Cookies have been created to improve the user experience of websites. their main use is to enable companies to collect data on their users as they browse. So they can gain a better understanding of the audience they are targeting and the way in which they it interacts with their content.

The problem is that the use of cookies has also raised a number of concerns, particularly with regard to the privacy of Internet users. In recent years, web browsers and regulators have taken stpeds to limit heir use and protect users. This is known as the cookieless world (a future era without cookies).

The end of third-party cookies has been announced for the second half of 2024. Google is therefore preparing to separate itself from this fundamental element for the brands that use it to optimise their advertising and cusotmer experience.

So what does this cookieless world look like? And what are the alternatives for continuing to understand your customers’ expectations? Here are a few avenues to explore to optimise your data collection strategy in this new world.

SUMMARY

• Google speeds up the advent of a cookieless world
• The challenges of a world without cookies
• Adopting intelligent marketing to collect proprietary data

Everyone is talking about it, but the much-heralded cookieless world has been slow to arrive. Google has repeatedly postponed the deletion of third-party cookies , with the aim of relaunching the process in 2024. On 4th January 2024, the American giant began testing a new tool, “tracking protection”, which aims to limit the tracking of users from site to site.

In fact, only a small percentage of Chrome users have already switched to a third-party cookie-free environment.. Dans les mois à venir, cette première phase de test sera étendue à un plus large public. Mais d’ici là, les entreprises ont encore peu de visibilité sur les effets de cette protection contre le suivi.

According to Google’s teams, the definitive deletion of third-party cookies should take place in the second half of 2024… Provided, however, that all remaining concerns are resolved. it has to be said that companies in all sectors of activity are still little or unprepared for the end of cookies. Not to mention governments, which are worried about the impact of this cookieless world on competition.

Businesses are therefore expecting access problems. Sites that do not function without thirs-party cookies may no longer be accessible. Google is also trying to reassure them with this first phase of the “more private web”. It announced that it would provide “the tools needed to make their online operations and experiences successful, so that high-quality content remains freely accessible”.

Cookies have always been a valuable tool for tracking user behavior and to attribute conversions to specific marketing campaigns. however, with increasing privacy concerns and the growing use of ad blockers, cookies are becoming less effective.

As the worlds moves towards a cookie-free future, marketers face new challenges in understanding and reaching their target audiences, optimising their data collection and advertising targeting.

Cookieless world: the problem of attribution

Attribution is one of the main challenges facin brands in a cookieless world. It refers to the process of identification of the marketing channels or contact points that have led to a conversion or the completion of an action desired by the company (such as signing up for a newsletter).

Without cookies, it can be more difficult to track users across all devices and browsers. Above all, it is more difficult to attribute conversions to specific campaigns or marketing efforts. Companies will therefore lack the data they need to optimise their strategy when it comes to targeting the channels most likely to convert their prospects.

The end of behavioural data

Another challenge for attribution in a world without cookies is the toss of granular data (or behavioral). Cookies enabled companies to track user behavior at a very precise level. They could identify which pages they had visited, the time spent on each of them and the actions they had taken.

Without cookies, it becomes more difficult to identify contact points and CTAs that generate the most on-site conversions. But it also makes it harder to target the firctions that may be causing the brand to lose out on commercial opportunities.

A world without third-party data

Finally, the end of third-party cookies also marks the end of third-party data. Third-party data is collected and shared by other companies.

Yet this data is generally used to optimise advertising campaigns aimed at a specific audience. For example, a retailer might use this type of data to send ads to users who have recently searched for a product or who have shown an interest in a product category.

With the disappearance of cookies, marketers will have to rely on other methods to identify users and understadn their behavior. What may appear to be a colossal challenge to overcome will prove to be so in the long term, an opportunity to optimise your data collection strategy.

Companies will therefore have to implement new tools to collect more reliable and complete proprietary data.

Gamification to collect first party and zero party data

Companies can focus on collecting data voluntarily shared by their customers and prospects (also known as zero party data and first party data).). En collectant des données directement auprès de leur audience, les spécialistes du marketing peuvent mieux comprendre leurs préférences et leurs comportements. Ils peuvent ensuite cibler leurs campagnes marketing plus efficacement.

Companies can also work to develop data collection practices that are more transparent and ethical. The use of interactive levers such as marketing games. makes data collection funnier. But it also introduces a stronger bond of trust between the brand and its customers.

Explore alternative identifiers

As third-party cookie disappear, advertisers are exploring alternative identifiers to track user behavior. These may include email addresses, device identifiers or even hashed data.

Be careful, however, to respect user confidentiality and existing regulations. Generally speaking, advertisers will have to learn to strike the right balance between personalising their marketing campaigns and respecting their customers’ privacy.

Unify data from several sources

Tools and techniques also exist to extract and unify data from several sources. The whole point of these solutions is to make it easier to collect and analyse behavioural data across all the channels used by customers. Then they can be activated more effectively to create and distribute advertising campaigns in the right place and to the right people.

Conclusion

While the transition to a cookieless world can be a complicated time for marketers, it also provides an opportunity to reassess their data collection strategy. By adopting less intrusive and more participative solutions and strategies, you can get to know your customers better and retain their trust.

Discover our interactive marketing experiences for collecting first party and zero party data in complete transparency!

Data collection has become a crucial resource for brands, offering a powerful lever for personalising experiences and optimising campaigns. This guide will help you to understand the challenges of data, even without technical expertise, and to use it to boost your company’s performance.

SUMMARY

• 6 reasons for data collection
• What are the different types of customer data?
• Which data collection method should you choose?
• Playable Marketing to optimise customer data collection: the example of Leroy Merlin

Brands can’t navigate by sight. To make the right decisions and above all refine their strategy to remain competitive, they need to understand who they are talking to and what their audience wants. The data enables them to refine their customer knowledge and gain better visibility of their operations.

So why collect data at all?

1. Getting to know your customers better

Data enables brands to identify the expectations, behaviours and preferences of their customers. By understanding their buying habits, interests and online journeys, they can refine their strategies and improve their offering.

Example: A retailer can analyse its customers’ purchasing history to identify their favourite styles. Based on these insights, it can send them recommendations. This is what Legrand did with its campaign for the Céliane brand. Thanks to the interactive mechanism Swiper, Legrand was able to collect more than 115,000 customer preferences.

2. Optimising your marketing campaigns

Using the customer data collected, a brand can precisely target its customers and optimise the ROI of its campaigns. By analysing behaviour, the right messages can be delivered to the right people, at the right time and in the right place.

Example: By tracking the pages visited and the products added to the shopping basket on its site, the retailer can better retarget each of its visitors with personalised advertising.

3. Improving the customer experience

A brand that makes good use of its data can offer a smooth, intuitive and enjoyable shopping experience. Thanks to the data collected on its e-commerce site or its application, it can identify friction points in the customer journey and adjust its experience (by simplifying the checkout process).

Example: An e-commerce company can analyse customer reviews to understand the main frustrations. In response, it can set up a chatbot capable of providing answers to the most frequently asked questions and thus improve its product sheets by including more details.

4. Customise offers and promotions

Rather than sending the same promotions to their customers, brands can use data to propose personalised offers based on consumers’ habits and preferences. These incentives increase loyalty and encourage customers to return.

Example: A supermarket chain can analyse its customers’ regular purchases and offer them targeted discounts on their favourite products. These incentives encourage loyalty and strengthen attachment to the brand.

5. Anticipating demand and managing stocks

Good stock management is essential to avoid stock-outs and limit unsold goods. Thanks to the data it collects, a brand can predict trends and adjust its production/supply.

Example: It is in a brand’s interest to analyse buying trends in order to estimate demand for its models. Using this information, it can adjust its production to avoid stock-outs on models while limiting overstocking on less popular items.

6. Create new products or services

Insights gained from customer data can be used to identify new needs and adapt the existing offering. This can lead to the improvement of a product or the launch of a service to meet the expectations of its audience.

Example: A beauty brand can use the Customizer mechanism to sound out the desires of its community and offer them products that meet their needs.

Data is essential information to the management and profitability of a business. The good news is that
there’s no shortage of customer data. A survey revealed that the organisations surveyed manage an average of 3 petabytes (PB) of data, and that this volume doubles every two years.

To avoid being overwhelmed by data, it’s important to focus on the data that can make the difference to your company’s development.

1. The different types of data according to their content

We can start by differentiating customer data according to its content and the information it reveals. Here are the 6 types of data that are useful for brands:

• Demographic data, i.e. information that characterises each user, such as their age, gender, place of residence, marital status, level of education, etc.
• Behavioural data, collected by observing actions. Examples include clicks, time spent on a site or previous purchases.
• Geographical (or geolocalised) data provide information about physical location. This includes data such as location or IP address.
• Transactional data relates to purchases made (products purchased, frequency of purchase, average basket, etc.).
• Psychographic data concern interests, values, opinions and lifestyles. They go beyond demographics to capture consumer motivations and attitudes.
• Contextual data refers to information about the context of the interaction, such as the time of day, the location or the device used.

2. Different types of data depending on their source

But we also need to differentiate between data depending on how it is collected and its source. There are Zero-Party Data, First-Party Data, Second-Party Data and Third-Party Data.

1. Zero-Party Data

Zero-Party Data is information that consumers voluntarily share with the brand. This includes personal preferences, opinions, purchase intentions, wishes or specific feedback.


Advantages :

• User consent, which strengthens the trust between the brand and its audience;
• This data is invaluable for personalisation, as it reflects what users want or expect from a brand.
• Compliance with regulations (in particular the RGPD), as consumers have explicitly shared this information.

Disadvantages :

• They are difficult to collect because they depend on the goodwill of the consumer.
• There may be a bias in the responses if consumers are not honest or do not wish to share certain information.

2. First-Party Data

First-Party Data is data collected directly by the brand, through interactions with users on its channels (website, application, social networks, etc.). This includes information such as purchase history, browsing behaviour, account information, etc.

Advantages :

• This data is proprietary and therefore available to the brand.
• Collected directly, without the intermediary of third parties, which guarantees a certain reliability and protection of personal data.
• Easier to manage and analyse because the brand has total control.

Disadvantages :

• First-Party Data can be limited in quantity, as it depends on interactions with the brand.
• Large-scale collection is more difficult, particularly for brands with a small audience.

3. Second-Party Data

Second-Party Data is First-Party Data collected by another company. In other words, it is data collected by a partner and then shared ethically between the two parties.

Advantages :

• Allows brands to access data that they would not collect themselves, particularly if they do not have a very large customer base.
• Less intrusive than buying data from third parties (as in the case of Third-Party Data), because the data comes from partners with whom there is a relationship of trust.

Disadvantages :

• Limited availability, as you need to establish partnerships with other companies that share relevant data.
• The data may be less personalised than First-Party Data, because it comes from a different source.

4. Third-Party Data

Third-Party Data is data collected by third-party companies (other than the brand or its direct partners) and resold or shared with brands. These companies specialise in data collection and gather information about behaviour on different websites or across different platforms.

Advantages :

• Reaches new audiences that the brand would not otherwise have been able to identify.
• Can offer highly detailed data on user behaviour, consumer trends, etc.

Disadvantages :

• This data may be less accurate or reliable than First-Party Data, as it may lack context.
• Confidentiality issues: Using this data without users’ consent may entail legal risks (particularly with regulations such as the RGPD).
• Collecting this type of data can be perceived as intrusive, and some consumers use tools to avoid being tracked (e.g. ad blockers, VPNs).

To sum up, here is a summary infographic to help you remember the most important information:

Now that we have identified the types of data and the uses that companies can make of it, we turn our attention to data collection.The organisations have a range of tools
which can be used (in synergy) according to their objectives and needs. Here are the most effective.

1. CRM (Customer Relationship Management)

CRM software (Salesforce, Hubspot, Brevo) is used to collect, organise and analyse data throughout the buying process. These tools centralise the information collected on prospects and customers.

Use case :

• Enables First-Party data to be collected (contact information, purchase history, preferences, etc.).
• Helps personalise customer relations by centralising relevant information.
• Automate the collection and use of customer data by integrating other marketing tools (emailing, chatbot, etc.)

2. Gamification tools

Gamification consists of integrating game elements (interactive mechanics, challenges, rewards, etc.) into communication to encourage users to interact. Playable marketing attracts attention, stimulates engagement and collects data in a fun and less intrusive way than a form.

Use case :

• Game marketing boosts user engagement and encourages them to provide data (via a contact form before or after the game) in exchange for a stimulating experience and/or rewards.
• Certain mechanisms can make it easier to collect first-party data and identify needs and preferences. This is the case with Swiper or Battle, which identifies the specific interests or behaviour of each user.
• Surveys (or open quizzes) are gamified mechanisms for asking questions on a range of subjects (preferences, purchasing intentions, satisfaction, etc.).
• Gamification can be used to energise a loyalty programme to encourage redemption and provide the brand with accurate data.

Ma Gare + ran a marketing competition, including a survey of travellers’ habits and their desire for station facilities. This quiz enabled Ma Gare + to collect more than 15,000 new qualified leads, many of which were new accounts.

3. Opt-in and data enrichment solutions

Opt-in collection solutions allow explicit consent to be collected (when subscribing to a newsletter or service). This type of collection makes it possible to enrich the data initially held on users, added to their profile from external sources.

Use case :

• Obtain explicit consent for data collection, thereby complying with regulations (e.g. RGPD).
• Collection of First-Party data to enrich profiles and understand their needs. When people sign up for a newsletter, they may indicate their interests or their location. This data is used to personalise communications according to preferences, by sending geolocated promotions.

4. Behavioural analysis tools

Behavioural analysis tools make it possible to track and analyse behaviour on a website or application (pages visited, time spent on each page, actions taken). These tools are invaluable for collecting behavioural data and understanding interactions with the brand.

Use case :

Tools such as Hotjar can be used to create heat maps to improve the design of the brand’s website or application. They help to understand which elements are noticed by users and which can be ignored. This helps designers to improve the interface by highlighting key elements.

In the same way, behavioural data can be collected by recording visitor sessions or analysing user engagement. This is also useful for optimising the user experience as well as conversion rates.

5. Tools for collecting data on social networks

Social network data collection tools allow you to retrieve information from interactions on platforms such as Facebook, Instagram, LinkedIn and so on. This includes behavioural, demographic and even psychographic data.

Use case :

• Provides access to a wealth of data on online behaviour.
• By tracking interactions and analysing data on social networks, we can understand their interests, opinions and attitudes, and therefore optimise content.

The choice of data collection tools therefore depends on the brand’s objectives, resources and the type of data it wishes to collect. It is advisable to combine these tools to obtain a complete picture of its audience while complying with the rules on consent and data protection.

Playable Marketing is one of the most effective methods of collecting data. By incorporating game elements into the collection process (quizzes, competitions, surveys), this lever offers a number of advantages:

• The collection of accurate data, shared by consumers ;
• Increased audience engagement through access to enriched information;
• Reducing friction in the collection process. Fun experiences to make it more enjoyable and less intrusive.

For these reasons, Leroy Merlin chose gamification to optimise its data collection strategy. The ‘Renovation’ campaign was based on a Swiper to identify its audience’s renovation projects.

This game enabled Leroy Merlin to collect leads (creation of customer accounts) and qualify them according to their preferences. Each lead was segmented according to its intentions and projects (kitchen, bathroom, decorating preferences), enabling the brand to send offers tailored to the needs of the participants.

The campaign has increased Leroy Merlin’s visibility and brand awareness. It inspired customers and prospects to launch renovation and home improvement projects. The campaign resulted in the collection of 3M pieces of information and increase traffic to the site with over 40k clicks.

Conclusion

Collecting data may seem like a complex subject, but there are a number of tools that can make the job easier, while improving the relationship and trust between your brand and your customers. Discover our interactive mechanisms and use gamification as a powerful lever for collecting qualified data!