Is there still a role for traditional planning tools, methodology, panel based research and demographic based personas in campaign planning?
For a long time, Big Data has enabled scale, personalisation, targeting and efficient trading through programmatic buying. It’s allowed engaging dialogue within campaigns, putting motivating, interactive content into people’s thought processes, rather than relying on a (potentially) inefficient, broadcast media approach.
We’ve never known more about our target audiences, about who they are, what they do, what motivates them, how they communicate, interact and engage; and we’ve collected most of this data passively and unobtrusively – in many cases consumers have volunteered it.
The future is here – but is it being exploited to its maximum effectiveness?
Panel Research v Behaviour
Research tools such as TGI, Kantar, and Nielsen rely on small, potentially unrepresentative samples of difficult to acquire insight and information. For years they were the best source of research data that we had, but now they’re archaic and by implementing campaigns based on their representative personas and data sets, planners are inherently planning an inefficient campaign because the demographic they’re targeting has in-built differences in the way that they behave – meaning that only a small proportion of that demographically defined audience is actually interested in the product or service.
Big Data groups people by their micro interests, likes, behaviours and actions, rather than their age or gender. No longer can retailers assume that their audiences can be conveniently placed into ‘buckets’ and assumed to be the same type of person as someone else who happens to live in their postcode, and is of the same gender and similar age.
Some of the foremost exponents of this type of ad-targeting are Google, Facebook, & Quantcast, which (not) coincidentally are 3 of the 5 largest data processors in the world (United States Department of Defense and Amazon Cloud – I know you were wondering).
Similar – But Different
As part of ongoing social self-affirmation, I always analyse my peers for context, and it always amuses me how different, yet similar I am to many colleagues and friends. My best mate (my Best Man) and I went to school together. We have comparable education levels, comparable professional paths, and both work in London. We like Sport, Politics and socialising in pubs, bars & restaurants. We are very similar.
We’re also completely different types of consumers.
I am married, mortgaged, with 2 children. I travel once a year on a family holiday. I own 2 cars. I go to restaurants once a fortnight if I’m lucky. I spend most evenings watching TV at home, and most weekends at children’s play parks or visiting family & friends.
He is single, lives in rented accommodation, has no children, travels abroad about once a month and eats out 3-4 times a week. He goes to the theatre and/or cinema most weeks, and plays sport every weekend.
If we were to be put into some of the usual demographic buckets, we’d be in almost exactly the same groups every time – age, gender, household income, education level, location, so we’d be bundled in together and be served the same ads.
Which means that most advertisers would be wasting their money on targeting one or the other of us.
The challenge is knowing which one of us they should stop targeting, (and whether that’s even possible?)
Lord Leverhulme once famously said “Half of my advertising is wasted, and the trouble is, I don’t know which half.”, but that no longer need be the case. By targeting us based on our user behaviour, rather than being hit with ads for family MPVs (of interest to me, not him), he can be served ads for city-breaks or theatrical performances (of interest to him, not me).
The Role Of Ad-tech And Programmatic
Programmatic trading has benefited everybody involved in the process. Large advertisers have been able to diversify their creative and create more personalised and engaging ads; smaller advertisers have suddenly been able to reach their smaller, potentially niche, audiences at an affordable and efficient level, and consumers are being served ads which are more relevant and interesting to them, which is helping fund publishers to provide interesting content for them to consume.
Whilst ‘behavioural targeting’ has been around for almost 10 years, in its infancy it was a fairly rudimentary product (usually just retargeting someone who’d previously visited a site), and cynics would say involved ‘cookie-ing’ half the internet and claiming attribution for an action on a post-impression basis. However, driven by technology development, behavioural targeting has evolved into a far more sophisticated product as delivered by companies such as Quantcast, Rocket Fuel, AdRoll & Criteo, however the era of true personalisation at scale is only just upon us.
Being able to pre-qualify an individual by multiple levels of inferred information based on hundreds of online interactions, and then serve them very specifically targeted messages will be the new normal, and will take advertisers closer to their holy grail of hitting their point of diminishing returns – at an affordable ‘cost per acquisition’. With the data available, it’s not inconceivable to think that advertisers would be able to effectively guarantee sales or leads, at volume, within their target CPA, meaning risk-free planning and campaign execution.
We’re a little way away from that yet, but we’re definitely in the era of behaviour data providing targeting capabilities which provide consumer engagement delivering ‘quality, quantity, quickly’.