The Data-Relevancy Reset

I sit down with dozens of clients and prospects each week to discuss data-driven advertising, programmatic media, adtech stacks and martech integrations. I talk about CRM onboarding and the efficient use of 1st, 2nd, and 3rd party data.  I talk about content recommendation and dynamic creative deployment. I talk about centralized audience management and holistic data controls. One thing is for sure, this ecosystem is complex, fast paced and often times over-complicated.

In short I talk about the “how” of what we do in data-driven advertising, and the market is so fixated on the “how”, on the new techniques and instruments, on the new ad tech solution or vendor, on the new buzz word, that we often fail to focus on the “why” of what we do.

Recently, I’ve been thinking and talking about data-driven advertising as simply as I possibly can. I’ve been deliberately and consciously trying to reduce the “noise” in my own talk track in order to address the fundamental “why” behind data-driven advertising.                                                                                                                 

Data-driven advertising is about using tangible and meaningful signals regarding a person’s behavior in order to better understand that person’s interests, demographics and purchase intent, and to subsequently translate that understanding into more relevant advertising, which grabs a person’s attention and establishes a platform via which you can foster awareness, saliency, consideration and ultimately action.

The problem is there is so much data available in the market today that it has become highly commoditised and homogenized, and as a result data segmentation is being created and targeted without the necessary thought behind how it impacts relevancy. Without relevancy, you can’t hope to grab a person’s attention and without attention, you can’t hope to influence preferences or behaviors. Furthermore, we’ve honed in on one dimension of relevancy – the audience, but there is also context (where are they spending their attention online), time (how long ago did they show an interest, where are they in the purchase funnel etc.) and value (what is this person’s attention worth to me given what I understand about them).

The power of data targeting can be seen clearly with retargeting using 1st party data sets. It is not rare for retargeting to achieve a 0.10% CTR or higher, when compared to an average display CTR of 0.05% (a 100%+ increase in engagement). Common sense dictates that this increased engagement rate is a natural result of high recall and attention, which is derived through high relevancy. It stands to reason, that if you have recently been looking at a specific product, and an advert showing that exact same product is delivered to you moments later, it is far more likely to grab your attention than an unrelated advert for a product for which you have shown no prior interest. In this respect, we know data-driven advertising works, it is measurable, tangible and historically proven.

However, when we move beyond 1st party data segmentation, we experience challenges in proving efficiencies, or rather, we experience challenges in establishing relevancy. Too few practitioners verify the source, segmentation, and specifications of the data they are using for targeting purposes. Too few focus on scale over relevancy. And too many believe data should be used for all media targeting, regardless of context, intuition or common sense.

For example, some would prefer to use a modelled 3rd party data segment to identify gender, without knowing the source, the modelling methodology, or the accuracy of that segment, instead of working with known publisher partners, with whom they have strong relationships with, who will provide transparency on data curation and that have proven and well-established audience skews. Data comes in many forms, it is a collection of our knowledge and experience, it is as much the latter in the example above as the former, and yet all of our current focus is on the former, the smart data collection methodologies, the machine-driven clustering of audiences, the extrapolation of segment membership through “proprietary predictive modelling solutions”. We’re focussing so much on the how in today’s marketing that we’re losing sight of the why.

The more you know about the source, segmentation, and specification of the data, the easier it is to align creative messaging and to ultimately drive relevancy.

Worse than simply selecting the wrong type of data to inform our marketing and communications, we often build audience segmentation for prospecting without clearly identifying and aligning creative or content. We’re deciding on the person we want to target, without thinking about the message we want to put in front of them, so we end up putting the same message in front of everybody and as a result, relevancy suffers, attention suffers and results suffer. If you aren’t going to differentiate creative, there is very little reason to adopt an audience segmentation strategy (other than to A/B test segment performance in order to derive audience insights).

So, what am I recommending my clients do? In short, I’m recommending:

  1. Focussing on 1st party data and ensuring you extract maximum value from your first party data before you consider moving into other data sources
  2. Use all data and information available to you, use every piece of consumer insight, whether it comes from TGI, ComScore, Market Research, your DMP, or from business or media partners. Don’t rely too heavily on one source of information, and remember audience is only one dimension of a multi-dimensional problem which also includes, context, time, and value.
  3. Work with media partners to understand their 1st party data sets, their audiences, their content and the context they provide for your brand/product. Think about how you can leverage these elements to build additional relevancy and disrupt attention across these properties
  4. Use 2nd and 3rd party data sparingly, and do so with as full a knowledge and as much customization as you possible can.  Work with data providers and media owners to build bespoke segments, define your own parameters and data-sets, and vet accuracy on a continual basis
  5. Always align creative and content with the target audience. Challenge yourself to find ways to tailor the message towards the intended target, to speak on an individual and 1:1 basis as much as you possibly can.
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