Google’s decision on third-party cookie deprecation—and what is still at risk for your brand.

Google's latest move doesn't signify a step back in prioritizing consumer privacy. Instead, it emphasizes giving users more individual control over their data. Similar to Apple's App Tracking Transparency (ATT) framework that rolled out in 2021, consumers will be given a more prominent opt-in/opt-out choice within Chrome. This functionality already exists within the browser’s settings, but will be surfaced in a “new experience” in the future, according to Google.

For brands who have not made significant progress in mitigating the impact of third-party cookie deprecation, this announcement might seem like a lifeline. However, even without a specific cut-off date from a centralized body like Google, there will still be a decline in use by consumers. With a gradual erosion as consumers opt out, the bigger danger is that many brands won’t realize that the third-party cookie pool is getting smaller and smaller, and therefore less useful for their ad strategy.

We expect the majority of third-party cookie signals to shrink, regardless of Google’s decision.

The digital industry has seen this scenario play out in the past, and the data shows the impact will still be huge, if just gradual. When Google switched to a third-party cookie for Google Analytics over ten years ago, Sayf Sharif, SVP Data, says that his analysis showed “some sites were losing over 80% of their traffic, depending on the industry, due to the adoption of ad blockers.”

This trend has repeated itself over the years; based on the impact from Apple’s ATT rollout, we’d expect to see cookies “capture maybe 15% of the available universe,” according to Liz DeAngelis, SVP Digital Strategy. Even if third-party cookies will continue to exist as an option within major browsers like Chrome, consumers have shown time and again that when made aware of their options, the majority will opt out.

Moreover, third-party cookies have proved increasingly ineffective in today’s digital landscape. Sharif points out, “We still face numerous challenges for measurement, activation and attribution (such as a high use of ad blockers, consent rules and fast cookie expiration), which make a focus on a cookieless approach to measurement and attribution a priority.” This shift to consumer choice underscores the reality that brands should continue to avoid over-reliance on third-party cookies.

Regulatory and consumer influences on third-party cookies helped shape Google’s decision.

The journey to Google's latest decision has been shaped by a blend of regulatory pressures and evolving consumer expectations. “Google has been caught in the crosshairs between evolving global privacy regulations and competition laws in a range of markets, most notably Europe,” says Benjamin Combe, Sr. Director, Data Optimization and Personalization. Similar regulations like the Australian Privacy Act have gained steam elsewhere, reinforcing that this is a global trend, not a regional or cultural one.

Meanwhile, consumer behavior has shifted toward greater consent and control over personal data. The move toward giving users the ability to set their preferences in Chrome, then, is well aligned with the experiences consumers seek online—and their changing attitudes and expectations toward digital privacy. Combe adds, “It merely reflects a more gradual end to a long-running, multi-factored trend. Google will no longer be the executioner, but third-party cookies are dying regardless—and their utility as the foundation of digital advertising’s targeting and attribution capabilities will not return.”

Still, cookies haven't been the only source of scrutiny in recent years. Google's Privacy Sandbox, a privacy-safe alternative to third-party cookie tracking, has faced several challenges since its announcement in 2020: the initiative has struggled with lack of adoption, anti-competitive scrutiny, conflicting industry feedback, mixed testing results and regulatory pressure. “Google’s Privacy Sandbox raised anti-competition issues with the UK’s Competition and Markets Authority (CMA), while simultaneously raising privacy concerns with the European Centre for Digital Rights and the UK’s Information Commissioner's Office,” Combe adds.

In short, both the regulatory landscape and consumer demand for greater data control led us here. So, what are brands supposed to do next?

Your brand’s first-party data strategies still need to evolve, or put your visibility and efficacy at risk.

Google's decision to give users control over third-party cookies rather than enforcing a complete deprecation has different implications depending on where brands stand in their preparation journey.

For businesses who may have used previous postponements of third-party deprecation as an excuse to delay action and conserve their resources, Tyler Stewart, Media Solutions Architect Lead, sees challenges down the line: “Smaller businesses may not have had the luxury of being on the front foot. In the longer term, this may only widen the gap between haves and have-nots as larger enterprises find themselves better positioned to compete in the privacy-first future.” Our advice to them: start prioritizing a cookieless approach now by focusing on first-party data and robust measurement strategies. Investing in AI-powered solutions and privacy-preserving technologies remains critical for future-proofing your marketing efforts.

Brands that have already embarked on their third-party cookie deprecation and privacy roadmap initiatives, meanwhile, have no need to pivot. “Strategies like the judicious use of first-party data, consent management, modeled measurement solutions and conversion recovery mechanisms will continue to be future-proofed strategies worth investing in,” says Stewart.

If you’re in this camp, don’t feel as if your efforts were in vain. “Those that have invested in reducing the impact of third-party cookie deprecation should take pride in being ahead of the curve with respect to utilization of first-party data, increasing compliance with global privacy regulations, innovating in measurement capabilities, and respecting their customers’ preferences,” says Combe. Staying the course will help future-proof your business’s data as the industry standards continue to evolve.

Better solutions for measurement will be customized for your business.

As an industry, the fragmentation and complexity we’re seeing across the digital ecosystem indicates we’re unlikely to move back to a uniform standard. “If you want to reach your customers wherever they are digitally, you need to be looking for new solutions for targeting, buying, and measurement. We can no longer rely on a consistent tactic that the entire industry adopts; brands need to move on from awaiting the next cookie alternative, and work on the solutions that are best for your company,” says DeAngelis.

The right strategy for your brand will depend on the complexity of your digital footprint and the data that’s most valuable for you to capture. To measure efficacy of your marketing activity, an important first step is to establish server-side tracking for your advertising, and take advantage of any event APIs from ad platforms, such as Meta’s Conversions API (CAPI). But in the long run, deterministic (user-level) measurement models will continue to weaken over time. Probabilistic models that assess changes across your entire business and media mix for causal contribution will be a necessity in the future, not an option. Strategies like Market Mix Modeling (MMM), or a Cookieless Multi-Touch Attribution (MTA) model offer viable alternatives to those challenges.

Similarly, identity resolution and user graph technologies are still viable for targeting, but a clear winner has yet to arise across the many providers that brands can choose from. As part of the announcement, Google emphasized that Privacy Sandbox will continue to be supported and developed as brands look ahead toward adapting their strategies beyond third-party cookie reliance—a goal that will remain important should users choose to opt out of third-party tracking en masse.

Move forward with a privacy-first marketing strategy.

No matter where your brand stands on the spectrum of cookie deprecation readiness, the path forward remains clear: continue to prioritize privacy-first strategies and the development of robust first-party data practices.

While third-party cookies have a new lease on life for now, they will never be as functional as they once were. They have already been deprecated in most non-Chrome browsers, and with Chrome indicating it will implement greater user permissions and controls, their availability is likely to continue declining—think of opt-in rates for ATT on iOS as a comparable scenario.

Brands should see this as an opportunity to stay ahead of the curve by continuing to invest in first-party data practices, consent management, and alternative measurement solutions—for teams that need advisory and executional support here, our data experts are ready to talk. The shift towards a privacy-first future is inevitable, and those who adapt proactively will be best positioned to thrive.

Google's Meridian is built upon the foundation of the previously released RBA/LMMM materials. The developments include geo experiments to ingest into the modeling, as well as detail on reach for YouTube. The emphasis on triangulation via A/B testing to enhance MMM accuracy is a strategy we are well-versed in ourselves and offers a good base to start from. However, it is crucial to note that while Meridian provides a step forward in measurement, it remains just a tool—a sophisticated one that requires expert hands to wield effectively. 

At Media.Monks, we pride ourselves on our robust internal platform that is industry-leading in terms of speed and functionality. Meridian gives a step up for brands who are just starting off in their MMM journey, helping them move away from last click to better quantify media uplifts.

From an experienced modeler’s perspective, these are some of the key points to consider with Meridian:

• The methodology behind Meridian is solid and makes sense around the emphasis on triangulation, which enhances the accuracy of the results.
• However, experienced econometricians will be essential for operating Meridian effectively in-house. Brands must ensure their teams possess the expertise to source the right data, build the models to reflect the real world, and translate data insights into actionable ROIs and response curves, or they risk making flawed decisions from the outputs.
• As with all MMM initiatives, data quality remains a critical factor in whether or not you’re adding value or making accurate decisions. Having accurate and full data across all drivers of sales (media, price, promotions, seasonality, climate, etc.) is critical for MMM. Strong data foundations also gives a significant advantage, whether brands are utilizing Meridian or any other technology.
• Effective communication within organizations is key to driving traction and implementation of MMM strategies, and explaining models clearly and effectively is key for any MMMs success.
• The launch of Meridian represents a shift away from outdated attribution models towards a more accurate, incremental media valuation approach. Even if it isn’t the best-fit tool for all brands, it is another step in the industry’s maturation, especially in the wake of cookie deprecation and changing privacy legislation.
• Smaller clients with simpler data structures, such as ecommerce clients spending less than $2 million USD on digital media, will benefit from this tool as an entry point to the world of MMM.
• Some clients may question running their media measurement on a platform from a media owner

In conclusion, Google's Meridian offers a solid starting point for less complex brands looking to enhance their measurement capabilities via a framework. Increasing the usage of MMM can only be good for the industry as a trusted tool to measure and optimize media. That being said, hard work is still needed in attracting econometric talent into the marketing world to maintain model accuracy and increase adoption of these methodologies. At the end of the day, a model is only as good as its modeler: you can have the best model in the world, but if it's not fed with accurate, high-quality data or delivered clearly to key stakeholders, it's not going to be trusted (and therefore, adopted) in an organization.

A good step forward, but still more to do on the talent front. See our post on apprenticeships to learn what we are doing to address this.

For more information on how we can help with your marketing effectiveness measurement or Market Mix Modelling, visit our Measurement page or contact us.

1. Will the measurement show the incremental uplift of media?

If the MMM models are measuring media but don't include the impact of other factors—such as Covid, seasonality, economic effects, etc.—then it is not providing you with an incremental measure and the media effects will be overstated. 

Always ask what factors other than media will be included in the model, and the sources of the data they use, to ensure your results are as accurate as possible.

2. What period of time does the model cover?

MMM needs at least two, preferably three, years of data to ensure it is deriving an accurate measurement of media and not conflating it with factors such as seasonality or other longer term impacts such as economic movements. If you are getting results with a lookback window of three months, then it's very unlikely to be MMM and therefore it will not be incremental measures you receive. 

Ask how much historical data the provider will require. 

3. What is the KPI that is being modeled?

Ask what the “dependent variable” will be. This is the KPI that is being modeled, and should be the metric on which your business success is judged. A sales metric—such as acquisitions, sales volume, revenue or similar—is ideal, as you can convert uplifts into revenue, then use margin to get to profit which enables you to assess true payback to the business bottom line.

If it is just web visits or digital conversions, alarm bells should be ringing! 

4. How are you dealing with interactive channel effects?

Any model needs to be reflective of how things work in the real world. For example, brand media can drive consumers to search for your products or services, which then drives up paid search. This needs to be accounted for correctly in the model specification, as well as any synergistic effects between channels and media’s ability to drive online and offline sales. If these are not accounted for, it’s probably not proper MMM. 

Ask how interactive media effects are taken into account.

5. How are you testing for causality, collinearity and significance?

These sound like complex terms, but they are not as scary as they seem!

Causality states the directionality of impact, i.e. which way something impacts something else. For example, does brand media drive consumers to search for a brand or does volume of searches impact brand media performance? There are certain econometric tests that can be done, which help determine this and validate your results. 

Ask for a list of all the possible data variables they would like to include in the model as well as the processes they will use to determine causality. 

Collinearity occurs when two factors move in a similar way and it becomes difficult to separate their impact, e.g. if TV and radio were planned with a constant weight over the same four weeks, an MMM model would struggle to determine the impact of each of these separately. Occurrences of collinearity can be tested for and should be flagged by the modeler.

Ask what kind of tests the modeler will use to determine collinearity.

Significance tells the modeler how important each of the factors are in the model. You need to be careful when there is low significance (usually on low spending media channels), as this is where the modeler cannot be confident in the result—which should be flagged to the client. 

Ask at what statistical level media is considered, and how the modeler will flag lower measures.

6. What is your verified forecasting error?

To establish a verified forecasting error, information about how the KPI has performed over a period of time is “held back” or not revealed to the modeler. The modeler needs to then use their analysis to “forecast” what they expect the KPI results to be. The forecast can then be compared to actual sales to verify the accuracy of the model.

The aim should be to have an error no greater than 8%, with a sensible range being between 2% and 8%. Non-incremental models (e.g. last click or attribution models) are poor at forecasting.

Ask if they have any validated forecasts from previous clients.

Hopefully this has helped to give a steer on what you need to be looking for. When in doubt, rely on these simple questions to get a sense of whether your partner is robust and reliable.

For more information on how we can help with your Marketing Effectiveness measurement or Market Mix Modelling, visit our Measurement page or contact us.

But how do you ensure marketing effectiveness measurement is robust, defendable and clear? Here are some top tips for you to consider.

It has to start with objective alignment!

The first step is to be clear on your objectives: what is the campaign’s purpose? Is it to drive up knowledge of the brand? Or is it a reminder to purchase again? Are we trying to increase the reliance on online as its higher margin? By being clear about what you want the campaign to do, and to which audience, you are starting to define which metrics you should be measuring against, and therefore the definition of the KPI you should track against (for example, increasing awareness if your goal is to increase brand knowledge). This article by data guru Avinash Kaushik is worth a read on further defining the right KPIs.

Set realistic targets!

Once you’ve chosen the KPI, you will need to set realistic expectations of how you expect the campaign to move the KPI. For this, look to the past to see how much the metric has moved; if there hasn't been much variation, then perhaps you shouldn’t expect a huge increase. If there is a lot of movement, what looks realistic in terms of uplift? Make sure you consider seasonality. For example, look at the three-year pattern: are there certain times of the year that are always up? If so, take that into account.

How will you measure?

With targets set, you will need to think about how you are going to measure the campaign before you deploy it. Some techniques include:

• Random control tests (RCTs) and geo testing
• A/B tests
• Measuring against a historic baseline
• Multi Touch Attribution (MTA)
• Market Mix Modelling (MMM)

Knowing which technique you will use helps you define the shape of the campaign. Knowing which technique you will use helps you define the shape of the campaign. For example, if you undertake geo testing, you will need to identify the most appropriate geographical area for your activity to occur within and a comparable area to use as a control. Meanwhile, for MMM, you will need to ensure you have sufficient media spend levels and variation to enable you to get a read on the impact.

Make sure the test spend has the following key attributes:

• Is there enough spend to move your KPI?
• Is it shaped so you can get as clear a read as possible (i.e. bunch it up, don't spread it out)?
• Is it at a time that will conflate with other impacts?

Execute and measure.

Once you have the right objectives and metrics, know which measurement method you are going to use, and the campaign is successfully deployed, it is time to then execute and measure. When it comes to measurement, be conscious of limitations of your measurement technique.

RCTs, geo and A/B approaches are easy to deploy, simple to understand and can be deployed internally. However, there are some limitations to these techniques, which can prevent them from giving a full read on the effectiveness of your activity.

First, there's difficulty getting a read on the "carryover" of the campaign (often called the memory effect), which is the effect the campaign continues to have after the campaign has finished. These approaches also struggle to measure the impact of specific media activity onto other channels; for example, running social activity can boost PPC. These findings are key when trying to understand a full view on your media performance.

These methods are also unable to provide information on scaling the results. A test spend of £20k in one region will not have the same ROI as a £5M national campaign. Be aware of diminishing returns. You can get around this by upping the investment in increments and continuing to test.

MTA is great at giving detail, can be relatively easy to set up, and gives you a good relative read. It is not, however, an incremental analysis, so it is not reliable for ROI calculations.

MMM is incremental and includes a full read on all drivers of your KPI. However, it is blunt (you need to spend at least £100K on a campaign), does not give as granular detail as other techniques, and can be expensive.

Considerations for in-housing measurement.

So, as a client what could you do yourself?

• RCTs, geo and A/B tests: Most of the time, there’s no real need for external partners.
• Multi Touch Attribution: Give it a crack, it’s fairly straightforward and you can use techniques like Markov chains. But you can only use traditional MTA for a short while: with third-party cookies going away, there is a longer term need to invest in cookieless solutions.
• MMM: Great to in-house if you have the scale, but you need to keep a team busy and fulfilled. This, therefore, only works if you are either an enterprise or global company spending over £100m on media.

Be aware of a couple watch-outs for in-housing.

• Don’t use data scientists for MMM—use econometricians. We’ve seen time and time again that where data scientists have been tasked with building MMM models, it very very rarely works. Data scientists and econometricians see things in different dimensions.
• Econometricians are hard to find, and harder to keep hold of. You need to make sure the work is varied and interesting for them to stick around.
• Make sure there is enough work for at least four people. Otherwise, if you are reliant on one econometrician and they leave, it's very hard to get someone else in to pick things up.
• Career progression: A consultancy can always get bigger, but an in-house enterprise team hits a limit. So then you have no progression and people leave. Or you move them into non-econometric roles but then you still have the problem with recruiting in a niche field.
• Method stagnation: There is less opportunity from your econometricians to learn new techniques from larger teams working on other clients. So there is a danger that your capability starts to fall behind the curve—unless you have really high staff turnover, which you’ll need to have a big enough team to support.

However, if you are large enough and have the right team, then in-housing can save a lot of future money on consultants, whilst keeping complete control of your data and models. To keep it moving, and to make sure you don't stagnate, consider using an external partner to help refine your MMM approach.

Summary

In conclusion, getting the start nailed down is critical in marketing evaluation. Align objectives with KPIs and how you lay down the campaign. Think carefully about which technique you will use to measure, and whether you do that internally or with external help. In these uncertain economic times, there has never been a greater need to get this right. Bon chance!

For more information on how we can help measure drivers of growth for your business visit our Measurement page or contact us.