This article on why digital advertising is being held back by the current siloed approach to consumer data was published on business technology website Technative.

Consumer data is the lifeblood of modern marketing and it’s currently undergoing dramatic shifts, driven largely by an increased demand for transparency around how consumer information is processed.

Despite the increasing amounts of ‘Big Data’ at their disposal and the growing investment in data management technology, marketers lack the resources to harness its full potential in accurately targeting the desired audience at the precise moment when they intend to make a purchase.

This means that although the digital advertising industry is growing rapidly, campaign performance has stagnated to the point where it’s threatening to undermine a sector that’s predicted to top traditional TV advertising in terms of spend in 2017.

Despite the Advertising Expenditure Forecast from Zenith Global predicting that digital ad investment will grow 13% this year, taking it over US$205 billion (and beyond TV’s US$192 billion), click-through rates have remained consistently poor at around 0.15% over the past five years or more, according to Google-Doubleclick.

At best this means brands are failing to get full value for the money they’re investing in advertising; at worst it means consumers are being presented with irrelevant ads that are ruining their online experience, and potentially fuelling the growing phenomenon of ad blocking.

So where does the problem lie?

The current siloed approach to structuring and purchasing data is hindering the development of a single view of consumers’ buying behaviour. This is because each data supplier only tracks part of the consumer journey, leaving knowledge gaps that mean brands are unable to develop a full understanding of the consumer decision making process and so fail to get a complete picture of purchase intent.

There also seems be a disconnect between data and the real world. There’s an obsession with targeting ads at the right consumers, yet we appear to have lost the art of understanding and interpreting people’s behaviour and identifying the key signals that indicate intention. Advertisers can serve ads to the right audience, but if the individuals targeted aren’t in the right frame of mind, they are simply wasting their time.

Intelligent approach

The good news is that there is a solution to all these challenges. It lies in a more intelligent approach to how all the data a marketer can draw upon is modelled. It delivers a greater understanding of the consumer’s intent, so that advertising can be used not simply to drive a purchase, but also to guide consumers along their buying journey, providing the information they need when they need it to ultimately make an informed purchase.

Intelligent data modelling moves brands closer to consumers and reverses the current targeting methods. Starting with the “right mindset, right intent signals”, then establishing the right place and right advert drives performance and provides a better understanding of what a consumer is going to buy, guiding which product and price point you should communicate.

Advertisers should develop a different strategy for targeting “active buyer” audiences that are looking for a product or service compared to ‘hot prospect’ audiences that are ready to purchase. As an example a mobile provider will know a lot about their customer from name, gender, what type of phone, when their phone is up for renewal to how much they spend on data each month. However, how do they build and strengthen loyalty to their brand?

They could go further in understanding what the customer’s current, real-time focus is on. What if they knew that in the last hour the customer has shown multiple “intent” signals from their digital behaviour that indicate their intention to book a holiday right now. This would be an ideal opportunity to change the marketing message in real-time to, say, “No data roaming charges when abroad this summer”, thereby becoming powerfully relevant to the customer.

Intent Signals

What’s even more exciting is how the mobile provider can use the intelligence of understanding “intent signals” from data modelling efficiently and effectively when prospecting for new customers. This includes knowing when NOT to target certain customers – not everyone is going on holiday at the same time, even if it is summer!

The result would be more efficient and effective advertising trading strategies that would drive up campaign performance levels. This also holds the key to closing the current dangerous gap between digital advertising growth and ad effectiveness.

The smart brands are already working with data modelling companies to help them boost campaign engagement and build stronger relationships with consumers. The problem is that there is currently a severe skills shortage of data modelling experts that not only understand advertising and programmatic trading.

Intelligent data modelling is poised to re-boot the entire data eco-system and offer advertisers unparalleled access to their target consumers without infringing on their privacy. Those brands that find the right partner will steal a march on their rivals, but more importantly the industry as a whole needs to work towards ramping up skill levels in data modelling and those people who can deliver it.

Harvey Sarjant of Sirdata

(This article was co-written by Three-sixty)