Going Back To Basics: A Deep-Dive Into Contextual Advertising’s Resurgence

Digital advertising has come a long way since AT&T purchased the first online banner ad in 1994. Pay-per-click (PPC) ads have since emerged to become firm favourites of brand marketers and mobile advertising exploded courtesy of the smartphone boom.

However, purchasing ad space is one part of the battle. Placement is key to determining a marketing campaign’s success – and this is where third-party cookies have made themselves invaluable. But the winds of change are blowing through ad land.

Consumers have had enough of their personal information being sold to the highest bidder. Government bodies around the world are taking a stand in the name of privacy – see the American Data Privacy and Protection Act (ADPPA) and the EU’s General Data Protection Regulation (GDPR). Both were introduced over the past few years in the hope of creating a safer virtual world.

Leading tech brands have also taken note of shifting expectations. Despite Google pushing back cookie depreciation until 2024, brand marketers can’t afford to rest on their laurels. This is the optimal moment to proactively adjust your strategy – and you don’t need to reinvent the wheel to do so.

Contextual targeting was the go-to form of advertising pre-internet – but interest faded with the rise of behavioural targeting. Its value, however, should not be underestimated. And recent advancements to machine learning and AI in this field have only served to reinforce its case.

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Changing of the (targeting) guard

Walled Gardens – such as Facebook, YouTube and Amazon – used to be at the heart of all marketing strategies. Consumers flocked to these sites, meaning the reach and user insight they offered was unrivalled.

Publishers held unprecedented power during this era. But evolving consumer behaviours are shifting the status quo. 30% of Facebook users, for example, acknowledged that their usage on the site had dropped significantly over the past 12 months. And this means that third-party cookies – so long a crutch for brand marketers – are seeing their importance decline.

Brands need to focus their attention on the open web. Purchasing large caches of consumer data is outdated – and whilst behavioural strategies are considered simpler, these do not cater to consumers’ desire for relevant experiences. McKinsey’s research has uncovered that 71% of consumers expect brands to provide personalised experiences with 76% becoming frustrated when this isn’t the case.

Bespoke, tailor-made ads are key to breaking ground with new audiences – and this is where contextual targeting shines. It may not be a new innovation but its benefits have remained largely unchanged. Collecting data about articles, such as their content subject and viewing figures, allows brand marketers to ensure ads remain effectively placed.

Cost-effectiveness also works in contextual’s favour. Research indicates that contextual targeting ads offer almost 50% lower cost-per-click (CPC) and 36% lower cost-per-thousand impression (CPM) compared to behavioural ads. Employing this strategy can be the first step to unlocking greater return on investment (ROI) for marketing budgets.

But contextual targeting isn’t perfect – there are a number of flaws that have previously prevented it from becoming the dominant force. Its time-consuming nature and excessive categorisation have both been cited as major pain points. And this is where recent technological advancements are flipping the script.

New bells and whistles

Standardisation has infected the whole marketing sphere – contextual advertising included. Predefined categorisations and content taxonomies have helped streamline the targeting process. But this has come at a cost.

The emphasis on simplicity was negatively impacting accuracy. And although basic keyword targeting was effective in securing brand safety – allowing brands to select topics to be included and excluded from strategies – this umbrella categorisation was too broad.

Brand marketers face an uphill battle staying tuned-in to what consumers want. It is estimated that more than 2 quintillion bytes of data are added to the internet every day – and with the number of internet-connected devices continuing to grow, the situation isn’t going to get better without a change in tactics.

AI’s ability to analyse copious amounts of data within a short space of time is revolutionising contextual targeting. Seedtag’s Custom AI features network level analysis (NLA) – this observes all URLs to identify trends and content clusters relevant to campaign briefs, as opposed to the traditional technique of assessing single pages. This newfound efficiency shouldn’t be overlooked.

Contextual targeting’s precision is also benefiting from machine learning’s evolution – particularly AI’s ability to identify content patterns on its own through unsupervised machine learning.

Picture a vegan fashion brand trying to develop a marketing campaign around its new vegan trainers. Old contextual targeting would make the ad appear next to fashion and sustainability content – but the likelihood is most of the sustainability content wouldn’t be directly relevant to the brand message. Custom AI allows the brand to appear exclusively alongside sustainable fashion-related articles.

These issues would not only persist with scale – they would grow more prominent. But brand marketers can now leverage AI and machine learning to scan the complete network of URLs. This is the foundation to creating advertising strategies which remain precise irrespective of their scale.

Looking back to look forward…

Thirty years on from AT&T’s groundbreaking purchase and the digital advertising landscape looks significantly different. User privacy is at the forefront of all conversations. Brand marketers can’t be tone deaf to these changes – they instead need to embrace the sign of the times. And this starts by leaning more on contextual targeting.

Third-party cookies have been a reliable asset up until now – but 2024 will see their stock fall rapidly. Adopting a revamped contextual-based strategy built around AI and machine learning offers an opportunity to hone your campaigns’ efficiency and precision – as well as keep your consumers’ data safe.

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