Busting the myth of precision-targeting
Opinion
Precision-targeting may be seductive but comes with limitations. Here’s how agencies and brands can navigate the shifting sands of data-driven marketing.
Precision-targeting’s allure in digital advertising is undeniable. The promise of reaching “the right person, with the right message, at the right time” is compelling. However, the reality is far more nuanced.
The truth is, many advertisers are spending far too much time, effort and money in pursuit of the impossible, going all-in on precision-oriented tactics and paying heavily for enabling technology.
There are frequent challenges associated with privacy, accuracy, scale, expertise and return on investment. These uncertainties cast a shadow over the precision-targeting narrative. It’s enough to make any advertiser question whether addressable media is a buzzword or a fallacy.
In addition, advertisers are increasingly having to operate with black-box tools within walled gardens, unaware of the specific data points and algorithms used to target their audiences. This lack of transparency and fragmentation is a problem for omnichannel campaigns.
It’s equally important to recognise that data-driven marketing can be incredibly powerful when limitations are accounted for within a multifaceted data strategy. It’s about using data to sharpen your targeting, not labouring for complete precision.
In an era of signal loss and the crumbling cookie, it’s never been more essential to build a strong data strategy. But it starts with acknowledging the limitations of precision.
The perils of precision
The pursuit of precision-targeting presents several significant challenges.
Understanding the data: Tech giants possess vast troves of data on individuals, but it’s crucial to acknowledge that this is a blend of certainty and probability. Deterministic data points, such as email addresses or purchase history, offer a degree of certainty, but probabilistic data derived from inferences and predictions is inherently less reliable. And sometimes laughably inaccurate.
This lack of transparency is problematic and the risk of buying garbage is real.
Walled gardens: The dominance of walled gardens, where data and user experiences are siloed within specific platforms, creates significant hurdles. This fragmentation hinders cross-platform audience identification and targeting, forcing advertisers to play by the rules of individual platforms. I define my audience one way in Meta, then another in Google, using the data available to me in those ecosystems.
While consolidation of media spend within a single platform might seem like a solution, it ultimately limits reach and restricts access to the diverse media landscape.
Tyranny of the bottom of the funnel: The obsession with eliminating “waste” often leads advertisers to prioritise tactics that are easily measurable, such as those focused on conversions at the bottom of the marketing funnel. This is where black-box precision-targeting works well. But this myopic focus neglects the crucial role of brand-building, which often operates on longer time horizons and is more difficult to quantify.
The long-term impact of brand equity, nurtured through consistent and impactful messaging, is often undervalued in the pursuit of immediate, measurable results.
The privacy paradox: The increasing emphasis on user privacy, driven by regulations like GDPR and the California Consumer Privacy Act, presents significant challenges to traditional targeting methods.
Browser restrictions, user opt-outs and evolving privacy settings make it increasingly difficult to identify and track individual users. This shift necessitates a re-evaluation of targeting strategies and a greater emphasis on privacy-respecting approaches.
Cost conundrum: Precision-targeting, when feasible, comes at a premium. Targeting small, precisely defined audiences (and they will be small) often requires higher bids in auctions and can significantly increase media costs.
Moreover, the technology and data solutions that underpin these advanced targeting capabilities carry a substantial price tag, raising questions about return on investment and the long-term sustainability of such investments.
Organisational silos: Internal organisational structures can also hinder effective data utilisation. Data silos, fragmented teams and conflicting priorities can impede the seamless flow of data across departments and limit the ability to leverage data for strategic decision-making.
A multifaceted approach
Despite these challenges, precision does have a place. But a smart data strategy recognises the limitations of precision-targeting and is multifaceted.
First-party data as a foundation: First-party data, collected directly from customers, still forms an important bedrock. But it’s important to broaden the scope of usage. Leveraging this data beyond basic precision-targeting and lookalikes can unlock valuable insights into customer behaviour and motivations.
Power of geo-targeting: Geo-targeting is increasingly overlooked in the pursuit of hyper-personalisation. It just doesn’t sound sexy enough. But advancements in geo-behavioural modelling, which combines location data with other data sources to create rich audience profiles, offers a powerful, privacy-respecting and scalable approach to identifying and targeting audiences.
The enduring value of contextual targeting: Contextual targeting, which aligns advertising with the content surrounding it, remains highly effective. The insight we can get at a geo and one-to-one level informs placement.
Building a data-driven culture: Cultivating a data-driven culture within the organisation is paramount. This involves breaking down data silos, fostering collaboration between teams and ensuring that data-driven insights inform all aspects of the marketing process, as opposed to using different approaches in channel silos.
Prioritising transparency: When using third-party data, advertisers need to understand how much they’re paying for it. So demanding transparency from suppliers is important, where possible.
Maintaining consistency: Regardless of data type, one of the most important yet overlooked ingredients for success is consistency. Whether using deterministic or probabilistic data, it’s crucial to use it as consistently as possible, defining and identifying audiences in the same way for omnichannel activation, rather than building inconsistent proxies.
At the minimum, at least you know you’re targeting the same people to achieve the benefits of cross-channel frequency or deliver incremental reach.
Move away from narrow solutions
While seductive, the pursuit of precision-targeting should be tempered with a realistic understanding of its limitations. Like most things in marketing, there is no silver bullet. Advertisers need to be aware of this when buying into narrow solutions. And, like most things in life, consistency is key.
The end goal should be increasing the likelihood of reaching the people you want to reach, not finding and reaching them exclusively. It’s about using data to sharpen your targeting, not labouring for complete precision.
By embracing a multifaceted approach that leverages first-party data, geo-targeting, contextual targeting and a strong emphasis on transparency, marketers can navigate the shifting sands of data-driven marketing and deliver impactful campaigns.
Adam Chugg is head of data and technology at the7stars