CDPs (Customer Data Platforms) combine data from multiple tools to create a segment-able database of your customers, helping to create countless personalised marketing campaigns. There’s a bit of a buzz surrounding them - they’re often touted as the ‘next big thing’ in marketing technology. But, as a CMO, what should you be considering if you’re thinking about implementing an enterprise CDP? And, crucially, should you build a bespoke CDP or simply purchase an ‘off the shelf’ version?

Build vs. Buy

Forward-thinking CMOs are aware of the power of data-driven marketing - a deeper understanding of customers which leads to personalised and highly-targeted marketing, deeper engagement, and ultimately, more conversions. CDPs unify this data, and work in conjunction with intelligent and automated technology to help CMOs gain the insight to help them target the right customers-in the right place, at the right time. 

The CDP works in conjunction with other elements of the MarTech stack and can be considered an addition rather than a replacement, according to the needs of an individual organisation. It can not only enhance existing solutions like CRMs, but also has the flexibility and scalability needed to help a growth-focused business reach its goals. 

The ‘build or buy' question poses a problem. However, one of the main things this can be boiled down to is whether the data model or use case itself is priority, or the speed at which actionable insights can be gained, and therefore conversions and ROI. If the former is most important, building may be the best solution. If your priority is time, then purchasing an existing CDP is the answer. 

Pre-built CDPs which are tailored to your sector can offer a good fit with your organisation and marketing strategy, and a fast ‘time to value’ return. 

CDPs and DMPs

The CMO Council’s ‘Getting it Done in 2021’ report found that 69% of marketers plan to increase their MarTech spend in this year - with the investment priority being analytics, insights and intelligence. There was also found to be a gap in the ability to understand the customer journey, automate processes and engage. Difficulty in integrating MarTech solutions is an all too-common issue for marketers when it comes to delivering against marketing and business goals. 

Part of the issue with a reluctance to adopt a CDP comes from (as with many other similar solutions) a lack of understanding. Early CDPs were similar to Data Management Platforms (DMP), but with much greater technological ability, including the ability to deal with Personally Identifiable Information (PII). This led to versions of DMPs effectively masquerading as CDPs, leading to confusion and to many CIOs writing off CDPs as yet another data management tool. 

For businesses who prioritise first-party customer data, and use omnichannel marketing strategies, it’s crucial to understand the benefits an enterprise CDP can bring to marketing and advertising campaigns. It’s a simple fact that customers prefer receiving personalised messages based on information they’ve shared with a brand directly - and this is where CDPs can be worth their weight in gold! 

The temptation to rush and buy a CDP is strong given their perceived value, however it is all too easy to add to the complexity of your Ad-MarTech stack and inadvertently create another data or team silo as a result.  

Considerations for CMOs

Identity Resolution and Graphs

Marketer visibility over consumers, and the consumer journey itself, are becoming increasingly fragmented - even more so as we move into the post-cookie online world. Consumers no longer browse online on a single device - they switch from laptop to desktop, from mobile to tablet, to making purchases on less ‘traditional’ devices like connected TVs, games consoles and even voice assistants. Therefore, to gain an accurate picture, it’s necessary to link devices to a consumer in order to gather data on their habits.  

Identity resolution involves creating a unified profile for individual customers by gathering data on all the interactions they've had with your business. This profile of unified data can then be used to better engage and serve each customer. Identity (ID) graphs provide a database that holds not only these consumer profiles, but also all known identifiers that correlate with individual consumers. 

In the average consumer journey, many types of PII can be linked to any one consumer, such as addresses (both postal and email), contact numbers, and usernames. These identifiers are all collated into the identity profile within the ID graph, alongside relevant behavioural data, such as past purchases and browsing history.

The best methodology to use in ID graphs is a topic of much debate, and the two camps here when it comes to matching data are deterministic and probabilistic. While deterministic approach is sometimes referred to as the ‘golden record’ in data matching, probabilistic is more of an ‘educated guess’.  

Deterministic matching uses PII to facilitate an accurate match - based on data that is 100% certain - such as a previous purchase, use of a payment method linked to a customer, or a customer logging on to a website or app. Probabilistic matching is more scalable, and estimates the probability of a consumer being linked to a certain device through predictive algorithms.  

Big Data Analytics

Big data is growing at an exponential rate, driven on further by both open-access ‘democratisation’ and the expanding reach of the Internet of Things (IoT). In many cases, businesses simply have more data than they know what to do with. There’s also the issue of linking online and offline data in a usable way. This is where using a CDP to unify data can help. 

Using a CDP with AI and machine learning technology is becoming increasingly popular. With the amount of data a CDP absorbs the potential for it accurately predicting consumer behaviour becomes clear. 

This can help not only with ‘hyper-personalisation’ in marketing, but also in recognising game-changing ‘black swan events’ that cause a change in consumer behaviour and therefore require strategies to pivot. Although at present there are limitations to AI capabilities in a CDP, the logical step is evolution to a Customer Intelligence Platform (CIP).  

AI can also be useful in multi-touch attribution, evaluating the impact that each ‘touchpoint’ has in driving conversions, providing visibility into the value and success of various ‘touchpoints’ across the consumer journey. Again, this helps with customising marketing outreach to meet consumers at the right place, at the right time. 

Data in Real Time

It’s important for CMOs to consider that a CDP can create a customer profile in real-time. This helps to provide a targeted, personalised experience across multiple channels - including and of course not limited to customer interactions through email, direct messaging or website chat.

Speed of response is important in giving you a competitive advantage, and real-time data gives you the ability to immediately spot opportunities to push certain campaigns, deal with issues more quickly and achieve higher levels of agility within your outreach.

Simply put, real-time data helps you meet the real-time demands of business. It makes sense that going forward into an increasingly competitive market, enterprise systems will need to incorporate some form of continuous intelligence using real-time contextual data to help them make better decisions. 

Scalability

CDPs are often ‘pigeonholed’ as a solution that’s meant exclusively for the marketing department. However their value extends far beyond this, with a scalable infrastructure and the computing power to support many use cases. As the CDP market grows and shifts further towards the mainstream, it’s likely that many more functions within business will become apparent. 

CDP use isn’t confined to organisations with complicated, multi-tool technology stacks, either - although it’s true they can provide a multitude of benefits here. However, smaller (or growing) companies can also obtain great value from a CDP, taking advantage of the data processing, segmentation and identity resolution capabilities. Unifying data effectively ensures it remains ‘clean’ and usable across the organisation, avoiding data silos and poor collaboration. 

The finance department can also use this real-time to help with decision making in investments and spend, or the advertising teams can use the CDP to activate consumer intelligence most effectively. The use cases are countless!

Privacy and Compliance

An important benefit of CDPs is how they can help with aiding compliance through the unification of data. With better visibility, the data can be governed more accurately, and obligations such as Subject Access Requests are made much easier to complete when they occur. 

Consumers and regulators alike are taking a much more vested interest in how brands and businesses use customer data, as evidenced by GDPR and CCPA, as well as the other global compliance and data privacy laws in place. 

Enterprise CDPs must take into account the ever changing landscape of data privacy, compliance and residency, ensuring rules are easily defined at the point the data is captured, wherever this might be in the world. Additionally, provisions need to be made with data processing for those countries that have strict data residency laws, not allowing citizen data to leave their borders.   

As things move increasingly online, the volume of data generated is only set to keep growing. The IDC estimates that by 2025, 75% of the global population will be interacting with online data daily. These huge datasets will be a challenge to work with, and businesses will need to leverage the latest solutions in order to harness the full potential of their data. Traditional solutions will struggle to keep up - as many are already. 

CDPs are evolving fast, and choosing the right model with the necessary scalability and computing power will not only prove to be a valuable solution for today’s CMO, but a solid foundation for forward-thinking businesses to incorporate the growing need for data unification and analysis into their growth strategy. 

Alex has enjoyed the last 21 years in Ad-MarTech. After leaving Oracle of 10 years, where he led different regions of the Marketing Cloud business and worked closely with the Advertising (formerly Data Cloud) business, he has since launched an Ad-MarTech growth network. This focuses on headless software capabilities for today’s CMO, complementing their existing Ad-MarTech stack without having to contend with the pain of replacing deeply integrated applications. 

To learn more, give Alex a call: +447591220113, or email - alex@mysupero.net

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