Achieving Unified Customer Profiles & Future of CX with Beth Pfefferle | Redpoint Global

1. How important is achieving a unified customer profile, and what best practices ensure its accuracy and utility? 

A unified customer profile is a detailed, nuanced, and contextual understanding a customer, a business, a household or another entity that organizations use to provide a differentiated customer experience (CX) – or to power AI, analytics, and operations. It’s a critical part of a solid data foundation that turns a company’s customer data into business value.

A unified profile must be complete, accurate, and timely for marketers and business users to completely trust that it represents the customer they’re engaging with. A few best practices elevate a true unified customer profile over simply aggregating customer data from various sources, applying a simple match and calling it a day. 

The most important is to continuously apply data quality steps at data ingestion, not downstream. Accuracy depends on not only ingesting data from all possible sources, but also in applying normalization, standardization, data enrichment and advanced identity resolution as data enters the system. This prevents bad data from entering critical downstream systems and creates a strong data foundation that enables marketers to make more confident decisions and successfully execute campaigns.

Another key step to ensure the utility of a unified profile is to make it available and accessible across the enterprise – ensuring that all users have an identical understanding of a customer. For dynamic segmentation and real-time decisioning, a common understanding results in a consistent CX across every channel – as if the brand is speaking to the customer with one voice, regardless of the interaction touchpoint.

2. What are the key challenges organizations face in unifying customer data, and how can they overcome them?

Most companies have deep organizational silos that are difficult to overcome. They’re set up operationally with the mindset that each department needs data for its own purposes. Each department has a different idea for what constitutes business-ready data, leading to a lack of standardization. As a result, marketing teams and business users never develop a complete understanding of their customers – and can’t pull off an omnichannel CX. 

From a technology standpoint, the challenge in unifying customer data is that most customer data technology fails to prioritize data readiness, which includes making sure that data is complete, accurate and timely as soon as data enters the system. A basic match of customer data whenever there is a changing key, matching that is not tuned to the desired use case (overmatch, undermatch), or even failing to correct data inconsistencies are common when customer data technology approaches data quality as anything but a core capability. 

Technology that instead prioritizes data readiness in the building of a unified profile solves for the downstream problems associated with a lack of a single customer view, and it also is instrumental in helping organizations change their mindset for how customer data is used across the enterprise.

3. How can businesses balance the need for immediate insights with the challenges of data integration and system performance? 

Data integration and system performance challenges can often make it difficult to generate immediate insights needed to provide a great CX.  This problem gets at the heart of why data readiness is so important, and why so much customer data technology falls short of extracting value from customer data when it relies on third-party solutions for preparing data for business or CX use.

Because customer data integration has a direct bearing on delivering a real-time CX, data must not only be ingested in real time, but the various sources of customer data integrations mush also be updated in real time. If various business users accept different requirements for when data must be made ready for business use, then the result – just like having different standards for data quality – will be a poor CX due to a lack of a real-time customer understanding. Data readiness assumes that immediate insights are an indispensable part of a relevant, omnichannel CX.

4. What considerations should businesses keep in mind when adopting a composable approach to their data infrastructure? 

One consideration when assembling a composable martech stack is to understand if and when data quality processes occur. Many composable CDP vendors leave data quality to someone else, thus avoiding the consequences of having different components treating data quality with different approaches. But when a composable framework includes central ownership for making data ready for business use, marketers can trust the data they’re using to build segments and execute personalized campaigns – all without having to wonder if IT is returning the latest customer record.

Because a composability framework gives marketers direct access to a unified customer profile, they can independently build audience segments and launch personalized campaigns without having to rely on IT support, allowing them to more easily focus on strategy, executing and improving CX.

Ultimately, a composable infrastructure should ultimately make it easier – not harder – to power a differentiated CX, and that is only possible when data quality is a priority.

5. What metrics should organizations track to measure the success of their CDP implementations? 

Retention, loyalty and customer lifetime value (CLV) are three key metrics for measuring the success of a CDP implementation. A robust, enterprise-grade CDP should produce significant improvements in all three, the result of transforming raw customer data into actionable insights through having a deep customer understanding. In a McKinsey survey on CX, 76% of consumers said that receiving personalized communications is a key factor in prompting consideration of a brand, and 78% said that such personalization makes them more likely to repurchase. 

Higher retention, a more loyal customer base, and an increase in CLV are the direct result of implementing a CDP that gets data right – ensuring it is complete, accurate and timely – and makes it actionable for any business or CX use case. That means a unified profile is accessible for segmentation and real-time decisioning, that it is tunable depending on the desired use case, and that it is privacy compliant.

6. Looking ahead, what emerging trends do you believe will shape the future of customer data management and personalization?

Agentic AI will play an enormous role in the evolution of customer data management and CX. There is an expectation that brands will rely on agentic AI to manage and execute an end-to-end customer journey, essentially taking the familiar chatbot experience to another level with agents representing a virtual concierge for an individual customer. 

An expectation for personalization will harden into an expectation for agents to be responsible for a consistently relevant CX across channels. Because a chatbot can now easily handle questions about a company’s return policy, for example, customers will soon expect agentic AI to be able to execute a specific return – print a label, schedule a pick-up, apply a balance, etc. – and help guide the customer journey – show similar items in stock that match a customer’s stated preferences, find complementary items, show updated loyalty points, etc. 

 Successfully deputizing AI agents into customer data management and personalization will require agents to have access to the unified customer profile. As consumers become more comfortable with agentic AI as a CX tool, we may even begin to see a time when consumers create their own personal agents for different brands – with more trusted brands receiving more detailed data and preferences from the customers’ agents. Agentic AI may become a two-way street in other words. Brands that are open with how they use agentic AI to improve CX may be rewarded by customers providing more data through their own agents, which will then further improve CX. The key component in successfully integrating agentic AI into CX is high-quality data. Organizations that prioritize data readiness will discover that an accurate, real time understanding of a customer is the backbone to power any emerging CX use case.

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