Have we lost the art of understanding data and interpreting key intent signals? Harvey Sarjant, managing director UK, sirdata.com certainly thinks so. Writing exclusively for ExchangeWire, Sarjant explains why intelligent data modelling will allow brands to reconnect with the entire consumer decision-making process.
Procter & Gamble’s recent close shave with a batch of bad data has, fortunately for them, turned out to be an amusing marketing mishap. It could have been much more serious and brand damaging however.
When Gillette, which is owned by Procter & Gamble, sent out free men’s razors wishing the recipient a 'Happy 18th Birthday' and a warm welcome to manhood, it was received by a middle aged mother of three daughters, a 48 year-old man, and a 19 year-old woman. Clearly not the specific target audience they were aiming for!
Which just goes to show, once again, how unreliable data can have real and long-standing consequences.
This is particularly the case with digital advertising which is being held back by the current siloed approach to consumer data, and, as a consequence, failing to give marketers a complete picture of the consumer journey.
Surely it is time for a new generation of intelligent data modelling to step up and enable advertisers and marketers to target consumers more accurately. Never mind reaching the target audience, how about going deeper and targeting the right audience based on intent?
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.
Whilst 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.
This means that at best brands struggle to get full value for the money they’re investing in advertising; at worst consumers are being presented with irrelevant ads that are ruining their online experience, and potentially fuelling the growing phenomenon of ad blocking.
Currently each data supplier only tracks part of the consumer journey which leaves gaping knowledge gaps. This siloed approach to structuring and purchasing data is hindering the development of a single view of consumers’ buying behaviour, and, as a consequence, brands are unable to develop a full understanding of the consumer decision making process.
There also seems be a disconnect between data and the real world. Despite an obsession with targeting ads at the right consumers, we appear to have lost the art of understanding data and interpreting people’s behaviour and identifying the key signals that indicate intention. Advertisers can serve ads to the right audience, but if the data is not showing individuals in the right frame of mind, they are simply wasting their time.
There is, however, a solution to these challenges, and 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, directing which product and price point you should communicate.
Advertisers should develop a different strategy for targeting 'active buyer' audiences that are looking for a specific product or service compared to 'hot prospect' audiences that are ready to purchase. As an example an insurance company will know a lot about their customer from name, gender and demographic through to their health status and when their insurance is up for renewal. 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 an extreme-sport holiday right now. This would be an ideal opportunity to change the marketing message in real-time to, say, 'Free additional insurance cover whilst abroad', thereby becoming powerfully relevant to the customer.
What’s even more exciting is how the insurance company 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 keen on extreme sports neither are they 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.