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Quick Guide to Digital Transformation and the Role of Data

This quick version of the Digital Transformation and the Role of Data Best Practice Guide provides marketers with a starting point for actioning digital transformation by building a data-led strategy.It looks at how data underpins modern businesses, the challenges it presents and what the future holds, covering:The role of data in the digital transformation journey, and the areas of business touched by data The two key business models that are powered by data and the pros and cons for each An introduction to the best data models and processes, and strategies for putting these into action Actions businesses can take to avoid the pitfalls when following a data-led strategy Where data and digital transformation will go next, and the capabilities this will present businesses. This Quick Guide draws from Econsultancy’s Digital Transformation and the Role of Data Best Practice Guide.

1. Introduction Technological advancement, human ingenuity and data – lots of it – underpins digital transformation. But what data should marketers collect? And how should it be structured?This abridged version of Econsultancy’s Digital Transformation and the Role of Data Best Practice Guide aims to help marketers better understand data within the wider scope of digital transformation, how data underpins modern businesses, the challenges it presents, best practice strategies and what the future holds.

2. How Data Powers Digital Transformation

2.1 What do we mean by digital transformation? Digital transformation is a journey undertaken to futureproof businesses. Its success is underpinned by people, technology, processes and data and often involves re-engineering and automating business processes and culture. The aim is to change at a timing and speed that allows the business to disrupt the market before it is disrupted by others.Data is one of the main ingredients to digital transformation. Digitised data points need to be housed in such a way that many business units may access and use the data at the same time. It is the accessibility, ‘query-ability’ and usability of data by many lines of businesses simultaneously that underpins transformational business changes.

2.2 The role of data Digital transformation is about enabling data usage for forward-facing projections – predicting or influencing the likelihood of something happening. Bringing datasets together can surface unexpected things not previously considered as opportunities or options.Companies need to capture data about customers and logistics, as well as peripheral, contextual data. For example, a single piece of financial transaction data needs to link to a customer, products purchased, method of purchase, delivery, the device used to make the purchase, time, date, place and much more. It must then be accessed across most business units simultaneously; sales, ecommerce and marketing should all be using the data to understand what drives long-term higher productivity and business impact.

2.2.1 The multi-storey building analogyA foundation determines how high, solid and robust a building will be. Imagine processed, cleaned and catalogued data as the strong foundation of a multi-storey building. Get the data right and unimaginable business heights can be built.However, data is evolving and not yet mature enough for companies to build upon. Data development is in the discovery phase. Emerging volumes of data lack the rigour to build upon. Marketers must construct the conceptual build and then source appropriate materials.

2.2.2 Marketers need to lead on data literacyData increasingly continues to determine who sees what, where, how and how often in marketing products and services. Data also underpins the design of better customer experiences and more personalised communication. Marketers must show how data-backed evidence-based knowledge and insights deliver business results.

2.2.3 The automated and the XXaaS business modelsThere are two key types of business models that are powered by data: the automated model and the XXaaS model. The automated model aims to drive down cost and improve on speed. XXaaS businesses aim to drive up value and so command a premium on services while also encouraging more time spent with the brand.Digital transformation often diminishes the middle-of-the-range brands which are neither efficient nor personalised enough for consumers to keep buying them. The challenge is the companies that have been first to adopt automated solutions, such as Amazon, grow exponentially into other sectors as their efficiencies have scale.If it is unclear what digital and transformation strategy the organisation should take, picking one of these models is a great starting point.

The automated model

Customers want things to be easier, cheaper and faster. Digitalisation delivers speed and efficiency, increases productivity and sales at scale, minimises human error and maximises choice combinations by automating capabilities as much as possible. However, the automated model requires a huge volume of data to use for modelling out predicted successes. Consumers win with the automated model through independent self-service, 24 hours a day. The data being collected through this process is used to fuel personalised communications.

The XXaaS (something as a service) model

The XXaaS digitalisation business model focuses on using data to create more value during a service experience. It is the opposite of the automated model, which enables independence.The ‘XX’ in XXaaS refers to an industry or offering. Many major industries are moving towards service models, including transport as a service, retail as a service, entertainment as a service, or even laundry as a service.Such models often entail consumers having access to something ‘as a service’ or an enhanced ‘serviced’ experience rather than outright ownership or self-service. The ease of experience, relevancy and high-touch nature of the service is appealing and may also remove perceived hassles.With the service model, consumers may not need to make an upfront investment in an asset purchase and all repairs and maintenance become part of the service cost, which might be a monthly subscription model or a per usage fee.The aim of XXaaS is to establish deeper customer relationships via the extensive, built-up history of relevant customer data including their engagement with the business, preferences and habits.

3. Why Data Fails Many companies are still in data discovery mode and have not reached data maturity. There is uncertainty and fear for some and a spirit of adventure for others. Ensuring data is used appropriately in the process of digital transformation is a continual work in progress.

3.1 Lack of strategy Less than 50% of companies have formal corporate data strategies documented for their data and analytics teams, even though it is key for delivering enterprise value.[1] Consumers usually only want to share data once. Without a data strategy, data collection is resource intensive, costly and can inhibit value-producing outcomes.

Organisations need strategies for what exactly they want to build from data. Once this is defined, the associated metadata can be designed and implemented.

3.2 Legacy data, systems, tools With legacy digitised systems, data has typically been purposely collected and housed in a very structured and restrained way for a specific (often singular) purpose. Businesses can find it hard to shut down or switch off redundant services. Strict centralised data governance can be like herding cats and requires a taskforce on its own if the business is sprawling with disparate data.

Organisations should get leadership buy-in, explaining the impact of not breaking contracts or decommissioning redundant data sources, systems and tools. Legacy siloed data ownership can break transformations as teams refuse to or cannot see benefits to digital transformation.

3.3 Data quality, hygiene and access A lot of data is captured with little structure or clarity on its business use. Often it is poorly formed, not granular, big or rich enough. A lack of harmonisation in data fields, labelling, metadata and structure will render most data nonsensical and incompatible with meeting the needs of the stakeholders and use cases.When it comes to data hygiene or integrity, painful amounts of time are spent by data engineers and data science teams doing repetitive manual cleaning work. Ideally, teams would have access to the original data source – though this may be difficult.

Attention to detail is crucial, as well as clear guidance on use cases for data collection.[2]

3.4 Misunderstanding software capabilities Visualisation software is a great case in point. It is often, incorrectly, thought of as an analytics platform; visualisation software can filter data, but it cannot calculate or compute it. This distinction often leads to marketers being wooed by visualisation solutions and onboarding them, only to realise internal data is in no state to be visualised.

Visualisation software needs to be paired with systems and tools that can manage the computations before loading.[3]

3.5 Leadership and decision making Data decisions are often micro-decisions with major implications. Not getting the small decisions right can cause long-term implications.Seeking perfection in data can lead to inaction. Having too much data can also cause decision-making paralysis. If a business culture is extremely risk adverse, choices can feel like risks.

In order to succeed in their digital transformation, businesses must unlock decision-making bottlenecks. C-suite buy-in, skillsets and datasets all need to align. Clear awareness on who owns the decision-making responsibility can greatly speed up productivity as teams fall in line with defined roles and aid decision making rather than lobbying for their own position to be agreed upon.

4. Best Practice Data Models and Processes

4.1 The Data-as-a-Service (DaaS) Transformation Model Transforming data so that it can be used by many teams, systems and processes simultaneously, for a multitude of business uses and outcomes, involves a democratisation of data access.The Data-as-a-Service (DaaS) Transformation Model explains the step-by-step journey to democratising data for multipurpose. The building blocks allowing deployment of the model are:

1. Data sitting in business silos (lines of business data)

2. Breaking down the data (data transformation)

3. Making the data accessible, regardless of where it is stored so it can be blended with other data including second- and third-party data (DaaS)

4. Systems and tools that are used for internal as well as external data

5. The strategic outcomes from the process leading to business solutions being built like a skyscraper (digital transformation)Some of these steps have been covered in previous sections. This section will explore step 4 (systems and tools) in more detail.For a step-by-step outline, details on what it is like to experience the process as a business stakeholder, case studies and illustrations, see Econsultancy’s Digital Transformation and the Role of Data Best Practice Guide.

4.1.1 Selecting and using systems and tools: best practice

Understand internal business data

When selecting systems and tools to aid digital transformation, knowing internal business data is key. If necessary, lean on vendor expertise to determine what value can be extracted from data. Most reputable vendors allow test drives, proof of concept tests and, under non-disclosure agreements, a look at capabilities with live data. Consider how many vendors, tech platforms, or service and data providers are needed. The right cultural fit into a wider ecosystem also needs to be explored in depth.

Know who owns what data

Investments in digital transformation systems and tools should focus on whether the data inputs are first-, second- or third-party data sources. Building and procuring solutions with first-party data inputs gives the business better control over the course of its transformation and future costs of enhancements and iterations.

External data, systems and tools The further away an organisation is from controlling data as the primary owner, the less time should be spent on customising solutions internally and building expensive infrastructure around the data. Minimise multiple dependencies on data that the business has limited control over as the knock-on effect to losing the data source can be substantial. Focus on data with clear use cases and quantifiable business outcomes to justify investing in second- and third-party data sources. Plan alternative sources of data if the future growth potential for the business warrants such investments.

Internal data, systems and tools If software and tools are not part of the core business competencies, then partnering, subscribing or renting is advised. When buying in solutions, ensure that they come with ongoing upgrade support and the scope for product enhancements and customisation. Equally, the system and tool providers need to fully audit the data and understand it to appropriately help with onboarding, scaling and cost control.

Decide where to start

Start with data controlled by the business and work outwards. Marketers should start with knowing the use case for data and then work backwards from that. Having data sources with common data points that match with internal sources is a key advantage, allowing companies to assess what incremental value the new data sources, systems and tools will add to the business before onboarding them.

Centralise data taxonomy and governance and simplify access

Data taxonomies, data architecture and management require strict compliance frameworks so that data engineers and data science teams can work with and build models upon clean data, within the law. Lines of business metadata should be designed and detailed enough to support changes in those lines of business. Data extraction, data appending and classifications are increasingly automated, as cataloguing by humans is simply too slow and not exhaustive enough. Enterprise data management strategies are required to enable metadata automation and the creation of innovative metadata details and cannot be avoided if businesses are working globally and in many languages.

Make data ownership a centralised track with clear stakeholders

Data will eventually have many users; assigning one impartial data owner who is accountable to many stakeholders and their requirements is advised. Marketers as champions or stakeholders need to be actively involved decision makers with the responsibility of focusing on the business aim and how well it is translated into the strategy and design of the data collection. Having senior business sponsors for data initiatives that tie into bigger transformation plans, as well as clear KPIs around well-rounded goals, helps with business buy-in and can instil discipline needed to stay on track.

Communicate purpose, benefit and strategic outcomes to staff

Educating staff on how data can help them, how to use it and what insights can be derived to aid better work outcomes, will take goal setting and time to implement. Being aware of the current data literacy among staff will help manage expectation from the start. The 6 PsThe 6P framework should be considered when planning a transformation. It can be used alongside those of the IT and product teams, who will have detailed frameworks specific to data schema, taxonomies, infrastructure and operating models:

1. PURPOSE: understand the business focal point today

2. PLAN: map out strategies to make them tangible

3. PROCESS: build frameworks to execute decisions

4. PEOPLE: set expectations

5. PARTNERS: hire vendors to work with

6. PILLARS: ask the right questions to understand more

5. What is Next for Data and Digital Transformation? Exponential volumes of data will continue to be created in the foreseeable future. The aim will be to make data simpler, more meaningful and accessible to all parts of the business.The Fourth Industrial Revolution[4] is advancing, as organisations start to work with artificial intelligence to replicate human-like capabilities and emotional intelligence. Detecting mood, tone, feelings and more subjective data points will be the next evolution and revolution.

5.1 Data growth areas

Natural language programming (NLP)

NLP data is generated by and about people and their thoughts, feelings and communications. While text is being codified, digital transformation data will evolve into data covering human relationships and wellbeing.

Signal data

Signal data is sensor data from any kind of item that can send data from one location to another, such as from fabrics and clothing, to rooms, surfaces, cars and devices. The collection and analysis of biodata – signal data generated by wearables, for example – is expected to enable the development of game-changing solutions in healthcare.

Biometric data

Biometric data can include facial encoding, fingerprints, iris scans, voice data, DNA and biodata.  Coupled with personally identifiable data, it can create a very powerful dataset which is transforming governments, corporations and people – and raising ethical concerns on appropriate usage.

Surveillance data

Surveillance data can be as simple as capturing data at regular intervals or in a systematic way through to enterprise level, 24/7 video and image monitoring. This data might allow marketers to audit stock placements, sales and competitors in innovative ways. For example, by taking photos of shelves in stores, promotions or sales receipts, and digitising, decoding and analysing what is happening at point of sale, marketers can move into near real-time data decision-making capabilities.

5.2 Future marketing with data Influencing a buying journey which is part-human and part-machine will be the new paradigm. Marketing skills will need to adapt to changing buyer behaviours and machine-enhanced capabilities. Marketers will need to understand how smart voice command devices make decisions and then find ways to make the voice output choose the brands they represent. Being open, agile and continuing to learn will be key to the ability to thrive.6.

Key Takeaways Marketers must understand the main characteristics of data: its quality, its purpose and its context.

Data transformation is a human journey:Humans transform data for human purposes. Data must be electronic and ideally collected for many uses from the start. Mindsets, skillsets and datasets within an organisation all need to align. Successful journeys start from top down; leadership and culture are paramount.

Data management needs a higher level of rigour:Data architecture, governance, policies and strict metadata management must be set. There must be considerations of privacy and ethics as more personal or biological data is collected. Having an operating model in place for all vendors to work together speeds up productivity.

Digital transformation shows no signs of plateauing:Data science roles and functions will continue to evolve as technology advances. A diversified team needs to collaborate and so good communication is vital. The DaaS model is a tool to help explain the transformation process for data as well as the human impact of data transformation. As laws evolve, businesses will need to react quickly to legal changes. Being digitalised, businesses can pivot and respond more readily.

There are many time-saving tips that can help guide marketers through data transformation. However, these are likely to continue evolving. Keep learning, while leaning on strong foundations and networks. Further reading

Econsultancy | Digital Transformation and the Role of Data Best Practice Guide https://econsultancy.com/reports/digital-transformation-and-the-role-of-data/

Econsultancy | The Fundamentals of Marketing Measurement and Analytics https://econsultancy.com/reports/the-fundamentals-of-marketing-measurement-and-analytics/

Econsultancy | AI, Machine Learning and Predictive Analytics Best Practice Guide https://econsultancy.com/reports/ai-machine-learning-and-predictive-analytics-best-practice-guide/

Econsultancy | Customer Journey Mapping Best Practice Guide https://econsultancy.com/reports/customer-journey-mapping-best-practice-guide/

Econsultancy | Measuring Digital Marketing Effectiveness Best Practice Guide https://econsultancy.com/reports/measuring-digital-marketing-effectiveness-best-practice-guide/

Econsultancy | Digital Transformation Monthly https://econsultancy.com/digital-transformation/

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An overview of the third-party cookie crackdown

n March 2021, Google announced on its Ads & Commerce blog that it was “Charting a course towards a more privacy-first web”. The blog post was a re-affirmation of Google’s commitment, first revealed in January 2020, to remove support for third-party tracking cookies in Chrome from 2022, and contained further details about how it plans to protect user anonymity once these are phased out. But it was also a sign of just how much the conversation around third-party cookies has shifted over the past few years.

The debate around third-party cookies, which for years have been heavily relied-on by the marketing and advertising industry to compile records of a user’s browsing history and build up a profile of their interests, preferences and habits, stretches back as far as 2013, when Firefox, Safari and even Internet Explorer all began implementing some form of default blocking of third-party cookies or ad tracking.

At the time, this decision to disallow third-party cookies – even coming from most of the major internet browsers – was highly controversial among industry commentators. They argued that cookies were necessary for the ad campaigns that fuel the internet to function, or that cookies – at their heart simply a piece of technology designed to retain preferences by identifying when the same user returns to a website – are not inherently intrusive, rather that certain applications of them are.

And for all that steps to block third-party cookies were taken by web browsers very early on, it took several more years for some of them to implement comprehensive blocking, as well as for the web browser with the largest share of the market – Google Chrome – to join them. During that time, cookies remained pivotal to the way that marketers tracked, targeted and measured the impact of their campaigns; but the conversation around privacy on the web, particularly as it related to advertising, was changing.

Fast forward to 2021, and the demise of the third-party cookie (nicknamed the “cookiepocalypse”) is now an accepted fact, with discussions turning instead to how marketers can prepare, and what kinds of solutions and alternatives should be adopted. But how did we get to the point where one of the biggest web advertising heavyweights is withdrawing its support for third-party cookies, and where do we go from here? In this briefing, I’ll look at the recent developments that led to our current situation, the stances being taken by key industry players, and the alternatives that promise to balance privacy with marketing effectiveness.

Covered in this briefing:

The cookiepocalypse: a timeline

An end to cookies, but not to behavioural targeting

Alternatives to third-party cookies: what should marketers use?First, second and zero-party data Contextual targeting Alternative forms of measurement

A more positive – and private – future

The cookiepocalypse: a timeline

While 2019 was by no means the beginning of the debate about cookies – as we’ve established – it was in many ways when the tide really began to turn against third-party tracking cookies, with a number of huge developments taking place in a short space of time. And while the calamitous impact of the Covid-19 pandemic overtook the global conversation early into 2020, significant developments on the privacy and tracking front still took place in 2020 and early 2021 as players like Apple and Mozilla entrenched their established positions and moved to block additional forms of tracking.

March 2019: Apple releases Intelligent Tracking Prevention (ITP) 2.1 for Safari, the latest version of a feature designed to prevent cross-site tracking by placing limitations on cookies and other website data. First introduced in 2017, ITP prevented cookies from being used in a third-party context after 24 hours, and purged website data and cookies altogether after 30 days if the user hadn’t interacted with the website again during that time.

Adtech companies responded to the implementation of ITP by finding ways to make third-party cookies behave like first-party cookies, circumventing the block. So, with ITP 2.1, Safari squashed this workaround by wiping first-party cookies after seven days. In May 2019, it upped the ante with ITP 2.2, which deleted certain types of first-party cookies after just 24 hours, further closing the loophole. This seriously rattled advertisers, as Safari is the world’s second-most popular browser (with a current market share of 19.14%, according to GlobalStats) and the default for anyone using a Mac, Macbook, iPad or iPhone.

May 2019: At Google’s annual developer conference, Google I/O, the Chromium team announces upcoming changes that will give users more visibility over the types of cookies being set in their browser and options to block them. It also announces plans to “reduce the ways in which browsers can be passively fingerprinted” – fingerprinting being a catch-all term for more difficult to detect methods of user tracking that subvert cookie controls.

July 2019: The ICO publishes stringent new guidance that rules out many of the pretexts that advertisers have been using to set cookies in users’ browsers without actively obtaining their consent. The guidance specifies that websites:

Cannot rely on implied consent for the use of cookies

Do need to obtain consent for analytics cookies (which are not considered essential to the functioning of the site)

Cannot use a ‘cookie wall’, which restricts access to the site until users consent to cookies being set

Cannot rely on legitimate interests, a lawful basis for processing personal data under GDPR, to set cookies without consent.

August 2019: A study by researchers from the University of Michigan and Ruhr-University Bochum finds that 86% of cookie consent notices offer no other options than a consent button, while 57% are found to use dark patterns in order to influence a user into consenting.

September 2019: Mozilla announces that Enhanced Tracking Protection (ETP), which blocks third-party tracking cookies and cryptominers, will be enabled by default in Firefox on desktop and Android. It had previously enabled ETP by default for new users in June; the September update deploys it for all Firefox users across the board. While Firefox has a much smaller market share than Chrome or Safari (currently standing at 3.76% worldwide), it is still the world’s third-most used web browser and its implementation of cookie blocking by default is another blow for marketers and advertisers.

Apple also releases ITP version 2.3 for Safari, which blocks two additional workarounds that ad companies have been using to track users: localStorage and document.referrer.

October 2019: The Court of Justice of the European Union (CJEU) rules that pre-checked consent boxes for the use of cookies are not valid, and that consent requires an active opt-in. It also rules that users must be provided with information on cookie duration and whether third parties have access to them. Given the findings of the August study by researchers in Michigan and Bochum, this means that the vast majority of websites are contravening EU law by not giving users the option to decline cookies.

January 2020: The loudest death toll yet sounds for third-party cookies as Google announces that it will withdraw support for third-party cookies in Chrome by 2022. Executives at the Association of National Advertisers (ANA) and the American Association of Advertising Agencies (4As) issue a joint statement warning that, “Google’s decision to block third-party cookies in Chrome could have major competitive impacts for digital businesses, consumer services, and technological innovation”, and that it “would threaten to substantially disrupt much of the infrastructure of today’s Internet without providing any viable alternative.”

March 2020: Apple confirms “full third-party cookie blocking” in iOS, iPadOS and Safari, blocking cookies for cross-site resources by default across the board. John Wilander, the WebKit engineer behind Safari’s ITP, writes that, “This is a significant improvement for privacy since it removes any sense of exceptions or “a little bit of cross-site tracking is allowed.”” He celebrates that Safari is now “the first mainstream browser to fully block third-party cookies by default.”

In the same month, AdExchanger reports that an unnamed business group within the World Wide Web Consortium (W3C) is petitioning Google to delay the phase-out of third-party cookies in Chrome in light of the unfolding coronavirus pandemic. Google product manager Marshall Vale later writes to the W3C to say that it is “premature” to discuss any adjustment to the cookie phase-out timeline, although he promises that Google will “revisit” the topic as the situation evolves.

June 2020: At WWDC20 – Apple’s World Wide Developer Conference – Apple announces that the next version of iOS (iOS 14) will ask users whether they want to be tracked by any given app, and that app developers will be required to self-report the kinds of permissions that their apps require. This development, called App Tracking Transparency or ATT, was later pushed back beyond the launch of iOS 14 to give developers additional time to prepare; it will now form part of iOS 14.5, which entered developer beta on 1st February, and is expected to be deployed fully at some point in March.

While not directly related to cookies, Apple’s move to require apps to disclose the data they track is in line with its broader push towards protecting user privacy, and is particularly relevant to the question of whether and how users can be tracked within mobile apps, where cookies (which are browser-based) cannot be set.

November 2020: Apple announces further enhancements to ITP to defend against something called CNAME Cloaking – a tactic that maps an internal domain to an external one and allows a tracker to circumvent the division between first-party and third-party cookies, giving it the same level of access as a first-party cookie.

February 2021: Firefox introduces “Total Cookie Protection” as a feature of ETP Strict, an optional, more privacy-conscious version of ETP.

According to the blog post published by Mozilla, Total Cookie Protection “works by maintaining a separate “cookie jar” for each website you visit. Any time a website, or third-party content embedded in a website, deposits a cookie in your browser, that cookie is confined to the cookie jar assigned to that website, such that it is not allowed to be shared with any other website.” It goes on to add that Total Cookie Protection “makes a limited exception for cross-site cookies when they are needed for non-tracking purposes, such as those used by popular third-party login providers … Such momentary exceptions allow for strong privacy protection without affecting your browsing experience.”

Meanwhile, Bloomberg reports that “according to people with knowledge of the matter”, Google is exploring an Android version of Apple’s App Tracking Transparency. To this end, it is “seeking input” from stakeholders such as developers and advertisers in a bid to find a solution that does not compromise their ability to generate revenue. The report goes on to state that Google’s solution “is likely to be less strict and won’t require a prompt to opt in to data tracking like Apple’s … The exploration into an Android alternative to Apple’s feature is still in the early stages, and Google hasn’t decided when, or if, it will go ahead with the changes.”

March 2021: Google reveals further details on how it plans to handle user identification and tracking in the blog post ‘Charting a course towards a more privacy-first web’. In particular, it confirms that it does not have any plans to create “alternate identifiers” that would track users around the web – which many marketers, publishers and content creators see as a betrayal of their interests. Instead, Google intends to draw on the vast amount of first-party data at its disposal – thanks to its ownership of properties like Youtube, Google Maps, and of course Google Search – to target users, which will further entrench its dominant position as one of the most powerful online advertisers.

Google has also not made any commitments to phasing out the use of Google Advertising IDs, or GAIDs, which are used to anonymously track user ad activity on Android devices, in a similar fashion to a third-party cookie. Given that increasing amounts of activity take place within mobile apps and away from the open web, this would arguably be a bigger concession for Google – and while there have been hints that Google may implement an ATT-style solution on Android, it is still ultimately in Google’s interests to be able to track users and the effectiveness of its own advertising, whatever that entails.

An end to cookies, but not to behavioural targeting

While Google has been making all the right noises in its cookie-related announcements about the need for privacy on the web, in reality it is not about to do anything that would seriously compromise its own ability to effectively target and drive conversions from advertising. Marketers always knew this, of course – but had hoped that Google would throw its considerable weight behind one of a number of proposed industry solutions such as Unified ID, an initiative with widespread support among adtech providers that uses an email-based identifier to target users while purportedly giving them more control over how their data is shared.

Instead, Google has made it clear that it is only interested in implementing home-grown solutions like Federated Learning of Cohorts (FLoC) – an interest-based targeting method that Google claims will provide “at least 95% of the conversions per dollar spent when compared to cookie-based advertising”. Marketers are waiting to see whether this claim is borne out – but what is evident is that while cookies may be going away, behavioural targeting will not.

FLoC is a more anonymised method of targeting than third-party tracking cookies in that it uses machine learning to divide users into groups with similar interests and target ads based on those interests. As Google puts it, this method “effectively hides individuals “in the crowd” and uses on-device processing to keep a person’s web history private on the browser.” Users may not be tracked across browsers or across devices, but their activity within Chrome will still be tracked and used for ad targeting, with presumably no capacity to opt out (except by using another browser). This has not gone over well with privacy advocates, and privacy-conscious users may see it as little improvement over third-party tracking cookies.

Meanwhile, as marketers wait to discover more information about the effectiveness of FLoC, some of the drawbacks are already becoming clear: FLoC’s interest-based groups will not be precise enough to measure interest in a specific product, which jeopardises advertising methods like product-specific retargeting. FLoC will also make attribution and measuring campaign effectiveness much more difficult, with metrics like viewability potentially taking precedence over conversions.

There’s also the issue that the introduction of FLoC only promises to further entrench Google’s position as the dominant force in web advertising. Google’s blog post was a reminder that in a world without third-party cookies, first-party data will reign supreme – and fortunately for Google, it has access to a wealth of it, through properties like Search, Youtube and Maps. The UK’s Competition and Markets Authority (CMA) has already launched a probe into Google for “suspected breaches of competition law” over its plan to phase out third-party cookies in Chrome, following complaints that Google’s plans for a replacement would “abuse a dominance position” in advertising.

Marketers have known for some time that the mass withdrawal of support for third-party tracking cookies by the major web browsers would require changes to the way they track and measure campaigns, and many have been preparing for the change for some time. Nevertheless, a considerable number have been left dismayed by the approach that Google has chosen to take instead.

With Google retreating further behind its own walled garden with the introduction of FLoC, and the second- and third-most-used browsers (Safari and Firefox) blocking identity-based tracking of almost every kind, where should marketers go from here?

Alternatives to third-party cookies: what should marketers use?

As it became increasingly clear that the writing was on the wall for third-party cookies, the industry has been abuzz with talk of alternatives: alternative identifiers, alternative forms of measurement, alternative data sources, and alternative forms of advertising.

Some of these are more viable than others. Many alternative identifiers, for instance, encounter the issue of the need for universal adoption: while Unified ID has attracted widespread support from adtech players, Google’s refusal to support it has thrown its future into question, and there are also doubts as to whether consumers would agree to adopt it. Another potential cookie alternative, digital fingerprinting (which uses attributes like operating system, web browser type and version, language setting and IP address to identify and track a user’s device), is seen as even more intrusive than cookies, and Safari, Firefox and Chrome have all either taken steps to block digital fingerprinting methods or have pledged to reduce the ways that users can be targeted by them. And device-level IDs, like GAIDs or Apple’s IDFA (ID for Advertisers), still present problems with regard to privacy and with cross-device tracking.

But identifiers aren’t the only means of personalising advertising for users in a meaningful way, or of measuring campaign effectiveness. With the eradication of cookies, marketers have known they’ll need to get creative, and possibly employ a variety of methods to achieve the same results that they were able to obtain with cookies. However, there are also benefits to using these alternatives. Let’s take a look at some alternatives to cookie-based targeting and measurement, and the reasons why marketers should employ them.

First, second and zero-party data

“First-party relationships are vital,” wrote Google in its Ads & Commerce blog post, and this statement isn’t just true for Google – it’s true for marketers, publishers, and businesses of every stripe. First-party data is data a business collects from its users directly, and can take a wide variety of forms, from things like internal searches carried out on an ecommerce site to previously purchased items, CRM data, loyalty programme data, and much more. All of these pieces of information can enable businesses to tailor marketing and the customer experience in a way that feels genuine and helpful rather than intrusive.

A subset of first-party data that has attracted considerable industry interest in the wake of regulations like the GDPR is ‘zero-party data’. This refers specifically to data that the consumer has shared proactively, such as profile information (which can include demographic data like date of birth and gender), content preferences, responses to polls and quizzes, and many other varieties. Again, this can be used to more authentically personalise a consumer’s experience, but without the need to infer preferences from their behaviour.

One advantage of the cookie crackdown is that it is prompting marketers and brands to look more closely at the relationships they have with their own customers, review the data they already have at their disposal, and think about smart ways to use it in marketing and advertising campaigns. And while larger businesses may seem to have an unfair advantage in that they have access to significantly more first-party data than smaller businesses, small amounts of first-party data can still be used for predictive modelling, analysed, and built out into audiences. Forming data partnerships with another organisation (data obtained in this way is known as second-party data) is another solid strategy and a way of evening out the playing field.

In other words, cookie data is far from the be-all and end-all – and using other data sources will lead to improved relationships with customers, better personalisation and more accurate results.

Contextual targeting

Contextual ad targeting, a method of delivering relevant ads based on the content of the page a user is viewing rather than who the user is (or is thought to be) has enjoyed increased attention with the looming cookiepocalypse throwing the future of behavioural targeting into question.

And although the likes of Google have signalled their intention to continue targeting users based on behaviour (just in a slightly less precise way), contextual targeting is still an option worth exploring, offering many of the same benefits in terms of ad relevance as behavioural targeting, plus some arguable advantages from a user perspective. After all, a user who reads about marketing technology for their day job might not be interested in having martech ads follow them around off the clock – but they might be interested in an ad relevant to the hobby-related article they are reading for fun.

Contextual targeting on the web has been around for decades, and to some marketers, might seem like a rather basic and outdated method of ad targeting compared with more precise cookie-based methods of targeting. However, as Patricio Robles wrote for the Econsultancy blog, “Contextual advertising in the 2020s will be more sophisticated … Better technology allows marketers to apply their first-party data to contextual ads, and they will also be able to target by various non-unique attributes, including device type, location, and time of day, many of which can impact the effectiveness of campaigns.”

Alternative forms of measurement

Measurement and attribution were already challenging enough for marketers before the demise of the third-party cookie. In an online world that has fragmented into countless touchpoints, channels and devices, how can marketers determine exactly which of their actions moved the needle or resulted in a conversion?

But many have seen this as a positive opportunity: cookies were far from a perfect solution to marketing measurement, particularly in the age of mobile where so many interactions already take place away from browsers. And while there isn’t one single, ideal measurement solution that will allow marketers to comprehensively measure campaign performance in the post-cookie age, a variety of methods exist with different strengths marketers can call on in different circumstances to achieve their goals. Here are just a few of those methods:

Econometrics/Marketing Mix Modelling

Econometrics, also called Marketing Mix Modelling, is a method of using statistical analysis to understand the impact of a range of different variables – from sales promotions to the weather or the economy – on marketing KPIs.

It pulls in data from a wide range of different sources, meaning that it depends relatively little on third-party cookie data, but it is also most effective when run over time, using a large number of data points. This makes it less of a viable option for marketers who don’t already have a lot of data. Marketing Mix Modelling is useful for informing high-level decisions like budget split between channels, but can’t inform granular decisions like how to optimise bids.

Incrementality testing

Incrementality testing is a specific variety of A/B testing that allows marketers to measure the lift provided by a particular type of advertising by comparing the conversion rate of one group, which was not exposed to that advertising, with the conversion rate of another that was. The difference between the two groups’ conversion rates can be used to calculate the incremental lift provided by that advertising – assuming that all other variables between the two groups are the same.

In other words, incrementality testing is most effective when you can control all of the variables that are likely to impact on uplift – otherwise you might be attributing conversions to the wrong one. Incrementality testing can be useful for determining the true impact of a particular piece of advertising, channel or campaign on conversions and finding out whether it offers value (and how much) or just takes ‘credit’ for an action that would have occurred anyway.

Brand lift studies

Brand lift studies are a type of market research that surveys consumers to find out whether their level of awareness, their perception of a brand, and/or their likelihood of making a purchase has changed after being exposed to an ad. Many ad providers, including Facebook and Google, offer native brand lift tools to help marketers measure the impact of their ad campaigns.

Brand lift studies are useful for providing detail on the effectiveness of a specific campaign or channel, but are difficult to generalise beyond that. Wider brand studies can also be carried out as a means of obtaining consented, first-party data; providing a benchmark that future brand lift studies can be measured against; and obtaining more in-depth insights from consumers like their opinions on the category as a whole.

More detail on other cookie-less methods of measurement, including attribution analysis, controlled and uncontrolled audience testing, and geo-testing, can be found in Daniel Gilbert’s insightful post, How to measure marketing in a world without cookie tracking, as well as in Econsultancy’s Measuring Digital Marketing Effectiveness Best Practice Guide.

A more positive – and private – future

The announcement by Apple that it will be prompting users for their consent to share data with apps, and by Google that it will not be supporting an identity-based alternative to cookies going forward, has collectively galvanised the industry into searching for sustainable alternatives to the old methods of targeting and measuring ads. This has not been – and will not be – an easy transition for marketing, but many believe the outcome will be beneficial for marketers and consumers alike. At the end of 2020, Harmony Murphy, GM Advertising at Ebay UK, gave her views on the ��post-cookie’ future for marketing in a 2021 predictions roundup for Econsultancy, saying,

“Instead of relying on quick wins, a cookie-less, premium, user first experience is a move in the right direction for brands. After all, this is what advertising is about: quality engagement with consumers that helps them and genuinely delivers ROI for the brand. It’s about engagement and relevance, not irritation.”