12 October 2020
Too many marketers are still relying on inaccurate first-party data. In this article, Joachim Sontag, Consulting Director at Artefact Germany, explains how brands can clean up their dirty data’’, build a connected data loop, and future-proof their data architecture.

As the coronavirus pandemic accelerates the shift towards online living, brands are under increasing pressure to serve customers personalised messages and offers. However, at the same time, consumers are pushing for evermore privacy and control over their personal information.

With the approaching death of third-party cookies and the tightening of GDPR restrictions, Proprietary and first party data will take centre stage in marketers’ efforts to understand, grow and target audiences. Yet years of neglect haven’t been kind to owned data. Fragmented, siloed, poorly categorised and managed, such ‘dirty data’ does little to deepen customer understanding.

In the new environment, brands must map out a cohesive first-party data strategy. They need to transition from a system of point-of-purchase marketing to one that is more precise, first party data-driven and based on direct-to-customer communication. First, however, they need to build an integrated and agile data infrastructure to underpin it.

Quantity won’t make a quality customer relationship

From the outset, a brand has to define its objectives before it can start building its data infrastructure. This determines what the platform is optimised to do and what first party data it will collect. When it comes to data generally, too many brands make the mistake of focusing on quantity rather than the relevance of the data. Brands have millions of sources they can draw first party data – from a single webpage to the individual app on a customer’s mobile device – and the number is growing all the time. Indeed, Web traffic grew 8% last year, averaging 223 billion visits a month. Yet, if the data doesn’t align with your objectives, how does it help?

It’s precisely when data environments get too large that they become unmanageable. Every byte of unnecessary data only adds to the amount of time a marketer or marketing automation programme needs to trawl for the information they need. There’s also a tendency for data to spill into other environments — including, the cloud — to avoid maxing out storage capacity. This only contributes to fragmentation and the danger of key data becoming lost in the system.

It’s important, therefore, to clearly define a brand’s data and analytics use cases upfront. This involves determining who your target customers are and what traits and behaviours are the most profitable for your brand. You can then translate this into data signals. This is the first party data you need to be collecting.

There’s a tendency to push for ever-greater personalisation in customer interaction. However, with the quantities of data at play, audience segmentation is crucial for reducing complexity and preserving integrity. Being able to segment users and previous customers into granular cohorts, at scale, is increasingly important in helping marketers identify the most relevant data to collect from user groups.

The circle of data

Once you’re confident you are collecting the right data, you need to ensure the different tools and systems are working together. The goal is to build an ecosystem of best-in-class tools that give you a single, consolidated view of customers, and the ability to track and target them rapidly. Integration is the crucial first step towards marketing transformation. There are many ways a company can do this. By bringing all its first-party data together on one cloud platform – which has unlimited storage, is scaleable, is available anywhere, works with existing APIs and in real-time – an organisation can properly analyse it to gain a richer understanding of its customers. However, it could also work with data experts – internally and externally – to develop APIs to connect all their tools and systems.

The benefits of a platform-based approach are two-fold. Time-consuming database resets are no longer necessary, as an error only needs to be corrected once and is updated simultaneously across all environments. More importantly, integrating all your data allows marketers to directly query the entire database with record response times, and no need to prepare data ahead of time.

However, the process needs to be transparent and efficient to ease reporting and ensure regulatory compliance. An important part of this is data cleansing, which consists of cleaning data to prepare it for analysis. This is where third-party tools can still help. A company can perform it manually but at great cost. Instead, for the sake of speed and simplicity, a brand could opt for easily integrated third party solutions that automate the process, such as Data Ladder or OpenRefine. It’s important when selecting tools for data integration and cleansing, not to become too dependent on one solution or technology, however. Success by itself is no longer enough – increasingly, it needs to be scalable and sustainable as well. Building your own solution gives you greater power to customise, but it’s not efficient as your needs, customer requirements and traffic fluctuate.

Seeking a third-party solution often lowers your risk and total cost of ownership, enabling you to be more agile and swap out solutions as the need arises. Yet, there is no one-size-fits-all, off the shelf solution that will meet all your needs from day one. That’s why it’s so important to work with a technology expert that’ll help you define clear requirements and select or build a system that’s tailored to your needs – whether it’s a single platform or best of breed approach held together by APIs.

A robust, flexible data infrastructure is the hallmark of sustainable first party data strategy. When your objectives are segmented, your tools integrated and your data cleansed, you build a virtuous data loop that drives customer engagement. Relevant first-party data is collected, cleansed and analysed in one continuous, efficient process. Dirty data is banished and customers receive targeted recommendations rapidly and efficiently.

Article by Joachim Sontag initially published on

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