In this on-demand session, the BRC's Kris Hamer is joined by Owen Eddershaw to discuss next-generation Customer Data Platforms. Together, they explore the most significant shifts within the Customer Data Platform (CDP) market and look more closely at some of the ways in which innovators in this space are beckoning the next generation of CDPs.
In recent years, it has become increasingly difficult for retailers to differentiate between the options in the market as marketed capabilities seemingly converge. However, by defining the market landscape more clearly and recognising the fundamental issues with existing solutions, join us to support your journey to building a future-fit customer data infrastructure.
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Next generation CDPs - Key Takeaways
For this webinar, we are joined by Owen Eddershaw, an associate of innovation at True and a self-described nerd for helping businesses create a future-fit customer data infrastructure.
Owen shares his insight with our retail members on some of the key issues with conventional customer data platforms available on the market, and how next-generation customer data platforms can support retailers to overcome their challenges with customer data.
Primary goals with customer data
In the first webinar poll, it was identified that the key challenges experienced by retailers are personalising the data, using this data for conversion, and disparate or unstructured data – to sum this up, our retail attendees value having organised, personalised data that can be applied for optimising conversion and marketing efforts.
Why use a customer data platform?
Organisations have a whole host of sources of data spitting out insights, from your CRM to your web analytics to your sales and more. Customer data platforms aim to ingest data from the various sources you have in your organisation, store it in a single unified source of truth, and then make it presentable for teams to draw insight from, whether that’s for data analysis or personalised marketing efforts.
With customer acquisition prices high and consumers valuing a personalised customer experience, using intelligent customer data for your marketing and conversion efforts is more important than ever. However, without the right intelligence from first-party data, many businesses rely on third-party cookies to get the data they need – and this strategy might not be around for much longer.
It’s likely that these businesses already have all the data required, but it is unstructured and thus not useful; this is where customer data platforms are vital for breathing life into the data you already have, making it available for marketing and business insights.
How does a customer data platform work?
Traditional customer data platforms work on a model of ingestion (taking in data from your various sources), storage, unification (matching data to specific identities), segmentation (perhaps using rule-based or AI-driven segmentation) and connection to activation channels.
In short, traditional CDPs take in your data, store it, match it to specific customer profiles, and then feed out personalised information your teams can use to personalise messaging and boost conversions. This functionality is critical for personalising your messaging and understanding your customers, which is why CDPs are growing so quickly as an industry.
The question is: is this the most effective way a customer data platform can function?
Issues with the current CDP market landscape
In the full webinar, Owen highlights data-focused, orchestration-focused and activation-focused CDP options currently available on the market. However, despite the varied categorisation, he highlights a problem with today’s CDP offerings: there is very little differentiation between them. This makes it difficult to tell which platforms stand out as the best-in-market, as many have similar functionality and produce similar results.
There is room for a next-generation CDP to break new ground in the data personalisation space, and this means potential for retailers to better leverage their data.
Barriers to success when using CDPs
In the second webinar poll, attendees identified that their main barriers to success when using CDPs are the complexity of implementation, followed by stakeholder engagement, cost and time. Getting insightful data from a variety of data sources and using it for effective personalisation requires not only an accessible data platform, but also the time and engagement of a variety of stakeholders within your team – and if a platform is not intuitive to use, or is complex to implement, these issues are self-compounding.
These problems are also clear within stats around CDPs: just 23% of consumer marketers have completed their CDP projects on time, and 42% of companies with a deployed CDP share it is not delivering significant value.
Learn more about the specific role-based challenges with CDPs experienced by data scientists and marketers in the full webinar, and find actionable solutions.
Emerging CDP solutions
One of the challenges being solved by today’s emerging CDP solutions is the issue of creating multiple sources of truth. Rather than becoming yet another data silo, new CDPs coming onto the market aim to interpret your data where it lives, not create another platform – this means creating a single source of truth that is simple both to verify and to apply when personalising your marketing campaigns.
Composable CDPs
Composable CDPs incorporate your existing data warehouse into a “build” and “buy” approach, creating an integrated customer data system rather than another standalone silo. They essentially overlay as a flow, integrating directly into your existing data, enabling all of the same functionality of a traditional CDP without moving your data into another data lake.
Another benefit of composable CDPs is that you aren’t locked into a specific provider – as the functionality overlays over your existing data warehouse, composable CDPs can be taken away without compromising the accuracy of your data, giving businesses more freedom over the software they invest in.
Learn more about how composable CDPs work, and how they differ from traditional CDPs, in the full webinar.
Assembly model behavioural modelling
Behavioural foundation models make it simpler to interpret your customer behavioural data. These models analyse the full spectrum of observable data from your customers, including any historical interactions with your brand, and then use functional AI to predict future behaviours in both real and hypothetical situations. This allows you to make informed business decisions with the ability to predict how your customers will respond to changes – and, stacked on top of an integrated composable CDP, can be yet another powerful tool for leveraging your data effectively.
Key takeaways
At the end of the webinar, Owen sums up the best strategy for retailers aiming to create an actionable, accurate data warehouse for your marketing and data analysis teams, incorporating next-generation customer data platforms:
- Map your existing data sources
- Allocate one data storage platform to be your single source of truth
- Introduce a composable CDP that unifies your data into this database without confusing your single source of truth
- Once this core pipeline is established, explore adding additional behavioural modelling tools to make accurate predictions and leverage your existing clean data.
To learn how to do this in more depth, find the link to the full webinar.
This webinar was produced in partnership between the BRC and True; learn more about our partnership here.
We will be hosting additional webinars in partnership with True in the future – explore the BRC Events page and discover our upcoming webinars and in-person sessions, or check out more on-demand webinars.