As the old joke goes "I can't believe it's nearly a year since I didn't become a better person". If your 2018 resolutions were to try and keep 2017's (or 2016's for that matter) then you're in good company as 42% of people who make resolutions fail on them each year and a paltry 9% would say they were successful in reaching theirs.
There are good reasons why it is so difficult to keep resolutions: from unrealistic expectations, not being ready for change, having too many targets or being too easily dissuaded by initial failures - if I may jump to a conclusion you could say that it's all down to a lack of strategy!
I suppose all this makes for pretty negative reading and it would be enough to put anyone off bothering, yet contrary to the high failure rate so many believe that the turn of the year is the perfect catalyst for a self improvement make-over. In seeking sage advice from the plethora of life coaching blogs, I've been introduced to the basics of making achievable resolutions that will stick, which if I bind into one sentence for your benefit would be - Don't make resolutions - have a strategy like setting SMART daily goals.
So what's that got to do with DMPs you would be wise to ask - well to me, managing a DMP can sometimes feel a little bit like that post-binge seasonal life-hack during the down time between 26 and 31 December followed by the subsequent mid February resignation, let me illustrate:
So here are a few data management life-hacks as counsel and opinion from my own DMP consulting experience. Maybe one of these will be a SMART goal you can accomplish in 2018?
When it comes to DMPs and data, I am always reminded of a quote by H. Jackson Brown Jr when he said "Nothing is more expensive than a missed opportunity". To invest in a DMP, but not plumb in ALL your valuable customer data and then worse still not make it your goal to move from data integration to data comprehension is a huge missed opportunity.
A DMP cannot create miracles by turning poor customer data into rich and valuable insights or make an inefficient marketing strategy efficient overnight, in fact without a data strategy the DMP will simply turn into a very expensive audience building tool that sits at the front of your programmatic advertising process.
Moving to comprehension requires such a data strategy, and one under-pinned by a DMP, the ingredients of which are:
Stating the obvious, after all this section is called "move to comprehension" - the next step after data consolidation is to make sense of it all. The best way to approach this is to have a clear business objective in mind that will fuel your analytical approach.
Let's take for example the most popular challenge from that IDG study - Finding correlations across multiple disparate data sources. What is this really all about? Why would an organisation want to find correlations across data sources?
The goal of assessing your customer data in this instance is to arrive at a measure of three things:
If media efficiency and ROI are your businesses detox missions in 2018 then having an audience segmentation strategy is paramount. Good segmentation requires a thorough understanding of the following:
This however is not a strategy, having a firm grasp on the above means that anyone creating audience segments will be set up for success.
The strategy comes into play when you consider an audience segmentation strategy should be based on campaign goals, success metrics & target audience. A DMP user should first create preliminary definitions of their target audiences, build them in the DMP and then, before activating, run pre-campaign analytics. So what are we looking for in these pre-campaign reports? We should aim to understand demographics, preferred engagement channels, device engagement data and 3rd party data segments that enhance our knowledge of the customers intent. These insights allow you to tailor the creative message and ensure that the balance of scale and accuracy of the audience is suitable.
Once a campaign is active, mid campaign analytics should allow discovery of ideal customer attributes. What attributes correlate most highly with the converting profiles and which 3rd party data assets are contributing most positively to those conversions. Use this detail to optimise existing audience segments and introduce exclusions for converters into the original audience to reduce wasted ad-spend.
Now consider the full conversion/purchase funnel in your segmentation strategy. Create a second audience of only converters and analyse that audience to identify what the shared data attributes are (inc. propensity, affinity and congruency). Use these attributes to improve prospecting by building tiered targeted look-alike and act-alike audiences (targeting based on more and less accuracy adjusting bid strategy accordingly). Create retargeting and cross-sell campaigns for those converter segments with exclusions for prospecting segments to ensure consistency of message and reduce wasted ad-spend.
When I look to start building an audience segmentation strategy, I don't just look at reactive campaign needs, as this can become the trap of audience building in the DMP, literally building audiences to an agency media campaign brief. Instead consider that an audience can be predictive, descriptive, reactive and/or passive. We can build audiences with zero reach that will increase over time as customers qualify.
This means it is possible to concentrate on having granularity in your full funnel audience segmentations. After all it is through this granularity that we enable better performing audiences, with controlled cost and reach for ad buying that have the desired detoxifying impact on ROAS and ROI.
In the next article I will offer my opinion on something that is crucial to the DMPs value in the marketing and advertising industry - Organisational Readiness - It's not just about the right people, it's about the right team, the right decision making, the appropriate education and a relentless focus on test and learn reiteration.