Is MMM the Key to Cookieless Attribution?

Evolution of MMM and Digital Attribution

Marketing Mix Modeling (MMM) has roots dating back to the 1950s–1960s, when advertisers began using statistical models (often regression-based) to measure the impact of marketing tactics on sales.

MMM gained popularity in the 1980s as a way to guide big-budget media decisions (TV, print, etc.) before digital data was widely available. In the early digital era (1990s–2000s), attribution shifted toward user-level tracking.

“Marketing mix modelling is not merely about measuring spend – it is about uncovering the underlying drivers of brand success and optimising the entire customer journey.” – Nielsen

The introduction of the web cookie in the mid-90s enabled digital attribution models like “last-click” tracking, where credit for a sale is given to the last ad or channel that a user clicked.

Over time, more sophisticated multi-touch attribution (MTA) emerged in the 2010s, leveraging third-party cookies to follow individual users across sites and assign partial credit to multiple ads in a customer’s journey. However, these user-level models became heavily reliant on cross-site tracking cookies and device IDs.

Impact of Privacy Regulations on Cookie-Based Tracking

Recent years shown how privacy laws have significantly disrupted cookie-based tracking. The EU’s General Data Protection Regulation (GDPR, 2018) and California’s CCPA (2020) require user consent and give consumers the right to opt out of data sharing, which has made it harder for companies to drop tracking cookies freely.

Many users now decline cookies via consent pop-ups, resulting in gaps in data for marketers. Regulators have also issued hefty fines for improper cookie practices (e.g., Google was fined €50M for GDPR cookie violations), underscoring the legal risks of traditional tracking. In parallel, browsers and tech platforms enacted their own privacy measures: Apple’s Intelligent Tracking Prevention (ITP) on Safari (2017) and Firefox’s Enhanced Tracking Protection block third-party cookies by default.

Apple’s iOS14 update (App Tracking Transparency, 2021) made app tracking opt-in, causing a major drop in observable user data for advertisers on those devices. Google announced plans to phase out third-party cookies in Chrome (the dominant browser) – often dubbed the “cookiepocalypse” – originally by 2022, now delayed to late 2024.

These changes collectively shrank the data available from cookie-based tracking and put multi-touch attribution models in jeopardy. As a result, marketers began re-evaluating older, aggregate approaches like MMM that don’t rely on personal identifiers.

Transition from Third-Party Cookies to Alternative Methods

With third-party cookies on the way out, the industry has been transitioning to privacy-friendly alternatives. One major shift is toward first-party data – information collected directly by the company (e.g. on its own website or app). Unlike third-party cookies, first-party cookies (set by the site itself) and logged-in user data are still allowed, so companies are investing in capturing emails, loyalty memberships, and server-side analytics to track customers with consent.

“Cookieless analytics invites us to reimagine our data strategy, harnessing first‐party data and ethical insights to build resilient, privacy‐compliant campaigns.” – Forrester

Another approach is contextual targeting and attribution, which uses page context or aggregated trends rather than individual profiles. Google’s proposed Privacy Sandbox is introducing APIs like the Attribution Reporting API, which will let advertisers measure conversions in aggregate without identifying individual users.

Similarly, Google’s Topics API and FLEDGE aim to replace behavioural targeting with cohort-based methods.

Fingerprinting (using device characteristics to recognise users) exists as a workaround, but it’s generally disfavored by regulators for its privacy intrusiveness.

Because no single replacement has emerged as a standard, we now see a patchwork of solutions: from server-side conversion tracking (e.g. Facebook’s Conversion API, Google’s Enhanced Conversions) that send hashed first-party user data to ad platforms, to data clean rooms where publishers and advertisers can match data in privacy-safe ways. Importantly, the resurgence of MMM is part of this transition – MMM uses only aggregated data (e.g. total spend and sales by channel), making it inherently privacy-friendly in a cookieless world.

In fact, when Apple’s changes “left digital marketers in the dark,” many turned back to MMM as a solution from the pre-cookie era that could work again under new constraints.

Current Industry Trends & Statistics

Adoption of MMM and Cookieless Tracking

“In a world where traditional tracking is waning, transitioning to cookieless attribution empowers marketers to capture the holistic customer experience with greater accuracy.” – Harvard Business Review

The tightening of privacy has led to a notable uptick in MMM adoption and interest. Meta (Facebook) reported an 80% increase in MMM adoption among the companies it works with from 2021 to 2022 (Meta Marketing Mix Modeling Summit Highlights – Recast), attributing the surge to advertisers seeking alternatives after iOS14 limited mobile tracking. Industry surveys likewise show a resurgence: almost half of senior marketing leaders are now turning to MMM to optimise media spend in the face of privacy challenges. This represents a reversal from a decade of decline in MMM usage.

At the same time, marketers are preparing for a “cookieless future” by adopting new tracking methods. According to Adobe’s 2024 Digital Trends study, 51% of marketers had started investing in cookieless measurement tools by late 2023, and the reliance on third-party cookies is steadily dropping (‘Considerably less ready’: Marketers’ post-cookie preparedness has dropped by 23% since 2022 – Digiday).

However, many admit they are not fully ready: only 33% of marketers feel well-prepared for the loss of third-party cookies (per a 2024 YouGov survey).

In fact, Adobe’s data shows the portion of brands saying they’re “mostly/very ready” for cookie deprecation fell from 78% in 2022 to 60% in 2024, even as cookie usage declined – indicating ongoing anxiety about measurement gaps.

Performance Comparisons – Cookie-Based vs Cookieless Solutions

Early evidence suggests that while cookieless approaches can work, there are performance trade-offs. In digital advertising, loss of cookies has led to less efficient targeting and attribution: for example, ad impressions on Safari (which blocks cookies) see CPMs ~60% lower than on Chrome with cookies (advertisers pay less because targeting is broader and measured outcomes are fuzzier) (Cookieless Future: Marketing News & Updates | ROI Revolution).

Similarly, when Chrome trialed 1% of users in a cookieless mode, ad prices for that group were 30% lower than for cookied users. This implies traditional cookie-based tracking did enable advertisers to bid more confidently, whereas without cookies some conversion tracking and audience precision is lost. On the attribution side, studies show that if no alternative is in place, dropping cookies can severely undercount conversions.

For instance, one analysis found that 75% of marketers were still at least “moderately reliant” on third-party cookies as of early 2024, and many who tested cookieless attribution felt the current solutions “lack maturity” in replacing the detail that cookies provided.

The good news is that advanced modeling can close much of this gap: Google reported that its machine learning conversion modeling (as used in Google Analytics 4’s Consent Mode) can recover over 70% of ad-click-to-conversion journeys that would have been lost due to users opting out of cookies (What is Conversion Modeling in Consent Mode by Google?).

In other words, through probabilistic modeling and first-party data, marketers can retain a large portion of attribution signal – albeit not 100%. Many platforms are also leveraging longer look-back windows and identity graphs to link touchpoints.

For example, Facebook now uses Aggregated Event Measurement to attribute some conversions even without third-party cookies, and some third-party tools claim 30–40% higher attribution of conversions versus native platform data by using cookieless tracking methods (Stiddle | Frequently Asked Questions | The Better Customer Data Platform).

Overall, traditional cookie-based tracking is still considered the most granular, but the gap is closing as cookieless solutions improve. The industry is closely watching metrics like ROI and ROAS under new measurement regimes. In one survey, 75% of respondents still believed their marketing performance depends on third-party cookies (down from 82% in 2020), highlighting both the challenge and the progress in moving beyond cookies.

“Embracing a cookieless future is not a setback but an opportunity to innovate, ensuring that digital strategies remain both effective and respectful of user privacy.” – Google Analytics

Insights from Studies/Reports

Multiple recent studies underscore these trends. An Adobe 2024 report found that 49% of marketing strategies still relied on third-party cookies, down from 75% two years prior – a dramatic shift as companies experiment with first-party data and modeled attribution.

Yet paradoxically, confidence has eroded, suggesting many tests haven’t fully met marketers’ needs. The same study noted that only 28% of surveyed marketers still allocate at least half of their ad spend to cookie-based targeting (down from 45%), as budgets shift to walled gardens (platforms like Facebook, Google, Amazon that use login data) and other tactics.

In fact, 62% of respondents said they plan to redirect spend to walled gardens, and 49% are prioritising activation of first-party data. Another statistic shows 53% of digital marketing campaigns in 2023 were still leveraging third-party data, indicating that nearly half have moved to other data sources.

Meanwhile, 69% of media buyers expressed deep concern that a sudden privacy or tech change could disrupt their data strategy – a clear sign that marketers are wary and seeking more durable measurement methods. On the MMM front, studies by Deloitte and others note that roughly 50% of large advertisers are now using or testing MMM as part of their analytics mix.

Meta’s open-source MMM tool “Robyn” has been downloaded by many brands, and Google has been actively partnering with firms to modernise MMM (e.g., Google teamed with Ipsos MMA to help Subway use MMM for cross-media optimisation (Subway Maximizes Impact of Marketing Mix Modeling – Ipsos MMA).

The consensus in industry reports is that unified measurement approaches are rising – combining MMM’s big-picture view with more granular experiments – to compensate for the loss of user-level tracking.

In summary, current data shows marketers rapidly adapting: reducing dependence on cookies, adopting MMM and other models, but still navigating some performance challenges as the ecosystem shifts to privacy-first analytics.

Available Platforms & Solutions

With these changes, a variety of platforms and solutions have emerged (or re-emerged) to help marketers measure performance without third-party cookies. Broadly, solutions fall into two buckets: Marketing Mix Modeling providers and Cookieless analytics/attribution platforms (including new multi-touch attribution tools and first-party data platforms). Below is an analysis of leading options, their features, and suitability:

1. Nielsen – Marketing Mix Modeling (MMM) Platform. Nielsen is a longtime leader in MMM, offering deep experience in econometric modeling for large advertisers. Nielsen’s solution uses aggregated spend and sales data across media channels to estimate the contribution of each channel. A major selling point is its comprehensive offline and online coverage – Nielsen can incorporate TV, radio, print, digital, etc., making it ideal for omnichannel brands. It traditionally operated as a consulting service (with analysts building models for clients), though has been modernising with software tools. Nielsen participated in Meta’s 2022 MMM summit as a key partner (Meta Marketing Mix Modeling Summit Highlights – Recast), underscoring its role in the resurgence of MMM.
   Unique Selling Points (USP): decades of benchmarking data, trusted brand name, and scenario planning tools that let marketers simulate budget allocations.
   Technology: Regression-based econometric models, now often enhanced with machine learning for faster updates.
   Pricing: Nielsen’s MMM is typically enterprise-level – costs can run into six figures annually for continuous modeling, scaled to the size of the business and number of models. It’s most cost-effective for large companies with sizable media spend (millions of dollars), since Nielsen emphasises that the benefits of MMM should outweigh its cost, which tends to be true for larger brands (Marketing Mix Modelling is back: so what’s changed? – The Media Leader) (Marketing Mix Modelling is back: so what’s changed? – The Media Leader).
   Suitability: Best for enterprise and big-budget brands (e.g. CPG, retail, financial services) that need a holistic view and can afford expert support. Agencies handling very large clients might also use Nielsen’s services. Nielsen publishes case studies showing ROI improvements – for instance, its MMM helped a fast-food brand identify that combining certain ad formats doubled ROI (Subway Maximizes Impact of Marketing Mix Modeling – Ipsos MMA).
2. Analytic Partners – MMM and “Unified Measurement”. Analytic Partners is an independent analytics firm (now often ranked as a Leader in marketing measurement by Gartner/Forrester). They offer a platform called GPS Enterprise that performs MMM with more frequent updates and even integrates digital attribution and experimentation (hence “unified” measurement).
   USP: Emphasis on adaptive modeling (using AI to update MMM results quarterly or even monthly rather than once a year) and proprietary techniques to account for factors like brand equity and macro trends. Analytic Partners often highlights its ROI Genome – a database of marketing performance benchmarks to contextualise a client’s results.
   Technology: A mix of statistical models and AI; they often incorporate machine learning to refine attribution and even provide forward-looking optimisations.
   Pricing: Enterprise SaaS/consulting hybrid – typically a monthly or annual fee based on scope, often more flexible than Nielsen’s. They can cater to mid-size businesses as well, but still generally a significant investment.
   Suitability: Large brands and agencies; also some B2B or SaaS companies have used unified models from Analytic Partners if they have enough data. (Analytic Partners’ clients have included telecoms, banks, etc., where understanding both online and offline drivers is key.)
3. Ipsos MMA (Marketing Management Analytics) – MMM Specialist. Ipsos MMA is a branch of Ipsos (research firm) focused on MMM and attribution. They have a platform and team that recently partnered with Google for advanced MMM. A notable case study is Subway, which used Ipsos MMA’s modeling to get granular insights (e.g., finding that layering YouTube bumper ads on TrueView ads yielded 2× higher ROI than TrueView alone) (Subway Maximizes Impact of Marketing Mix Modeling – Ipsos MMA).
   USP: Strong integration of granular digital data into MMM – Ipsos MMA has pushed innovations to make MMM more actionable at a campaign level (they could isolate performance of specific ad formats, which historically was hard in MMM) (Subway Maximizes Impact of Marketing Mix Modeling – Ipsos MMA). They also tout “privacy durable” measurement (MMM’s advantage in a cookieless world) (Subway Maximizes Impact of Marketing Mix Modeling – Ipsos MMA).
   Technology: Advanced regression models with option to include long-term effects modeling (as Subway did to measure brand metrics impact over years (Subway Maximizes Impact of Marketing Mix Modeling – Ipsos MMA)).
   Pricing: Enterprise-level; likely project fees or subscriptions that scale with the complexity (they might offer modular services).
   Suitability: Retail, restaurant, and multinational brands that need to allocate mix across many markets/channels; Ipsos MMA often works with clients that have both offline sales and digital ads.
4. Meta’s Robyn (Open Source MMM) – Not a platform per se, but worth noting: Robyn is an open-source MMM code library released by Facebook (Meta). It allows companies (often with a data scientist on staff) to build their own marketing mix models using Python/R. Meta saw huge interest here, with many advertisers adopting Robyn in 2022 (Meta Marketing Mix Modeling Summit Highlights – Recast). Robyn’s release validated MMM as “reliable and accessible” to a wider audience. Some vendors (like Adjust, AppsFlyer, and Supermetrics) are even incorporating Robyn into their products (Meta Marketing Mix Modeling Summit Highlights – Recast).
   USP: It’s free (aside from internal resource cost) and privacy-safe (uses aggregated data).
   Technology: Robyn uses Ridge regression and evolutionary algorithms to auto-fit an MMM with adstock/lag effects.
   Pricing: Free to use, but you need capabilities to implement and maintain it.
   Suitability: Mid-sized advertisers or agencies with analytics talent who want a do-it-yourself MMM solution. It’s also used as a starting point for those experimenting with MMM before investing in a paid platform.
5. Google Analytics 4 (GA4) – Cookieless-Friendly Web Analytics. GA4 is Google’s latest analytics platform, designed with a “privacy-centric” approach. It uses event-based tracking (as opposed to session/cookie in Universal Analytics) and can function without cookies if needed by using modeling. GA4’s conversion modeling fills in gaps when users don’t consent to cookies: Google uses aggregated data and behavior patterns to infer conversions that could not be directly observed. This allows GA4 to report more complete attribution than a naive cookie-less setup. Google claims that using its Consent Mode with GA4 can recover over 70% of conversion data lost due to cookie consent being denied (What is Conversion Modeling in Consent Mode by Google?). Features: Cross-device tracking via Google signals (if users are logged into Google), integration with Google Ads for modeled conversions, and data-driven attribution models (which use AI to assign fractional credit to touchpoints).
   USP: Ubiquity (widely used, free for standard version), built-in machine learning for insights and anomaly detection, and compliance tools for GDPR (consent mode).
   Pricing: Free for most users (GA4 standard), and a paid GA4 360 for enterprise with higher data limits and support.
   Suitability: Virtually all business types – from small websites to large e-commerce – use GA4. It’s especially handy for those who need quick adoption of a cookieless measurement (just switching settings on GA4 can enable modeling). However, GA4 primarily tracks on your own site/app; it doesn’t automatically solve multi-touch across ad platforms (it will credit channels it knows about, often via UTM tags or linked Google Ads).
6. Adobe Analytics – Enterprise Analytics with Cookieless Capabilities. Adobe’s analytics suite (part of Adobe Experience Cloud) similarly has adapted to a world with less cookies. Adobe uses a concept of first-party IDs (organisations can define a visitor ID that might be based on login or other stable identifier). It also offers Server-side tracking and integration with Adobe’s Experience Platform, which can reconcile identities via email or CRM data (with user consent). Adobe has promoted its ability to do analysis on “authenticated” traffic and use data stitching to connect user interactions that were previously fragmented by cookie loss (‘Considerably less ready’: Marketers’ post-cookie preparedness has dropped by 23% since 2022 – Digiday). Many Adobe clients also leverage its customer journey analytics to piece together cross-channel paths using first-party data merges. USP: Highly customisable and powerful for those with data engineering resources; it can ingest offline data and support very tailor-made attribution models.
   Pricing: Enterprise only – typically very costly (licenses can be in the tens to hundreds of thousands annually).
   Suitability: Large enterprises, especially those already in the Adobe stack. Often used by retail, media, and finance companies that require robust analytics beyond Google Analytics limits.
7. Dedicated Cookieless Attribution Tools: A number of newer platforms specifically market themselves for cookieless, multi-touch attribution in a post-cookie world. These include:
8. Rockerbox – An attribution platform for DTC (direct-to-consumer) brands. Rockerbox centralises data from ad platforms and uses first-party tracking pixels to de-duplicate and attribute conversions. It focuses on being a “single source of truth” for marketing spend by ingesting channels like Facebook, Google, email, TV, etc. USP: It doesn’t rely on third-party cookies; instead it often uses UTM parameters and integrations to tie ad clicks to onsite conversions, and it can perform attribution even when platform pixels fail. Pricing: SaaS model, often tiered by ad spend or number of events (mid-range to high cost). Suitability: E-commerce and DTC brands, and agencies with such clients, that want an out-of-the-box multi-touch attribution solution.
9. Wicked Reports – A tool aimed at online retailers (Shopify stores, etc.) that tracks customer journeys via first-party cookies and email integration. It connects to CRM/email systems so that if a purchase is made later (even via email click), it can attribute back to earlier ad clicks. Wicked Reports uses a mix of attribution models (first click, last click, “attribution window” models) and claims to restore visibility lost after iOS14. Pricing: Starts a few hundred dollars per month for smaller businesses, scaling with contact or order volume. Suitability: Small-to-mid e-commerce who need better attribution than Facebook/Google alone can provide.
10. Hyros – An AI-driven attribution platform popular among online marketers (especially for tracking ads to sales in funnels). Hyros uses server-side tracking and unique identifiers (like email/phone) to match users across sessions and even across platforms. For example, it can capture a click ID from a Facebook ad, store it in a first-party cookie or local storage, then when a user purchases and provides an email, Hyros links that email back to prior ad engagements. USP: Aggressive claim of extremely high accuracy in tracking ads to revenue, boasting improvements in reported ROI by capturing what ad platforms miss. Pricing: Custom, often a monthly fee + setup fee; known to be on the higher side (targeted at businesses spending significant amounts on ads). Suitability: Advertisers with complex funnels (webinars, multi-step signups) and agencies who manage such clients.
11. Segment (Twilio) – Not an attribution tool per se, but a Customer Data Platform (CDP). Segment allows businesses to collect first-party events (web, app) and send them to various destinations. In a cookieless context, Segment is used to maintain a unified user ID (with consent), meaning all events (page views, clicks, purchases) get an internal identifier. Marketers can then attribute conversions by analyzing these events. Segment’s Personas module can do some multi-touch attribution and funnel analysis. USP: It’s developer-friendly and avoids third-party tags by using first-party data pipelines. Pricing: Tiered by volume of data – can be expensive as data scales. Suitability: Tech-savvy companies (SaaS, startups) that prefer building a tailored analytics stack; marketing agencies with strong analytics teams might also use Segment to feed data into custom attribution models.
12. AI-Driven Attribution Models: Many of the above platforms are now incorporating AI/ML to improve attribution. For example, Google’s data-driven attribution uses machine learning on conversion paths to assign fractional credit to touchpoints (essentially an AI model instead of a static rule). Facebook’s conversions API plus its modeled attribution is powered by AI that predicts offline conversions. Independent platforms like LeadsRx or Attribution App (by Adobe, formerly) have also touted AI that can churn through path data and find patterns (though some of these platforms struggled after cookies were curtailed). In general, AI-driven attribution aims to dynamically adjust how credit is given, based on training data about what sequences of touches lead to conversion. This can uncover non-intuitive credit (e.g., an early touch might get more credit if the model learns it’s highly predictive of eventual sale). The challenge is that AI models need a lot of data and ground-truth conversions to train on – something harder to get in fragmented, privacy-limited environments. Still, expect most major solutions to advertise “machine learning” in their attribution engine.
13. Stiddle: relatively new platform, positioned as “The AI-First Revenue Attribution Platform” (Stiddle | The AI-First Revenue Attribution Platform | Powered By IRIS). It’s designed to help advertisers adapt to a cookieless world by using advanced tracking and AI-driven attribution modeling. In essence, Stiddle combines features of a customer data platform (CDP), a multi-touch attribution system, and an analytics dashboard – with a focus on accuracy in linking ad spend to revenue. Stiddle’s attribution is powered by an AI engine called IRIS. The platform offers custom AI models trained on a client’s own data to determine how credit for conversions should be allocated (Stiddle | The AI-First Revenue Attribution Platform | Powered By IRIS). Unlike fixed-rule models (last-click, first-click, etc.), Stiddle’s AI models aim to “understand your customers” by learning from historical patterns. For those who prefer rule-based models, Stiddle can also apply standard attribution models – in fact, it mentions dynamic credit allocation based on one of three attribution models selected (Stiddle | The AI-First Revenue Attribution Platform | Powered By IRIS) (likely options are something like first-touch, last-touch, and a data-driven model). The AI can account for complex journeys; for example, if a customer touches Facebook, Google, and Email before buying, the model might learn how each typically contributes. Stiddle’s AI emphasises giving granular insight: it doesn’t just output channel-level results, it ties down to the customer profile level. An advertised benefit is the ability to see exactly which customer purchased and which ads they engaged with, thanks to the unified data Stiddle collects (Stiddle | Frequently Asked Questions | The Better Customer Data Platform). This is akin to having multi-touch attribution with a CRM-like database of users attached.

Pricing Models & Cost: The pricing for these solutions varies widely:

1. MMM providers (Nielsen, Analytic Partners, Ipsos, etc.) typically use enterprise pricing – either annual subscriptions or project-based fees. It’s not uncommon for a global MMM engagement to cost $100K+ per year, though smaller scale packages exist. The cost often depends on the number of models (by product line or country) and update frequency. Some newer SaaS MMM (like Cassandra, Recast) offer more affordable packages, perhaps starting in the low tens of thousands annually, by automating much of the modeling.
2. Cookieless attribution platforms (Rockerbox, Hyros, Wicked, etc.) mostly use SaaS pricing that scales with usage. Common models include pricing by monthly tracked users or events, by ad spend (e.g., a percentage of ad spend as fee), or by number of customer profiles. For example, a platform might charge a base fee plus a volume-based tier – Stiddle uses Monthly Active Profiles as its usage metric for billing (Stiddle | Pricing | The Better Customer Data Platform). Some tools targeting SMBs have entry plans around a few hundred dollars per month (Wicked Reports, etc.), whereas more comprehensive platforms or enterprise deals can run several thousand per month.
3. Agencies often get multi-client licenses or partner discounts. Many vendors have agency programs recognising that an agency might onboard 10+ clients. In Stiddle’s case, they offer volume discounts for agencies with 50+ clients and even a revenue-share commission for referrals (Stiddle | Pricing | The Better Customer Data Platform). This means an agency could resell the tool to clients and earn 20% of the SaaS fee as commission (Stiddle | Pricing | The Better Customer Data Platform).
4. Cost Comparison: Traditional cookie-based analytics like Google Analytics were often free/cheap, while new privacy-centric solutions tend to add cost. However, companies are justifying these costs by the savings in ad spend wastage. If a tool at $1,000/month can attribute conversions that let you optimise and save $10,000 in inefficient ad spend, it’s worth it. Case studies frequently cite ROI improvements that outweigh the measurement cost. For instance, fashion retailer Gina Tricot adopted an MMM SaaS and saw a +53% improvement in marketing ROI by reallocating budget optimally (How Gina Tricot improved its ROI by +53% by leveraging Cassandra MMM SaaS) – a huge gain that would justify the tool’s fees. Similarly, Subway’s use of MMM guided more effective media mix, increasing their online video ROAS by 1.8× since 2021 (Subway Maximizes Impact of Marketing Mix Modeling – Ipsos MMA). These examples illustrate why businesses are willing to invest in analytics despite tight budgets – better attribution drives better returns.

Suitability for Different Business Types:

• E-commerce & Direct-to-Consumer (DTC): These businesses benefit from cookieless multi-touch attribution tools that can track individual customers from ad click to purchase across web and email. Solutions like Stiddle, Rockerbox, Wicked Reports are heavily marketed to e-commerce brands (Shopify stores, etc.) because they can directly tie ad spend to revenue per customer. E-comm companies also use MMM in a lighter form to measure broader media like influencer or podcast ads that aren’t easily tracked by pixels. For large e-commerce (doing tens of millions in sales), a combination of MMM (for macro planning) and attribution platform (for granular day-to-day optimisation) is ideal.
• B2B & SaaS: B2B companies often have longer sales cycles and multiple touchpoints (whitepaper downloads, demos, etc.). They may use cookieless tracking to follow an account or lead through a journey. Attribution here might integrate with a CRM (to get when deals close). Platforms that integrate lead form tracking and CRM data are suitable. MMM is less common in B2B because data volumes are lower, but some larger B2B firms do use MMM to understand marketing impact on pipeline. Also, account-based attribution tools exist that don’t rely on cookies but on matching firmographic data (Terminus, Demandbase, etc. have attribution components for ABM).
• Mobile Apps & Gaming: With mobile ad tracking curtailed by Apple, app marketers have turned to aggregated measurement. AppsFlyer (a Mobile Measurement Partner) now offers an MMM solution and probabilistic attribution. Adjust (another MMP) also joined this space (Meta Marketing Mix Modeling Summit Highlights – Recast). These are specialized for high-volume app events and often use incrementality testing (holdout groups) to measure ad impact without device IDs.
• Agencies: Agencies often need flexible tools that can handle multiple client types. A platform like Stiddle markets an agency hub where you can manage multiple client workspaces under one login (Stiddle | Pricing | The Better Customer Data Platform). Agencies might use enterprise analytics (Adobe, etc.) for big clients, and use lighter-weight SaaS for smaller clients. Many agencies are also developing in-house MMM capabilities (even using open-source Robyn) to service clients in the cookieless era (Marketing Mix Modelling is back: so what’s changed? – The Media Leader). The right solution for an agency depends on its client mix: an agency with primarily DTC ecommerce clients might standardise on one of the ecomm attribution platforms, whereas a media agency for large brands might partner with an MMM provider or build one internally.
• Brick-and-Mortar Retail / CPG: These traditionally relied on MMM (from firms like Nielsen, IRI, etc.) because sales were offline. Now, even these companies are trying “digital-like” attribution by using loyalty cards or email to connect ad exposure to in-store purchase (with user consent). Data clean rooms from places like Google, Amazon, and retailers’ own shopper data programs allow some level of attribution without cookies (matching aggregated customer data). Still, MMM remains the primary method to measure marketing for offline sales at a national level. Case studies in CPG often show MMM guiding tens of millions in spend shifts leading to a few percentage points of sales lift – significant absolute gains.

FAQ

How do you implement cookieless analytics in your marketing strategy, including tracking customer behaviour?

In today’s digital marketing landscape, implementing cookieless analytics is becoming increasingly important. As privacy concerns grow, businesses need to adapt and find new ways to track customer behaviour without relying on traditional cookies. To implement a cookieless analytics strategy, consider the following steps:

1. First-Party Data Collection: Utilise first-party data, which is data that you collect directly from your audience. This can include data from website interactions, CRM systems, and email marketing. By focusing on first-party data, businesses can gain valuable insights into customer behaviour while respecting privacy.
2. Server-Side Tracking: Implement server-side tracking to collect data directly from your server rather than relying on client-side scripts. This method provides more control over the data collected and is less vulnerable to ad blockers and privacy restrictions.
3. Utilise Unique Identifiers: Use unique identifiers like hashed email addresses or user IDs to track user behaviour across different sessions and devices. This approach can provide a more holistic view of customer interactions.
4. Leverage Machine Learning: Employ machine learning algorithms to analyse patterns in user behaviour. These algorithms can identify trends and predict future behaviour without the need for cookies.
5. Behavioural Analysis: Focus on analysing user behaviour on your website, such as page views, session duration, and conversion rates. Use this data to understand user preferences and optimise the user experience.

How do you ensure and validate the accuracy of cookieless attribution for online campaigns?

Ensuring the accuracy of cookieless attribution in online campaigns is crucial for understanding the effectiveness of marketing efforts. Here are some steps to validate the accuracy:

1. Set Clear Goals and KPIs: Define clear objectives and key performance indicators (KPIs) for your campaigns. This provides a benchmark for measuring success and evaluating attribution accuracy.
2. Data Validation: Implement rigorous data validation processes to ensure the accuracy of the data collected. Regularly audit and clean your dataset to remove any inconsistencies or errors.
3. Use Multi-Touch Attribution Models: Employ multi-touch attribution models that consider multiple touchpoints along the customer’s journey, rather than relying on single-touch models like last-click attribution.
4. Cross-Channel Analysis: Analyse data from various channels to gain a comprehensive view of campaign performance. This helps in identifying discrepancies and ensuring accurate attribution.
5. Continuous Testing and Optimisation: Regularly test and optimise your attribution models to ensure they remain accurate and relevant. Adjust your models based on new insights and changes in customer behaviour.

How do you address privacy concerns and ensure compliance with data protection laws in cookieless analytics?

Addressing privacy concerns and ensuring compliance with data protection laws are paramount when implementing cookieless analytics. Here are crucial steps to achieve this:

1. Understand Legal Requirements: Familiarise yourself with relevant data protection laws such as GDPR, CCPA, and other local regulations. Ensure your analytics practices align with these laws.
2. Obtain Explicit Consent: Ensure you have explicit consent from users before collecting any data. Clearly communicate how their data will be used and provide options for them to opt-out.
3. Data Minimisation: Collect only the data necessary for your analysis. Avoid collecting excessive or sensitive information that could compromise user privacy.
4. Implement Anonymisation Techniques: Use techniques such as data anonymisation and pseudonymisation to protect user identities while analysing data.
5. Regular Compliance Audits: Conduct regular audits of your analytics processes to ensure ongoing compliance with data protection laws and address any emerging privacy concerns.

For more guidance on data protection and privacy compliance, you can visit ICO, the Information Commissioner’s Office in the UK, which provides comprehensive resources on data privacy.

What is the role of marketing mix modelling in modern business?

Marketing mix modelling plays a significant role in modern business by providing a data-driven approach to evaluate and optimise marketing strategies. Its primary contributions include:

Understanding Marketing Effectiveness

Marketing mix modelling helps businesses understand the effectiveness of their marketing efforts by analysing the impact of various marketing variables, such as advertising, promotions, and pricing, on sales and revenue.

Resource Allocation

By identifying which marketing activities deliver the best returns, businesses can allocate resources more efficiently and optimise their marketing budgets.

Scenario Planning

Marketing mix modelling enables businesses to simulate different scenarios and predict the potential outcomes of various marketing strategies, helping in strategic decision-making.

Performance Measurement

It provides a quantitative framework to measure marketing performance and assess the contribution of each marketing element to overall business success.

How do you evaluate and choose the right marketing mix modelling tools/software?

Choosing the right marketing mix modelling tools/software is crucial for effective analysis and decision-making. Here are steps to evaluate and select the best tools:

1. Define Your Objectives: Clearly outline your marketing goals and objectives. Determine what you want to achieve with the modelling tool, such as optimising ROI or understanding channel effectiveness.
2. Assess Data Integration Capabilities: Evaluate the tool’s ability to integrate with your existing data sources, such as CRM systems, sales data, and digital marketing platforms.
3. Consider Analytical Features: Review the analytical features offered by the tool, such as regression analysis, scenario planning, and predictive modelling. Ensure they align with your analytical needs.
4. Evaluate User-Friendliness: Consider the tool’s ease of use and user interface. It should be accessible to both technical and non-technical users.
5. Check Vendor Support and Training: Assess the level of support and training provided by the vendor. A reliable vendor should offer ongoing support and training resources.

How do you optimise marketing mix modelling for better results?

Optimising marketing mix modelling (MMM) entails refining the process to ensure that it accurately captures the effects of various marketing efforts on sales or other key performance indicators. There are several strategies and considerations that marketers can use to achieve this.

Data Quality and Integration: High-quality data is the foundation of any effective MMM. The data should be accurate, consistent, and comprehensive. It is essential to integrate data from various sources to capture a holistic view of marketing impacts. This includes incorporating data from digital platforms, traditional media, and any other channels used in marketing efforts.

Granular Data Collection: Collecting data at a granular level allows for more precise analysis. This means looking at data not just at a monthly level but potentially daily or weekly, depending on the dynamics of the market and the speed of sales cycles. Granularity helps in understanding the immediate impacts of marketing activities and allows for more agile adjustments.

Advanced Modelling Techniques

Using advanced statistical methods and machine learning techniques can enhance the accuracy of MMM. These techniques can handle complex interactions between variables and can adapt to changes over time. For instance, Bayesian methods can provide more flexibility and robustness in the face of uncertainty.

Regular Model Updates: The market environment is dynamic, and consumer behaviour changes over time. Regularly updating the model ensures that it remains relevant and accurately reflects current market conditions. This involves not only updating the data but also recalibrating the model to account for new variables or changing relationships between variables.

1. Incorporate External Factors: External factors such as economic conditions, competitive actions, and seasonal trends can significantly impact marketing effectiveness. Including these in the model can improve its explanatory power and predictive accuracy.
2. Scenario Planning: Using the model to run various scenarios can help in understanding potential outcomes of different marketing strategies. This can guide decision-making and help in resource allocation to maximise ROI.
3. Cross-Functional Collaboration: Collaboration between different departments, such as finance, sales, and marketing, can provide valuable insights and ensure that the model considers all relevant factors.

How do you measure the effectiveness of a marketing mix model?

Measuring the effectiveness of a marketing mix model is crucial to ensure that it provides reliable insights for strategic decision-making. There are several key metrics and methods used to evaluate the performance of an MMM.

Model Fit and Validation: A fundamental measure of a model’s effectiveness is how well it fits historical data. Statistical metrics such as R-squared, which indicates the proportion of variance in the dependent variable explained by the model, are commonly used. A higher R-squared value suggests a better fit.

Accuracy in Prediction

One of the primary goals of MMM is to predict future outcomes based on past data. The model’s effectiveness can be assessed by comparing its predictions against actual outcomes. Metrics such as mean absolute error and root mean square error are used to quantify prediction accuracy.

Incrementality Testing: This involves conducting controlled experiments to measure the incremental impact of marketing activities. By comparing outcomes with and without specific marketing interventions, organisations can validate the model’s predictions and refine its assumptions.

1. Return on Investment (ROI) Analysis: Evaluating the ROI of marketing activities as predicted by the model can help in assessing its effectiveness. A model that accurately predicts ROI enables better resource allocation and strategy optimisation.
2. Sensitivity Analysis: This involves altering input variables to test the model’s responsiveness and robustness. It helps in understanding how changes in marketing spend or external factors impact outcomes, indicating the model’s reliability in various scenarios.
3. Cross-Validation: This technique involves dividing the dataset into training and testing sets to assess the generalisability of the model. A model that performs well on unseen data is considered effective and reliable.

Additionally, real-time feedback from marketing campaigns, consumer surveys, and sales data can provide qualitative insights into model performance. By employing these metrics and methods, marketers can ensure that their MMM is not only effective but also adaptable to changing market conditions.

What are the key components of successful marketing mix modelling?

Successful marketing mix modelling relies on several key components that collectively ensure its effectiveness and reliability. Understanding these components is crucial for developing a robust MMM framework.

Comprehensive Data Collection: Successful MMM requires comprehensive and high-quality data. This includes data from various marketing channels such as digital media, television, print, and radio, as well as sales data and external factors like economic indicators and competitive actions.

Well-Defined Objectives

Clear objectives are essential to guide the modelling process. This includes defining what the model aims to achieve, such as optimising marketing spend, understanding channel effectiveness, or predicting future sales. Well-defined objectives ensure that the model remains focused and relevant.

Advanced Analytical Techniques: Employing advanced analytical techniques, such as econometric modelling, machine learning, and statistical analysis, is crucial for capturing complex interactions and dynamics within the data. These techniques enhance the model’s accuracy and predictive power.

1. Integration of External Factors: Incorporating external factors such as economic conditions, seasonal trends, and competitive actions is vital for a holistic understanding of marketing impacts. These factors can significantly influence consumer behaviour and marketing effectiveness.
2. Continuous Model Refinement: The market environment is dynamic, and consumer behaviour evolves over time. Continuous refinement of the model, including regular updates and recalibration, ensures that it remains relevant and accurate.
3. Cross-Functional Collaboration: Collaboration between different departments, such as marketing, finance, and sales, can provide valuable insights and ensure that the model considers all relevant factors. This collaboration enhances the model’s comprehensiveness and applicability.

How do you interpret results from marketing mix modelling?

Interpreting results from marketing mix modelling is a critical step in leveraging the insights generated by the model for strategic decision-making. Understanding the results involves a combination of statistical analysis and practical application.

Understanding Coefficients: The coefficients in a marketing mix model represent the estimated impact of each marketing activity on the outcome variable, such as sales. A positive coefficient indicates a positive impact, while a negative coefficient suggests a negative impact. The magnitude of the coefficient reflects the strength of the relationship.

Assessing Statistical Significance

Statistical significance is crucial in determining the reliability of the model’s results. P-values and confidence intervals are used to assess the significance of coefficients. A low p-value indicates that the effect of a marketing activity is statistically significant and not due to random chance.

Attribution Analysis: This involves attributing changes in the outcome variable to specific marketing activities. By understanding which activities drive the most impact, organisations can optimise their marketing strategies and resource allocation.

1. Scenario Analysis: Using the model to run various scenarios can help in understanding potential outcomes of different marketing strategies. This can guide decision-making and help in resource allocation to maximise ROI.
2. ROI Evaluation: Evaluating the return on investment of marketing activities as predicted by the model can help in assessing their effectiveness. A model that accurately predicts ROI enables better resource allocation and strategy optimisation.
3. Communication of Insights: Effectively communicating the results and insights from the model is crucial for organisational buy-in and implementation. This involves translating complex statistical findings into actionable business insights.

What are the best practices for cookieless analytics?

In the evolving landscape of digital analytics, the shift towards cookieless methods has become increasingly prevalent due to privacy regulations and changes in browser policies. To effectively navigate this shift, there are several best practices to consider for cookieless analytics.

First and foremost, it is crucial to adopt a privacy-first approach. This involves ensuring compliance with privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). By prioritising user privacy, organisations can build trust with their audience while also adhering to legal requirements.

Another best practice is to leverage server-side tracking. Unlike traditional client-side tracking, which relies on cookies stored in the user’s browser, server-side tracking allows data to be collected and processed on the server. This method not only enhances data security but also ensures more accurate data collection unaffected by browser restrictions.

Utilising first-party data is also essential in cookieless analytics. First-party data, collected directly from the audience, is more reliable and relevant than third-party data. Companies should focus on creating systems to gather, manage, and analyse first-party data effectively.

Implementing identity resolution technologies is another key practice. These technologies help in piecing together user interactions across multiple touchpoints without the need for cookies. By using deterministic and probabilistic matching techniques, organisations can create a seamless user experience.

Finally, investing in advanced data analytics tools is vital. Tools that incorporate machine learning and artificial intelligence can provide deeper insights into user behaviour by analysing patterns without relying on cookies. These tools should be capable of integrating with various data sources to offer a comprehensive view of the customer journey.

1. Adopt a privacy-first approach
2. Leverage server-side tracking
3. Utilise first-party data
4. Implement identity resolution technologies
5. Invest in advanced data analytics tools

How does cookieless attribution compare with traditional cookie-based methods?

Cookieless attribution represents a significant shift from traditional cookie-based methods, offering both advantages and challenges. Understanding these differences is essential for adapting to the changing digital landscape.

Traditional cookie-based attribution relies on tracking cookies stored in the user’s browser to follow their journey across various online platforms. This method, however, faces challenges due to increased privacy regulations and browser updates that limit cookie tracking capabilities.

In contrast, cookieless attribution does not depend on cookies to track user behaviour. Instead, it employs alternative methods such as server-side tracking, first-party data, and identity resolution technologies. These methods offer several benefits over traditional cookie-based attribution.

Benefits of Cookieless Attribution:

Firstly, cookieless attribution enhances user privacy by reducing reliance on third-party cookies. This aligns with privacy regulations and builds trust with users who are increasingly concerned about data privacy.

Secondly, cookieless methods offer more accurate data collection. By using first-party data and server-side tracking, organisations can ensure data integrity and accuracy, unaffected by cookie-blocking mechanisms in browsers.

Thirdly, identity resolution technologies used in cookieless attribution provide a more holistic view of the customer journey. By piecing together user interactions across multiple channels, organisations can achieve a unified customer profile.

Challenges of Cookieless Attribution:

Despite these benefits, cookieless attribution comes with its own set of challenges. Implementing and integrating new technologies can be complex and require significant investment in time and resources.

Additionally, cookieless methods may face limitations in scale compared to cookie-based approaches. Identity resolution, for example, may not always be able to match all user interactions seamlessly, leading to potential gaps in attribution.

What are the potential challenges and problems with cookieless analytics and attribution?

As the digital world transitions to a cookieless future, several challenges and problems arise with cookieless analytics and attribution. Understanding these challenges is crucial for organisations aiming to maintain effective data strategies.

Technical Complexity:

One of the primary challenges is the technical complexity involved in implementing cookieless solutions. Cookieless analytics requires a shift from traditional cookie-based tracking to alternative methods such as server-side tracking and identity resolution. This transition necessitates significant changes in infrastructure and technology, often requiring specialised skills and resources.

Data Integration:

Another challenge is ensuring seamless data integration across multiple platforms and touchpoints. Without cookies to track user journeys, organisations must rely on first-party data and identity resolution techniques. This can lead to potential gaps in data collection and difficulties in piecing together a comprehensive view of customer interactions.

Scalability Issues:

Scalability is a concern in cookieless analytics. While identity resolution technologies can provide a unified view of customers, they may not always scale efficiently, particularly for large datasets or complex user journeys. This can result in incomplete or inaccurate attribution models.

Privacy Regulations:

Privacy regulations and compliance pose additional challenges. As organisations adopt cookieless methods, they must ensure adherence to privacy laws such as GDPR and CCPA. Navigating these regulations while maintaining effective data collection can be complex and time-consuming.

Cost Implications:

Implementing cookieless analytics often involves significant cost implications. Investing in new technologies, training staff, and ensuring compliance with privacy regulations can be resource-intensive, particularly for smaller organisations.

In summary, while cookieless analytics offers a privacy-centric alternative to traditional methods, it also presents several challenges that organisations must address to maintain effective data strategies. For further reading on digital privacy laws, visit the Information Commissioner’s Office.

How do you maintain data quality in cookieless analytics?

Maintaining data quality in cookieless analytics is a critical concern for organisations aiming to derive actionable insights from their data. As the industry shifts away from cookie-based methods, several strategies can be employed to ensure high data quality.

Focus on First-party Data:

First-party data plays a vital role in cookieless analytics. By collecting data directly from users through interactions on owned platforms, organisations can ensure accuracy and relevance. This data is often more reliable than third-party data, which may be subject to restrictions and inaccuracies.

Implement Advanced Analytics Tools:

Investing in advanced analytics tools that leverage machine learning and artificial intelligence is essential for maintaining data quality. These tools can identify and correct anomalies in data, providing a more accurate representation of user behaviour.

Utilise Data Cleansing Techniques:

Data cleansing is a crucial step in maintaining data quality. By regularly reviewing and cleaning datasets to remove duplicates, errors, and inaccuracies, organisations can ensure that their data remains reliable and useful for analysis.

Enhance Data Governance:

Robust data governance frameworks are necessary to maintain data quality. This involves setting clear policies and procedures for data collection, storage, and analysis, as well as ensuring compliance with privacy regulations.

Conduct Regular Audits:

Regular data audits help identify potential issues and areas for improvement. By systematically reviewing data processes and outcomes, organisations can ensure that their analytics strategies remain effective and aligned with business objectives.

1. Focus on first-party data
2. Implement advanced analytics tools
3. Utilise data cleansing techniques
4. Enhance data governance
5. Conduct regular audits

How much does it cost to integrate cookieless analytics into an existing system?

The cost of integrating cookieless analytics into an existing system can vary significantly based on numerous factors. This involves considering the scale of the business, the complexity of the current analytics infrastructure, and the specific cookieless solution being implemented. However, understanding these variables can help businesses better estimate potential expenses.

Firstly, the scale of the business is a primary determinant. Small businesses may find that the cost is lower due to the smaller volume of data and simpler infrastructure. Conversely, larger enterprises with more complex systems and higher data volumes may face increased expenses.

Secondly, the existing analytics infrastructure is crucial. Businesses with up-to-date and flexible systems may find the transition smoother and less costly. In contrast, those with outdated or rigid systems might need substantial investment to upgrade their infrastructure, leading to higher costs.

Thirdly, the choice of cookieless analytics solution will significantly impact the cost. Some solutions offer basic features at a lower price point, while others provide advanced functionalities at a premium. It is essential for businesses to carefully evaluate their needs and choose a solution that balances cost and functionality.

Moreover, implementation costs should be considered. This includes potential expenses for hiring specialists or consultants, as well as training staff to use the new system.

In conclusion, while there is no one-size-fits-all answer to the cost question, businesses should conduct a thorough analysis of these factors to develop a realistic budget for integrating cookieless analytics into their systems.

What are the latest trends in cookieless attribution?

The evolving landscape of cookieless attribution is marked by several key trends that businesses should be aware of to stay competitive. These trends are driven by changes in technology, privacy regulations, and consumer behaviour.

1. Increased Focus on First-Party Data: Companies are increasingly relying on first-party data collected directly from their customers. This data is more reliable and less susceptible to privacy concerns.
2. Use of Machine Learning: Machine learning and artificial intelligence are being leveraged to analyse large datasets without cookies. These technologies can identify patterns and predict customer behaviour with high accuracy.
3. Emphasis on Privacy: With regulations like GDPR and CCPA, privacy has become a priority. Businesses are adopting privacy-centric approaches to attribution that respect consumer privacy while providing valuable insights.
4. Server-Side Tracking: This method involves tracking user interactions directly on the server, bypassing the need for cookies. It provides a more accurate and privacy-friendly way of tracking user behaviour.
5. Unified IDs: These are being used to create a consistent user identity across different channels without relying on third-party cookies.

How does cookieless analytics impact digital marketing strategies?

Cookieless analytics significantly impacts digital marketing strategies, necessitating a shift in how marketers approach data collection, audience targeting, and campaign measurement. Here are some key effects:

Data Collection and Management

Without cookies, marketers must rely more heavily on first-party data. This data, collected directly from customers through interactions on websites and apps, is more reliable and less intrusive. Marketers need to enhance their data collection strategies to gather meaningful insights while respecting consumer privacy.

Audience Targeting

Targeting audiences becomes more challenging without the granular data provided by third-party cookies. Marketers will need to develop new methods for segmenting audiences, possibly using contextual advertising and predictive analytics to ensure that ads reach the right people at the right time.

Campaign Measurement

Measuring the effectiveness of marketing campaigns is altered as traditional cookie-based metrics become less relevant. Marketers will need to find new ways to attribute conversions and understand user journeys. This may involve using machine learning algorithms and data integration from multiple sources to build a comprehensive view of marketing performance.

How can businesses benefit from cookieless attribution?

Implementing cookieless attribution offers several benefits for businesses, especially in an era where consumer privacy and data protection are paramount. Here are a few key advantages:

1. Enhanced Privacy Compliance: Cookieless methods align with stringent privacy laws such as GDPR and CCPA, reducing legal risks and enhancing consumer trust.
2. Accurate Customer Insights: By leveraging first-party data and machine learning, businesses can gain richer insights into customer behaviours and preferences, leading to more personalised marketing strategies.
3. Improved Customer Relationships: Respecting consumer privacy can lead to stronger customer relationships, as individuals are more likely to engage with brands that prioritise their data protection.
4. Future-Proof Strategies: Adopting cookieless attribution prepares businesses for the future of digital marketing, ensuring they remain competitive as cookie-based tracking becomes obsolete.

How does cookieless analytics influence customer experience?

Cookieless analytics has a profound influence on customer experience by reshaping how businesses interact with and understand their audiences. Here are some key aspects:

Privacy and Trust

Cookieless analytics enhances privacy by eliminating the need for invasive third-party cookies, fostering trust between consumers and brands. Customers are more likely to engage with companies that respect their privacy, leading to a better overall experience.

Personalisation

While personalisation may seem challenging without cookies, cookieless analytics leverages first-party data to provide tailored experiences. This data, combined with advanced analytics, allows businesses to deliver relevant content and offers based on genuine customer interests.

Seamless Interactions

By focusing on first-party data and more accurate attribution models, businesses can create seamless customer journeys. This reduces friction in the buying process and enhances satisfaction.

In summary, cookieless analytics can significantly improve customer experience by respecting privacy, enhancing personalisation, and ensuring smooth interactions.