Programmatic: The path to an effective data strategy?

Marketers don’t need to be told that it’s essential to have a data strategy. Brands, organisations and agencies are now swimming with Chief Data Officers and Heads of Insight – roles specifically designed to wrestle with the ever-increasing mountain of data available to hungry strategists. What’s more, estimates state that 35% of a marketer’s budget is now assigned to technology.

On the surface, this seems like good news. Business owners and marketers alike are finally taking data seriously – recognising that the ‘Big Data Revolution’ is the new normal and you have to get to grips with it if you’re to keep up with competitors. Yet, more and more often, I see articles referring to a brand’s new, revolutionary ‘data strategy’, only to read on and discover that the strategy revolves around one particular element of data-driven marketing: programmatic.

A truly effective data strategy helps you to understand why people are in those audiences in the first place

According to The Drum, programmatic spend now accounts for 70% of all display ad budgets. In some ways this is unsurprising: it offers a host of attractive benefits, including guaranteed impressions from specified audiences and posts that can be targeted to an apparently granular level, taking into account everything from the type of device to the weather.

However, while programmatic has its merits, by hopping straight onto the programmatic bandwagon without embedding it in a wider data strategy, or even taking the time to fully understand its limitations, you risk missing the opportunity to create genuine, lifetime connections with your customers.

Do you know who you’re talking to?

While programmatic promises highly-targeted marketing, in practice it often fails to deliver. In 2016, the ANA found that programmatic attracts 73% more bots than direct buys, meaning that ‘the average advertiser is spending $10m on ads that no consumer ever saw’. This much is clear: when it comes to relevance, programmatic can fall short.

Are you sure you know who you’re talking to?

Even when advertisements reach the eyes of consumers, they’re often not arriving in front of the audiences for whom they were originally intended. This was the case with the recent YouTube scandal, an incident that sent a number of high-profile brands scrambling to halt their programmatic efforts after a gap in Google’s filtering system caused their adverts to appear alongside a range of offensive and dangerous content.

Are you reaching people who care?

Scandals aside, the main problem with programmatic is that it fails to understand and deliver context to the data patterns it gathers – and context is key when it comes to engaging with people successfully. Programmatic doesn’t understand and distinguish between people’s impulsive browsing history or one-off buys and the purchases driven by genuine, long-term passions, motivations and mindsets. One curious click on a dress you never intended to buy and said dress will pop up on every website you browse for weeks to come (a dilemma skewered brilliantly in this piece). Buy some baby clothes as a present for a new mother, and you’ll be bombarded with offers from children’s brands, whether or not you’ve ever shown any interest in children’s products before or since the purchase.

You can’t just match offers to generic audiences; a truly effective data strategy helps you to understand why people are in those audiences in the first place – the motivations and mindsets that put them there – and adapt your marketing strategy appropriately. Constantly pushing adverts for ovulation tests towards a young woman who isn’t interested in having a child is not only pointless, it actually turns your marketing efforts into background noise. A much more effective alternative: using sophisticated data techniques to understand customers’ lifestages and predict when the woman in question is ready to have a baby, so that you can target her with personalised messaging at the relevant time.

Instead of relying on machine learning alone, brands must work on making the most of their own transaction data, combining it with new big data like social intelligence to create an enhanced understanding of real customers. A truly effective data strategy allows you to know your customers so well that you don’t have to rely on an algorithm to decide how and when you speak to them.