Harnessing big data for multi-channel.

multiple-screens

There’s no doubt we are entering a new age and the convergence of technology is steaming ahead at a super fast pace.

In the last few weeks for instance there has been a fair bit of criticism of the way that media numbers are reported as they are not particularly granular around the use of online, especially elements like TVNZ OnDemand and other media network related content. For instance, where does media like iHeart Radio sit in that reporting too? Is it online or radio?

So how can we even plan effective multi-channel campaigns when we can’t have confidence in the measurement?

Another factor is the way that reporting, especially around social media, can lavishly embellish the actual effectiveness. Likes, views etc are great but are they NZ eyeballs or from farther afield? Call me old fashioned, but fundamentally you need to be sure that the eyeball being measured as viewing your activity is also an eyeball that can have a positive impact on your sales. The last few years has seen the rise of overclaim in eyeballs to fuel adrenalin pumping award entries for instance, even to the extent of claiming more views than the population of New Zealand can deliver!

So what is a genuine eyeball?

Tying better understanding of how campaigns are delivering against a set of measurable objectives is just the start of making sure your multi-channel campaign is going to maximise your marketing spend.

Assuming you are setting out to drive sales – whether online or offline or both – and possibly brand awareness against a set of key metrics (eg modern, innovative, ‘for me’) you will be wanting to use big data to measure this effectiveness and map it against channel selection.

How does this help multi-channel?

Firstly, lets define multi-channel. Essentially, it is simply maximising every touchpoint that the customer can connect with you through. It just got a lot more complicated since on top of all the ones that have been around for decades we now have smartphones, tablets, laptops etc. Increasingly, we are seeing it as allowing people operating a range of screens to be able to access our content and interact with it, including the physical environment of the store or wider marketing collateral.

Reporting of how people are connecting is struggling to keep up with the explosion of the number of ways you can interact. And obviously how people consume media is different between screen devices, and different depending on the environment they are consuming them in. For instance, if I am a major retailer and a consumer is using their smartphone in my store to help them decide on product selection, pricing, or customer reviews, then the way that I allow them to do this and the experience could be quite different to their behaviour at home browsing on a larger screen device.

Adaptive web design, wi-fi, smart content delivery and device prioritisation all play a crucial role here. As does connectivity to consumer engagement media such as loyalty programmes, email bases and social networking.

The reality of all this is that harnessing what we learn from ‘big data’ in terms of user behaviour is becoming more important and the speed at which we can use this data to adapt our marketing programmes, content provision, and channel selection is increasingly important.

We can use our own data for ‘owned’ media, but then in channel selection for ‘bought’ media we are so reliant on the way that it is reported through the media owners (can you believe we still get such vague data out of Sky!). So we need our media owners to sharpen up their act or increasingly they will be bypassed as other more measurable media channels (not least YouTube) may become preferred choices for some campaigns.

And we need to develop adapative campaigns that recognise that people are not just second screening but sometimes third or more. Our data will give us the insights, so long as we know how to harness it.

Author: Ben Goodale, Managing Director .99 and justONE