Recently, there has been increased conversation on artificial intelligence and how it can make advertising more relevant for consumers. Its potential cannot be understated when it comes to improving the promise of personalised, adaptive and appropriate advertising; something programmatic is yet to deliver on.
However, before we relinquish all control to the machines, it is important to understand the broader effects this may have, especially in an industry that depends heavily on bringing brands and consumers together. We need to ask ourselves if we have been too quick to tout the benefits of being exclusively data-driven, without fully considering the broader and long-term implications or the available alternatives to make the best use of data.
If you look at the key themes of recent conversation across the industry, they have centred on driving outcomes. While very significant, it’s sidelined an important part of any brand’s strategy – establishing an emotional connection with the consumer, and in combination with contextual relevance – building long-term brand affinity.
Therefore, the debate should be extended beyond how data can be used to drive performance and cover how it can be applied to realise value. And not just how it is applied, but also the ‘when,’ which is fundamental to understanding different approaches, which are critical to successfully bringing together the already converging direct and programmatic disciplines.
Data-driven vs. Data informed
Data-driven advertising, also known as programmatic or real-time, has been revolutionary. No one can argue that it has forever changed the manner in which businesses can target consumers, and that media is now more accountable. However, the shift to data-driven advertising has seen the discussion move towards the efficiency of the transaction, which relates to price and performance.
Data-informed advertising is less well understood, but is equally important. Being data-informed means that an individual recognises that he has a small subset of the knowledge required to reach a decision. Its primary use is in media planning and direct sales, which describes the inventory purchased for future purposes. A good example of the utilisation of a data-informed approach is determining the potential return on investment by relating investment history with performance data. It can be extremely useful if access is available in real-time at the most basic levels, especially when used before and during the campaign. However, current constraints of what is a manual and iterative process mean the data can only be applied retrospectively, with no overarching consideration as to when it can be most useful.
Bringing the data where it is needed
Automation gets the data where it is needed. It addresses the gap that exists in direct sales, meaning data can be made accessible to both the buyer and seller, to help them make better decisions in real-time. Purpose built technology brings automation across the entire campaign lifecycle. It solves the issue of the highly iterative processes whereby decentralised information exists in separate systems that do not interact.
The disconnect often leads to inconsistent information, and, therefore, a removal from a point of truth of inventory availability. This increases the risk of errors, both pre and post campaign. Considering the numbers behind it, this is not only surprising, but with technology that’s available that directly addresses these issues, it should be inexcusable.
Move the Conversation Beyond Price to Value
Price is what you pay for a particular product. Value is what you are willing to pay, and what a commodity or service is worth. The discretion is necessary, particularly when considering a data-driven or data-informed approach. The latter opens up more opportunity for a sustainable industry-based model and balancing the two successfully.
Using investment history, to correlate price to volume to accurately inform a negotiating position is almost impossible in the eco-systems, as is correlating price to results (i.e. value). Historically, there has been no way of linking price and value insights to a marketplace of available inventory.
The key is to look beyond price and performance where the true value of inventory can be unlocked. The process of then conflating those impressions into products and buying it ahead of time, based on a shared understanding of its value, is a win for both buyers and sellers.
The idea of a shared value is only possible when the buyer and seller work together, and their relationship supported by automation and not replaced by it.
