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.

然而,这一过程需要透明和高效,以方便报告和确保监管合规。这方面的一个重要部分是数据清理,包括清理数据以准备分析。这是第三方工具仍然可以帮助的地方。一个公司可以手动执行,但成本很高。相反,为了速度和简单起见,一个品牌可以选择容易集成的第三方解决方案,使这个过程自动化,如Data Ladder或OpenRefine。然而,在选择数据整合和清理的工具时,重要的是不要过于依赖一种解决方案或技术。成功本身已经不够了--越来越多地,它也需要可扩展性和可持续性。建立你自己的解决方案给你更大的权力来定制,但随着你的需求、客户要求和流量的波动,它并不高效。

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 TechNative.com


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