In this interview, Erica Campbell, Global Consumer Data and Analytics Director at Electrolux discusses her transition from marketing to leading consumer data strategy. With a background in digital marketing at companies like L’Oréal, she now shapes data-driven decisions and strategic approaches, integrating efforts across multiple teams at Electrolux
Ahead of CDAO Nordics, we spoke with Erica Campbell about her journey from working in communications and marketing to her current role overseeing consumer data and analytics strategy.
In this exclusive interview, Erica explores key trends in consumer-centric data strategy, the role of advanced analytics in marketing, and the transformative impact of data on customer engagement. She discusses the ongoing convergence of digital, data, and AI, emphasizing the importance of a robust data foundation for enhancing customer relationships and driving strategic growth.
Join us at CDAO Nordics, to hear directly from Erica Campbell and other leading experts on the future of data-driven strategies in consumer engagement.
Could you give us an overview of your career journey and how it has shaped your approach to data and analytics?
My background is in communications and marketing, with over 15 years in media agencies and digital marketing roles at companies like L’Oréal and Electrolux. I’ve always been passionate about data—analyzing it, using it for strategic planning, and making data-driven decisions.
Four years ago, I moved into a new role, at that time still within the Marketing organization, leading the Global Consumer Data and Advanced Analytics team at Electrolux headquarters. This shift allowed me to lead shaping our consumer data strategy, collaborating with stakeholders from Marketing, D2C, and post-purchase teams to determine the strategic approach, technology, and overall vision.
Today, I’m part of the Data Experience Organization (DXO), where I co-lead the Marketing domain, together with the Data & Analytics Engineering Manager. Moving from a data user to a data provider—and now leading data product development—has helped me realize my true passion lies at the intersection of data and marketing. So, please keep that in mind as I discuss trends and other questions in this interview.
Which trend associated with data, analytics and AI are you most interested in or passionate about? Why do you think this is so relevant today?
The most impactful right now is the integration of generative AI into analytics, especially in two areas: predictive analytics to enhance marketing performance and hyper-personalization.
Generative AI enables predictive scenarios and market simulations, allowing marketers to refine strategies with far greater precision. It also empowers real-time, data-driven consumer engagement by using predictive models to anticipate behaviors and prescriptive analytics to suggest the next best action.
Although many companies already use these capabilities, they’re often limited in scope, primarily due to constraints in data integration, computational power, and model accuracy. I see the real trend for 2025 as the use of AI in analytics. Getting traction. With advancements in AI infrastructure, data accessibility, and modeling capabilities, we’re set to see broader adoption and more impactful applications at scale.
What are the top three trends you think will take AI, data, and analytics to the next level in 2025, and why?
Building on the AI applications I just mentioned, the first trend is using AI to enhance marketing performance. Generative AI can simulate countless scenarios in real time, considering budgets, channel mix, content, pricing, seasonality, competition, and more.
This will give companies a significant competitive advantage by maximizing the return on marketing investment. Plus, using simulation as a testing technique is a powerful alternative to live piloting. By using advanced generative AI models to simulate market reactions, companies can test strategies without risking real-world resources.
This allows for controlled, risk-free exploration of different budget allocations, content types, and consumer segments, offering actionable insights without the potential downsides of live testing, such as resource strain, misaligned targeting, or brand reputation impact.
The second trend is leveraging AI to enhance the consumer experience through personalized journeys. Think about the next best action not only in terms of the best next offer but also how to better serve the consumer throughout their lifetime. Think about how AI can increase consumer lifetime value. Generative AI and composable, AI-enabled analytics will redefine how we do marketing, combining real-time insights with deep consumer understanding.
The third major trend involves content automation with generative AI. This technology enables scalable, multilingual content production, automated support content, SEO-optimized materials, and real-time dynamic content for hyper-personalized engagement. This will save costs, reduce time-to-market, and help brands maintain a strong, relevant presence across channels.
What are the top three challenges that the industry will face in 2025, and why?
The biggest challenge is still, I believe, data quality. For companies to be truly data-driven, data accuracy, consistency, and accessibility are essential. Many organizations still need to improve their data foundations—integrating multiple sources, cleaning data, and managing it across platforms—often at significant time and cost. Another challenge is securing sufficient funding, especially since demonstrating the value of data products can be difficult and ROI is often hard to quantify.
The second challenge relates to data privacy and responsible AI, as regulations and consumer expectations around transparency and control continue to grow. The third challenge concerns people—addressing talent shortages, upskilling needs, change management, and adoption within organizations. Recruiting data scientists and analysts with specialized skills in consumer analytics and AI is increasingly competitive, making it challenging to build and retain a strong team.
In your upcoming talk at CDAO Nordics, you’ll be discussing ways to improve data strategy for customer-centric analytics. Could you give us an overview of some core elements that make a data strategy truly customer-focused?
The foundation of a customer-centric data strategy lies in achieving a single view of the customer (SVOC) - a comprehensive, unified understanding of each consumer’s journey. This integrated view across all channels enables seamless, personalized interactions, even when multiple teams manage different touchpoints. Although achieving SVOC may sound straightforward, in practice, it is often challenging due to data silos within organizations. However, when successfully implemented, SVOC empowers teams to deliver the relevant, timely experiences that today’s consumers expect.
Omnichannel personalization has become a baseline expectation, yet poor data quality or governance can quickly erode customer trust and lead to frustration. By focusing on a robust SVOC, organizations can overcome these obstacles and foster lasting relationships with their customers.
What are some of the main challenges companies face in shifting their data strategy to prioritize customer-centricity?
I just mentioned some. Fragmented data sources and siloed systems present the first challenge; often, customer data exists across disconnected systems that don’t communicate. Creating a unified view requires advanced technology, significant technical effort, and cross-departmental buy-in. Scaling personalization also demands robust infrastructure and AI expertise, which can be costly and complex to develop.
In addition, data quality and governance are critical. Inaccurate or outdated data can lead to incorrect insights and poor customer experiences, reducing trust in analytics and consumer trust. Finally, data privacy regulations and ethical considerations are critical. Customers expect transparency and control over their data, which means that organizations need to build a culture of responsible data use, embedding privacy and transparency by design.
For organizations just beginning this transition, what key steps would you recommend to make their data more impactful for customer analytics?
Start by talking with different teams that interact with customers to understand their goals and aspirations for customer data. What would they do if there were no limitations? What do they want to learn about consumers, and how do they aim to enhance the customer experience? Once these use cases are clear, work on the technical architecture to support them. Decide which tools and solutions are best suited to your organization’s legacy systems and priorities.
An equally important part is managing people. Creating omnichannel, personalized customer experiences require a strong change management approach, upskilling teams, and in some cases recruiting data scientists and analysts skilled in customer analytics and machine learning. Agile product teams can help streamline this transition but establishing them is sometimes a challenge in itself.
Given the Nordic region's focus on data privacy and consumer rights, how do you see these factors influencing data strategy decisions? Are there any unique opportunities or challenges here compared to other regions?
It is no secret that the Nordics are a step ahead in terms of data privacy and ethical use of AI, and living in Sweden for almost four years now has given me a firsthand view of this unique approach. While Brazil, my home country, also has strict privacy laws through the LGPD, the Nordic approach to privacy and ethics feels deeply embedded in the culture, much like the region’s commitment to sustainability—even when compared to other European countries.
This is also encouraged by educational initiatives, like Finland’s Elements of AI course, which has promoted ethical awareness among the public. Norway has a national AI strategy that emphasizes transparent AI, while AI Sweden encourages responsible AI across industries. Companies here often go beyond GDPR, offering users detailed control over their data and using consumer-friendly communication to reinforce trust.
This dedication to ethical data use presents a powerful opportunity: it builds customer trust while empowering employees to uphold high ethical standards. Plus, knowing that they are contributing to a culture of respect and responsibility around data, employees are inspired not just by compliance but by a sense of meaningful purpose.
You can find senior members of our expert community discussing these topics, as well as many others, at CDAO Nordics, click here to register now
