ratio of signal to noise. More data also means a greater risk of finding false correlations, or conclusions that aren’t relevant or actionable. Bottom line is that it takes a human to discern treasure from trash.

3. Know that everyone is lying
To put it more gently, people are masters of self-deception. Unlike weather patterns or traffic data, information that people volunteer is always biased in some way. People distort the truth about all kinds of things — sometimes directionally, as in how much they earn, and sometimes in unpredictable ways, such as how they feel about a product they know others like. This is another problem a machine can’t solve, but experience and human judgment can.

4. Understand that context is everything
The events that are captured and recorded in data are almost impossible to understand without knowing the context in which they were collected. The same action, even by the same person, can mean wildly different things. Purchase of a children’s toy at a supermarket, for example, often indicates a child is present — unless it is December, when the holidays play havoc with shopping patterns. The same product purchased online is usually bought by an adult without children. And if the toy is purchased in a store outside a consumer’s home area, there is likely to be a guilty parent travelling alone at the register.

5. Realise that a robot never told a great story
In reducing people to what data can measure, we leave out the most human of attributes — emotion. Emotions are marketing’s primary currency. People literally make decisions from the emotional centre of their brain, which is why smart marketers use narrative, context and feelings to tell stories that resonate. A story created by a robot is a story devoid of human emotion, which is one more reason why effective marketing, even in the data-driven era, will always need the human touch.

What it all boils down to is that it isn’t only down to the size of it – it’s what you do with it that counts when it comes to data, especially in a market like New Zealand.

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