Data quality to resolve 3rd party cookies ban
Marketeers and dedicated advertising benefit from good data quality
Google announced its intentions to kill off the tracking cookies (so called 3rd party cookies) within its Chrome browser. Cookies which advertisers use to track users around the web and target them with dedicated ads. Google is not the only major player altering the digital ad landscape. Apple has already made changes to restrict 3rd -party cookies, along with changes to mobile identifiers and email permissions. Big Tech altering 3rd party cookies is caused by the need to be respectful of the growing data privacy consciousness. Most consumers don’t like the feeling of being tracked across the internet (70% of U.S. adults want data regulation reform and 63% of internet users indicate that companies should delete their online data completely).
For most marketeers, this paradigm change presents huge challenges to enable customer acquisition by tracking users and targeting them with dedicated digital advertising.
On the other hand, 3rd party cookies are inherently problematic, from limited targeting capabilities, inaccurate attribution to the personalization & privacy paradox. Their loss presents an opportunity to provide a smaller group of high-value customers with higher-caliber and increasingly personalized experiences. In other words, losing these cookies might become a blessing in disguise.
Confronting data acquisition challenges in a cookie-less future
For all the shortcomings of 3rd party cookies, the marketing industry does not yet have a perfect answer for how to acquire customers without them. Marketeers are waking up to the impactfull change they are facing. One potential answer to the loss of 3rd party cookies can be that they will be replaced with 1st & 2nd* party data, i.e., gathering data shared directly by the customer, such as an email address, phone numbers and customer authenticators (see below). This data can become the mutual currency for the advertising business. First party data can be hard to obtain, you need to “earn” it, including solutions on how to gain good quality 1st party data.
Some solutions focus on technology, e.g.,Google’s Federated Learning of Cohorts (FLoC). A type of web tracking that groups people into “cohorts” based on their browsing history for interest-based advertising. Other technology solutions include building a 1st and 2nd party data* pool, i.e., a Customer Data Platform (CDP). CDPs are built as complete data solution across multiple sources. By integrating all customer data into individual profiles, a full view of customers can be profiled. Another solution are private identity graphs that hold all the identifiers that correlate with individuals. Private identity graphs can unify digital and offline first-party data to inform the single customer view and manage the changes that occur over time (LINK?). this helps companies to generate consistent, persistent 360-degree view of individuals and their relationship with the company, e.g., per product brand. All to enable stronger relationships with new and existing customers.
Earning good quality data will increase the need for standardized and good quality customer journeys. And therefore, the need for standardized and good quality data.
Where previously, design and data quality were not closely connected, the vanishing 3rd party cookies now acts as catalyst to integrate both.
Data quality is usually an unknown phenomenon for most designers**, design companies, front- & back-end software developers and marketeers. It requires a combined understanding of multiple domains, i.e., the user interface where data will be captured, the underlying processes which the captured data will facilitate, data storage & database structures and marketing (analyses) purposes.
Finding the expert that has all this combined knowledge is like finding a real gem. If you do, handle with care!
It will be more likely that all domains will need at least an understanding how their domain enhances and impacts the other domains.
For the (UI/UX) designer:
- Have a good knowledge of data quality rule types. What is the difference between a format & accuracy type? Is timeliness of data relevant? What are pitfalls for data quality rules? How to integrate multiple purposes (e.g. processes, data integration & analytics) into a dedicated data quality rule.
For process owners:
- Ensure that expertise of data entry and how data is used within processes at a granular level (i.e., on data field level). Onboard a so-called data steward who can facilitate the correct input for data quality. Let the data steward cooperate with front-end developers and designers.
- Keep your data fresh. Data doesn’t last forever. Make sure data stewards support data updates and cleansing.
- Data stewards should work with designers and front-end developers to determine which fields are considered as critical. These fields should be governed by a strict regime, e.g., for the quality and timeliness of data as well as for access to the data and usage purposes.
- Personal authentication is a separate topic that needs to be addressed as such. Relying on big tech firms as Facebook or Google can seem an easy solution, however increases the risk of being dependent on an external party. Yet authentication needs to be earned to build authentic customer relationships. When customers give a company a verifiable durable pieces of identity data, they are considered authenticated (e.g. signing up for a newsletter or new account via email address). This will be a new way of working for most companies. Therefore, data stewards need to up their game and not only know existing processes but extend their view, understanding and knowledge towards new developments.
- Data stewards must align with the Data Privacy Officer on how to capture, store and process data. When it comes to privacy, compliance and ethics, you can never play it too safe.
For data storage & databases:
- Ensure that data architecture (or at least a business analyst) is involved in the design process. This is sometimes resolved by the back-end developer (who cannot work without aligning with the architect office on data integration, models for databases and data definitions).
- If standardized data models and/or data definitions reside within the organization, this should be part of the database development. Refer to authoritative source systems where possible.
- If the application is made via low-code, standardization of existing data models/architecture, data definitions and data quality rules is often part of the approach. Yet, data quality checks should always take place as separate activity.
- Understand how customer journeys can facilitate 1st and 2nd party cookies. Determine which data is needed for insights. Gather insights requirements and work together with the data steward to define data quality rules that facilitate your insights. Now that the 3rd party source is limited, the value of the customer journey for marketing increases!
- Privacy is one of the catalysts to make 3rd party cookies disappear. This requires a new approach for acquiring personal data for marketing and ad targeting. New developments that require new skills and more importantly, a new cooperation between existing domains. Companies that enable this, will lead this new way of working.
* Data from 1st party cookies = occur only within a company’s own domain. & data from 2nd party cookies = ca be used within and outside a companies’ own domain. This article takes mostly 1st party data into account. For 2nd party data, you can further investigate e.g., ‘data co-ops’, complementary companies that share data. Each member of the co-op should relate to the others in a meaningful way because outside of your own web domain, you’ll be able to reach customers only on your partner sites — and this reflects on your brand.
** Of course, there are designers work who work with data enabled design. In the view of this article, this is a different topic, more focused on tracking & logging data, which is then analyzed to improve the design. This article is about good data quality when data is entered via a UI, e.g., as part of a customer journey.