A Customer Data Platform provides you with a Single Customer View - then what?

Over the last decade, two technology solutions have transformed data-driven digital marketing: Data management platforms (DMPs) and the more sophisticated customer data platforms (CDPs)

CDPs have proven extremely popular and effective because they are capable of ingesting data from multiple online and offline sources and finding common identifiers to create single customer views. There have been peaks and valleys in the success levels of CDPs in delivering on the promise of a single customer view — some have only been able to ingest online data, some have been unable to cope with real-time data. Many of the failures have occurred because when setting the goal of creating a single customer view companies have not taken into consideration what else is required outside of the CDP deployment. In 2019, Gartner even predicted that the CDP will hit the "trough of disillusionment" for the following 5 years - until 2024.

Think big, think business

The first question that should be asked when considering taking on the challenge of creating a single customer view is “what else do we need”? This question is two-fold: What else do we as the team commissioning the CDP need to be able to do once we have a single customer view? And what else does the wider business need that can be achieved through the CDP?

For example, if you want to measure the success of marketing activations in terms of customer retention then you will need to ensure that you have ongoing customer data flowing into the CDP which may require involving your IT, or customer service team.

Another reason that CDPs do not meet the objective of creating a single customer view is because a goal is set - like in the example above - but there is no use case attached to it, i.e. the “what next” hasn’t been defined. This creates a situation where the technology that has been selected and implemented may not be able to support the activation of data in a way that will deliver tangible value.

Standard use cases to get you started

Here are a few examples of relatively basic use cases for a CDP that enable brands to improve marketing return on investment.

Existing customer suppression

When setting up a new marketing campaign a CDP can be used to ensure that existing customers are not targeted. This is achieved using deterministic identifiers that pinpoint existing customers online and ensure they are not exposed to certain campaigns. This means that the entire campaign budget is used to recruit new customers. We worked with a brand to implement this strategy resulting in a saving of £3m in 12 months.

Dormant campaigns

Using a CDP, marketers are able to identify customers who have not been active for a given period of time, e.g. two weeks, three months, one year. These users can then be segmented and targeted through new campaigns, across multiple channels, specifically designed to bring those customers back. This can be an especially useful tactic as it targets consumers who you know already have an interest in your brand or products, they just need reminding!

Multi-channel orchestration

This involves using different targeting rules based on customer behaviour across multiple channels, leveraging real-time feedback loops to create personalised messaging based on customer engagement and segment. The key benefit of this approach is that, much like a musical orchestra, when all channels are working together you get the best result. In marketing terms, this translates to increased customer engagement and improved lifetime value.

Interoperability

This is where we start to look at wider business applications. The ability to stream data, in real-time, from the CDP into business intelligence (BI) tools enables multiple teams to access automated, custom reporting. The benefits of this approach are multi-faceted - marketers can optimise campaigns in real-time against business goals (not just marketing goals), and BI teams can identify areas to improve other aspects of the business, e.g. purchasing and order fulfillment.

Advanced use cases to get you thinking

Second party data alliances

Creating partnerships with other brands and partnerships enables you to target common audiences as well as identify new audiences that share similar characteristics with your best performing audiences. Furthermore, data security and brand safety are not compromised as all data from the allied parties is stored in multiple data warehouses. This is a great way to gain incremental customers and revenue, as well as gathering new insights for future campaigns.

Anti-churn leveraging machine learning

Adding machine learning (ML) to a CDP enables the integration of bespoke ML functions and algorithms that analyse the characteristics of customers who churn, score them, and use the learnings to predict potential new churners. Armed with this insight, marketers can then activate campaigns specifically designed to retain customers at most risk of churn. This, in turn, delivers more insight into the best messaging and channels through which to prevent churn across different audiences. We’ve seen up to 20% reduction in churn using this approach.

Additionally, ML can be used to identify when someone is likely to buy a product and that insight can be used to push out a marketing message designed to get them over the line and make a purchase.

AB testing

One of the major benefits of a CDP is the ability to build AB tests in situ meaning that multiple combinations of channel, creative, time, demographic etc. can be tested in real-time. This speedy delivery of learnings means that marketers can optimise campaigns within hours of launch significantly improving performance and reducing operational costs.

Funnel analytics

When all marketing and customer data is centralised in a CDP it is possible to unlock insights into consumer behaviour and interaction with advertising at each stage of the funnel, in real-time. This enables marketers to react swiftly to changes and optimise an individual’s advertising experience to maximise the likelihood of conversion. At scale, this results in an uplift in conversion rates and revenue.

What’s next for you?

There’s no denying that the CDP has huge potential, not only through the use cases in this article, but many, many more. Whether you already have a CDP and are looking to expand on the functions it performs today, or if you’re thinking about implementing a CDP the most important this is to start at the end… what do you want to be able to do? Don’t get caught in the trap of saying, or thinking “I want a single customer view”. Once you have this clearly defined, then you can work backwards and identify what you need in order to make this a reality.