How to thrive without cookies

From data partnerships to marketing mix modelling, to be successful in a cookieless future, marketers will need to have the confidence to reach the same, if not wider audiences. Here’s four simple steps to success…

As the sun sets on cookies and consumers become ever more aware of how their data has been used, the world of advertising is starting to change.

Second- and third-party data are no longer the ‘all-stars’ of the marketing world. Traditional data architectures are being revamped to fit a cookieless model. And consumer privacy is becoming a major lever in advertising campaigns.

All this means that marketers are looking for new ways of targeting their customers, but also methods of measuring, attributing and optimising their growing data sets.

For those who have only ever known cookie-based methods - particularly large corporations spread over multiple domains - the move towards cookieless can seem daunting.

Marçal Serrate, Chief Technology Officer, Hybrid Theory, offers four ways marketers can comply with data privacy regulations and thrive in a cookieless future?

1. Leverage first-party data

Everywhere you turn, the advice is to focus on first-party data, and there’s a good reason for this. Aside from being 100% third-party cookieless, it provides marketers with relevant customer insights that can be used to deliver meaningful ads that speak to users. 

First-party data, on its own, allows you to build stronger customer relationships over time. When expanding to brand awareness and prospecting activities,  that same data provides insights on trends and patterns about your existing audiences. Enhancing these with machine learning and lookalike modelling can help brands uncover new customers and open up new engagement opportunities across the funnel.

This valuable information can then be used to develop targeting methods that appeal to a current or future customer’s specific interests.

2. Tailor your targeting

Aggregating first-party data from a range of touchpoints is only one step of the process; in order to personalise ads, marketers need to tailor their targeting. In other words, they need to analyse and supplement all the data they collect to better understand their consumers, which in turn will enable them to deliver more relevant ads and enrich their audience insights. 

Enhancing data across all channels is key to achieving this, but it requires supplementing first-party data with other data sets.

3. Supplement with data partnerships

Forming second-party data partnerships can lead to better measurement and tracking opportunities, as well as giving marketers access to a wider pool of potential customers. It helps streamline the data aggregation process, enabling them to pull together disparate data signals from a wide range of environments (i.e. CTV, display and social) and better connect with their consumers.

Data partnerships can still be done in a privacy friendly way by using data clean rooms. You will still own and govern your datasets while allowing it to overlap with others.

A good data partner will not only be able to expand a brand’s audience by merging its data with their own, but will also provide far more granular tracking opportunities, enabling marketers to accurately drill down and measure against specific KPIs (i.e. ROAS or ad performance).

4. Move to marketing mix modeling (MMM)

While attribution models such as event-level and aggregated reporting have benefits - including cross-service capabilities, accuracy and simplicity - they’re fundamentally inconsistent. There always seems to be a trade-off between privacy and utility, making it difficult to track ad performance in a way that hits on both these aspects.

The solution? Marketing mix modeling (MMM). 

As a data-driven statistical analysis that quantifies the sales impact and ROI of marketing activities, MMM is a great alternative to traditional attribution models.

MMM is customisable and privacy-focused. It is able to break data silos, and factors in all channels, as well as seasonality. This means it is just as effective as cookie-based tracking models and can help marketers analyse metrics such as ‘share of spend’ vs ‘share of effect’, as well as ‘adstock decay rate’ (which measures the prolonged or lagged effect of advertising on consumer purchase behaviour).


To be successful in a cookieless future, marketers will need to have the confidence to reach the same, if not wider audiences, and know how their ads are performing across the board.

Making those preparations now will make a big difference in how easily they pivot when the time comes, enabling them to spend less time worrying and more time maximising the marketing strategy.

For more information on cookieless targeting, check out Hybrid Theory’s recent whitepaper: Cookieless Attribution & Measurement.

By Marçal Serrate

Chief Technology Officer

Hybrid Theory