02:56: What is a fit-for-purpose customer data foundation, and what is the state of the market today?
A brand’s customer data foundation should offer a full, continuous, 360-degree Single Customer View that is fully activated:
- Full: All data going into a connected view
- Continuous: Consistently updated with both behavioural and transactional data, giving you the most recent information available
- Activated: Data is seamlessly integrated into marketing and BI tools
The market has a long way to go to achieve these three key requirements. As a result, brands often buy solutions to fix the “symptoms” of ineffective marketing (e.g., point solutions) rather than fixing the root cause (the underlying data infrastructure).
Fit-for-purpose data platforms can power marketing across a number of areas, but in this discussion, we categorised them across three areas:
The platform has democratised data across the business and delivered a holistic view of both customer and product. It’s also enabled data storytelling, making it simple to take data and give it to stakeholders in a way they’ve never seen before. This data storytelling approach has helped drive the businesses to adopt a data-driven decision-making framework.
14:21: What is the impact of a solid customer data foundation on artificial intelligence (AI)/generative AI?
Businesses that flourish during times of change (such as Covid) are the ones that are data mature and data literate. The same will also be true about AI adoption. The businesses that will be able to “make the impossible possible” with AI will be the ones who are already data mature and have data that is unified into a single source of truth.
23:08: Can AI and machine learning make targeting and cross-channel orchestration more effective and efficient?
Yes, but only if your core data foundation is fit-for-purpose AND you have the tools that enable you to benefit from that wide range of data. Not to mention, once you have your audiences and insights, it should be simple for marketers to then push them out to the relevant touchpoints or optimise their campaigns with agility. From the point of analysis and insight through to activation, everything should be connected.
33:39: What data should go into a data foundation?
Starting with the digital data is usually the first port of call. This includes anonymous and known behavioural data, such as browse data. This should be combined with transactional data, including in-store where applicable, so you can get a full view of the customer.
In addition to customer level data, product, store and marketing content data will all enhance the platform so you can have a fuller picture for analysis and insight. Other sources include channel engagement data, product reviews, complaint data, and any data sources that are unique to your brand. Ultimately, your data foundation should be flexible to grow with your business.
38:40: What should brands think about when selecting a supplier to minimise resource requirement?
In terms of minimising resource requirement up front, your data platform should flex to fit your data. There should be many ways that your data platform can ingest data, from APIs to batch files. A robust data platform should be able to ingest data in raw format, reducing the effort from IT teams to transform it.
System integrations are another key consideration. Ideally, your data platform will have out-of-the-box connectors for many (or all) of your channel tools, meaning you won’t need to involve IT to set them up.
Ultimately, your business is unique, so your data foundation or customer data platform should be able to model your data in a way that it works for your business.
Ready to finally fix your customer data foundations for good?
