Advertisers in Asia Pacific are forecast to spend 45 per cent of their advertising budget on digital in 2018 – a 13 per cent increase in regional digital expenditure over the past three years. With this in mind, it’s vital to ensure this sizeable outlay drives maximum results by creating truly engaging ads.
To date digital advertising has mostly consisted of static or fixed ads, which once built and approved are served with no additional changes or development. But lack of creativity, measurability, and no guarantee of reaching target audiences make them ineffective. Today’s consumers engage with content fluidly — using multiple devices and channels to shop, browse and chat. This means ads need to be highly creative, relevant, and dynamic.
So, how can brands create these chameleon ads that change with each consumer? The answer lies in data and machine learning (ML) – not only using it to feed your campaigns and ad creative, but also to measure success.
Using data to drive creative
Traditionally, decisions about advertising creative are made at the start of a campaign with one version of an ad chosen to run across all digital channels. While such ads are much cheaper to create, and require zero involvement once they have been approved and delivered, they are often uninspiring for consumers. In an age of hyper-personalisation, ads risk falling flat if they are static and lack real-time relevance.
With the vast amount of data available through digital channels, marketers now have the opportunity to leverage dynamic creative optimisation (DCO), which imbues ads with constant fluidity by allowing for data-fuelled – and ML driven – fast-paced adaptation. Unlike traditional fixed ads — which never change size or creative — dynamic ads adjust to suit their environment based on data about their target audience, such as browsing history and current location. Therefore the same ad, for the same product can be delivered globally, with the language, colour, price and aesthetic tailored for each individual viewer.
But the benefits of DCO aren’t limited to better consumer experiences. DCO ads are highly measurable, meaning analytics can be used to track reach and responses, and optimise campaigns across multiple channels. For example, insights might allow brands to stop wasting ad spend by avoiding bidding for inventory on websites with low readership, or repeatedly showing ads that receive minimal engagement. By harnessing data to adapt their strategy, be that redirecting spend or leveraging formats certain audiences find most engaging, brands can significantly boost value for consumers and their business.
Machine learning: The driving force behind DCO
While data acts as the fuel in the never-ending cycle of digital advertising, ML is the mechanism that drives and delivers personalised ads to consumers in real-time. ML is the application of Artificial Intelligence (AI) where machines are given access to data and allowed to learn and create actions based on the information received. ML works with this data to ensure each user sees the most relevant ad to suit their needs within milliseconds, matching interests and even pairing ads based on the emotion and semantics of webpages the consumer is reading.
Increase inventory options
All advertisers want premium inventory — displayed on the best pages and in the best location — to increase exposure for their target audience. If the dimensions of ads are fixed, this could often mean that a whole host of inventory is no longer suitable. With DCO, ad and banner dimensions can be adapted to fit any number of inventory placements and can instantly reconfigure to fit any ad height and width. ML makes these adaptations possible as it funnels data and processes it to drive these adaptions in ad dimensions. This is particularly beneficial when engaging with individuals across many devices – giving brands the means to tell a seamless story no matter which screen consumers are using.
Targeting the right people at the right time
To maximise success, targeting ads is no longer enough — creating personalised messaging for each consumer is the key to driving user engagement, and results. By using data gathered directly from audiences with their consent, brands can customise ads in accordance with multiple variables, such as geo location, gender, and previous browsing history. For example, if data shows that a consumer has previously visited a site but not yet made a purchase, the brand might opt to serve ads featuring a voucher to drive a conversion. Or, if past customers return to a site, the brand may suggest items that compliment previous buys. Furthermore, brands can also use data to determine the best time to serve ads so they reach the right consumer, at the right time. This encourages positive brand associations as the user only sees ads that are relevant to current interests in real-time.
It is time to look to the future and embrace advertising driven by data. To further develop relationships between consumers and brands the need for personalised dynamic ads that adapt to a user’s preferences are vital. Moreover, the advantages of DCO – harnessing data with machine learning – are equal for companies and audience: measurability and analysis mean ad dollars are efficiently spent, while consumers receive relevant and engaging ads. This symbiotic relationship helps nurture brand relationships, drives campaign optimisation and ultimately increases conversions.