You founded and sold your own company earlier in your career. How does that entrepreneurial experience inform your tech marketing approach as Redpoint’s CMO?

I would say my experience as a small business owner taught me how to be resourceful and mindful of maximizing what I have, especially from a budgetary standpoint. Every single investment needs to be intentional, so they lead to real outcomes.

When it comes to my role as CMO of Redpoint and how I approach tech marketing, I take the same approach regarding resources, but I am very selective with which tools I invest in – and that each initiative drives clarity, efficiency, and measurable impact. Even though Redpoint isn’t a start-up, I still treat resources with discernment to help the company excel as much as possible.

Now that CMOs own true data-driven marketing, what does “poor data quality” really mean and why is data readiness more than just a cleanup project?

Conceptually, every operational marketer understands what poor data quality means. Duplicate, incomplete, inconsistent or out-of-date records, and excessive inaccuracies all contribute to a poor customer experience (CX), uneven AI, wasted spend and added time and resources trying to make things right. These negative consequences are the result of marketers not having a highly accurate, up-to-date, and contextual understanding of a customer or household – at the exact time such an understanding is needed. Whether on the website, the call center, in-store, the mobile app, an ESP, CRM, or another application, providing the pitch-perfect experience depends on being able to not only confidently differentiate one customer from another, but also to know what the customer is all about – likes, dislikes, preferences, and behaviors.

Data readiness is sometimes misunderstood as a synonym for data quality, but if data readiness was just a cleanup project, marketers and users of customer data across the enterprise would still lack a unified customer understanding across systems. Data might be nominally better, but only for specific, narrow purposes. Yes, data quality is a vital component of data readiness, but on a broader level data readiness provides the context that supports specific use cases and is integral for executing the overall customer strategy.

What are the risks of AI-powered personalization failing, or worse, backfiring if marketers overlook a strong foundation in data readiness?

From airline chatbots making promises they can’t keep, to incorrect treatment recommendations, and SUVs selling for $1, there are many recent high-profile examples of AI going rogue. The problems typically result from AI training on incomplete or inaccurate datasets, which leads to a lack of real customer context – and customers notice.

In a Dynata survey on AI sentiment sponsored by Redpoint, 73% of consumers believe that AI can have a positive impact on CX, but only if the experience is seamless, consistent, and convenient. And if a customer receives a disjointed experience, 76% say it will make them less likely to trust or continue to engage with a brand.

Again, it’s not just about data quality. It’s about having a real-time contextual understanding powered by a dynamic and up-to-date unified profile. Imagine an AI model missing a recent transaction. Or unsure which household member it’s interacting with. It’s being asked to make smart decisions without full context. Data readiness enables a deep, personal understanding that clears the runway for exceptional AI-powered experiences – from analysis and predictions to content generation and customer service.

Vendor lock-in concerns many enterprises. How does Redpoint’s integration flexibility feature in your competitive positioning?

The reason vendor lock-in is such a concern in the realm of customer experience is that when important data lives outside of the chosen ecosystem, it’s virtually impossible to create a complete and accurate unified profile. Marketers chase their tail trying to create a unified customer profile across all systems, spending so much time and effort trying to make data actionable that by the time data is “good enough” the customer has moved on – the customer journey is now in a different place.

Integration flexibility is a key component of Redpoint software. A wide and growing list of supported connections enables seamless integrations across the tech stack, providing for a future-proofed architecture as a company’s business evolves and use cases expand or change. This flexibility is particularly important with new AI applications cropping up regularly. A company must be able to quickly pivot to meet the changing needs of its customers.

How do you approach martech investments to ensure every tool enhances overall customer knowledge without disrupting workflows, duplicating efforts, or complicating reports?

Because a differentiated customer experience depends on knowing everything there is to know about a customer or household, brands need to prioritize tools that plug into a broader ecosystem that enhances a unified view of the customer. Tools need to contribute to a single source of truth – not create another silo. This need for a holistic view is why many brands are implementing a composable architecture, which can help ensure that every system and application builds toward the common goal. Composability is most effective when a central platform owns the single source of truth for the customer, applying the same data readiness standard across the ecosystem.

A central premise of data readiness is that it makes life simpler for operational marketers and business users of customer data. If you have to write ingestion code for every new data source, or have to continually apply standardization protocols at various stages of the data lifecycle, you’re already losing. The customer has moved on. Data readiness standardizes, normalizes and matches data at ingestion. The fact that it is an ongoing, continuous process is what gives the unified profile context; new data adds to the full understanding, with persistent key management providing the longitudinal customer view that is vital for the delivery of a consistent CX.

When it comes to justifying spend with hard ROI data, what advice would you give to CMOs struggling to prove marketing’s strategic value beyond just numbers?

It’s difficult to prove marketing’s strategic value when processes are organized around teams and channels. The problem with using channel-specific performance metrics – and why they fail to accurately measure the ROI of CX investments – is that the type of personalization that drives revenue these days transcends channels. Customers expect a brand to know them as an individual – and to receive a consistent experience in the precise context of a customer journey – across channels. As an example, a brand may personalize display ads, and then measure an increase in open rates as proof that personalization in that one channel was successful. But this fails to account for how personalization influences behaviors and actions throughout an omnichannel journey. A customer may click on a personalized ad, but what do they do next – visit the website, contact the call center, engage with AI?

Data readiness powers a broader view by building a real-time, persistently updated unified profile that is as dynamic as a brand’s customers. It accurately reflects how customers interact with a brand, allowing brands to become truly customer-centric in how they engage with a customer. Consistent personalization yields higher loyalty and CLV, which better prove marketing’s strategic value than traditional channel-centric metrics.

How would an ideal customer data platform (CDP) look like five years down the line? What will be the USP most brands compete on?

Let’s be honest – the term “CDP” has been stretched, diluted, and downright hijacked by marketing claims. These days, if a platform touches customer data, it is suddenly calling itself a CDP. But five years from now – when the noise settles – the market will refocus on what a true CDP is meant to be: software that builds a persistent, unified customer profile accessible to other systems.

The key shift will be a renewed emphasis on the data part of the customer data platform. Why? Because AI-driven CX will demand it. Brands will realize that simply collecting data isn’t enough – what matters is having a deep contextual understanding of each customer in real time. That’s where many so-called CDPs fall short. Few address data readiness in a meaningful way.

At Redpoint, we believe the real competitive edge – now and in the future – is data readiness. Data readiness needs to be a core function of a CDP to ensure that customer data is always accurate, current and fit for purpose. Whether it’s powering AI models, personalizing content, or driving real-time decisions, that level of contextual intelligence will be the USP brands compete on.