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This article was written by Dan Knauf, Chief Technology Officer at Material.
In an increasingly data-driven world, businesses are constantly looking for ways to leverage customer data to improve their products, services, and customer experiences. The Customer Data Platform (CDP) is one of the most powerful tools available, but there are various challenges that companies face during the implementation phase.
In this FAQ, we’ll explain what a CDP is, show how CDPs are used, and explore the best ways to maximize the value of CDP implementation through the use of predictive analytics and AI.
We’ll also highlight innovative strategies developed by Material to address challenges within the CDP space and our approach to helping clients maximize the power of data.
What is a Customer Data Platform (CDP)?
A CDP is a software solution that collects, stores, and unifies customer data from sources such as online interactions, in-store transactions, and mobile app usage. The platform then creates a single, comprehensive customer profile, which can be leveraged by marketing, sales, and customer service teams to create personalized experiences and drive customer loyalty. A CDP performs many jobs in a single solution. It offers a unified customer view, improved customer segmentation and targeting, enhanced personalization and customer experience, omnichannel marketing and data-driven decision making, as well as compliance and data governance.
What are the Challenges of CDPs?
By centralizing all customer data in one place, CDPs enable businesses to better understand their customers’ preferences, behaviors, and needs, resulting in more targeted marketing efforts and improved customer satisfaction. But the process isn’t always seamless. Some common pitfalls with CDP implementation include limited analytical capabilities that make it difficult to draw actionable insights, data quality issues that compromise the accuracy of customer profiles, and a lack of, or too limited, predictive modeling. In addition, sub-par personalization capabilities put brands at risk of losing more customers than they gain.
Customer Interaction Feedback Loop
How does Material Solve CDP Challenges?
We’ve designed an innovative strategy to address and overcome CPD challenges based on two complementary approaches.
Empower businesses by enabling them to bring their own data models to existing platforms with extendable Machine Learning (ML) capabilities.
This solution primarily focuses on leveraging data science and data integration teams. The strength of this approach lies in platforms that can extend their models, creating a kind of ‘venue’ for analytics to take place. This pivotal shift in focus both enables data scientists to do their best work and fosters a culture of experimentation.
The approach also emphasizes the continuous optimization and training of the models. For businesses that have a dedicated data science team, this is the perfect platform to deploy them. However, for those who don’t, we recommend partnering with experts who possess a comprehensive understanding of data science and the platform.
Augment your CDP with an adjacent solution.
This entails establishing an Adjacent Analytics Environment – essentially a separate tool or environment that can operate concurrently with your CDP. This fresh setup acts as another ‘venue’ where data is processed and analytics models are run, providing an arena to apply case-specific ML models.
The Adjacent Analytics Environment can be provided with data from various sources, including the CDP itself, and can then enrich customer profiles and improve real-time decision-making processes by feeding attributes back into the CDP. This approach is also very common in ‘composable’ solutions, which refers to an ecosystem of independent tools which work together to serve a business outcome.
There are significant benefits to setting up an adjacent solution: it’s quicker, allows companies to be agile, and promotes a more modular pattern of data and analytics in the enterprise environment. As teams work on generating productized datasets for improved performance, other data sources, products, and enterprise functions can also be extended to the analytics environment. With the use of modern data mesh patterns, these productized data sets can be shared across the organization, thereby creating new opportunities or extending existing value.
What is the role of AI in CDP Solutions?
Taking your CDP to the next level with AI entails a series of key steps designed to optimize your data handling and decision-making processes. Once you’ve established a venue for your data, set up a suite of predictive ML models to enhance personalization performance, and cultivated a culture of experimentation and optimization, you’ll naturally want to explore new methods to enhance your performance even further.
This is where AI enters the picture, offering a transformative approach to improving the value extracted from your data.
Whether through more efficient automation of data cleansing, merging, or Extract, Transform, Load (ETL) operations, AI is a valuable tool with capabilities that enhance the input quality for your analytics processes. The benefits also extend beyond the data cleansing phase—AI is a potent tool when it comes to generating additional insights from the data.
As AI engines continue to evolve, particularly those focused on generative AI, brands need to rely more on a culture of experimentation to ascertain which models truly add value. AI models are trained using different datasets and algorithms; thus, your results may vary from model to model.
Consider composable solutions, which can swap technology functions within the stack. Given the rapid evolution of AI, companies will likely want to experiment with different AI-driven enablers and replace current ones with superior solutions as they become available. Therefore, the focus should be on fostering a marketing culture of experimentation, using data to measure results, and constantly seeking ways to enhance performance and value.
Why is it important to remember the adage “Garbage in, Garbage out”?
This might not be a frequently asked question, but it should be. Despite having a robust feedback loop from browsing digital channels, CDPs require quality inputs to function optimally.
The solution lies in the incorporation of bespoke, tailored data models. These are designed to refine and make better use of the information at our disposal. The tailored data models operate in an adjacent environment, a space where they can process data – sourced from first-party databases, loyalty programs, and other resources – and then generate updated profile attributes. These refreshed attributes subsequently feed back into the CDP, enhancing its functionality and accuracy.
However, this is not just a matter of plugging in these models and expecting everything to work seamlessly. This method acts as a bolt-on and needs to be driven by specific use cases.
When implemented and executed properly, these data models can take into account every step of the customer journey, which is instrumental in predicting real-time opportunities to alter consumer behavior. This could mean presenting the right offer at the right time, recommending suitable content, or discerning the most likely ‘next best action’ that the customer might take. By such means, bespoke data models can significantly enhance the utility and performance of a CDP.
While modern CDPs offer significant advantages for businesses, there are certain limitations that can hinder their potential. By augmenting their CDP with advanced data analytics and predictive modeling capabilities, businesses can overcome these shortcomings and derive even more value from their customer data. By investing in data quality management, employee training, and fostering a data-driven experimentation culture, businesses can ensure they are maximizing the potential
of their CDP.