In the ever-evolving landscape of marketing operations, efficiency is the name of the game. With businesses collecting vast amounts of customer data from various touchpoints and sources, the need to streamline and leverage customer information to create more personalized experiences and drive business benefits has never been more critical. This is where a (Customer Data Platform) or a CDP takes center stage.
A CDP is a software system designed to gather, consolidate, and unify customer data from various origins, including online interactions, in-store purchases, and mobile app engagement. This consolidated data serves as a single, comprehensive customer profile, empowering marketing, sales, and customer service teams to craft tailored experiences. CDPs integrate with Marketing Technology (MarTech) tools, ensuring data is utilized consistently across marketing channels. They democratize data access, allowing you to leverage customer insights effectively.
Though CDPs can churn out customer information and enable businesses to gain a deeper understanding of their preferences, CDPs alone cannot tap into growing customer expectations. When it comes to the precision of marketing initiatives and elevating overall customer contentment, custom algorithms i.e. algorithms when integrated into CDPs, revolutionize the way data flows, are managed and utilized to achieve remarkable efficiency gains and cost optimization.
AI-Powered CDPs: Tailoring the Customer Experience
CDPs at their core, are designed to consolidate and manage customer data from various sources, such as websites, mobile apps, CRM systems, and social media platforms. This data is then organized, cleaned, and made accessible for marketing and customer service teams to create more personalized and effective campaigns. However, as the volume and complexity of customer data continues to grow, traditional CDPs have faced limitations in their ability to extract meaningful insights.
With third-party cookies only identifying customers with their browser addresses, marketers expect to see a holistic customer profile spanning across many platforms. This has amplified the need for algorithms. Where a CDP falls short of its promises, custom algorithms in CDPs allow marketers to curate opportunities. These personalization algorithms exploit ML and predictive modeling to enhance customer engagement and experience. They collect customer behavior data and use those data sets to offer user-specific hyper-personalized recommendations, in the form of targeted messages, promotions, and advertisements.
Let’s take a look at an example – if as a marketer, you wish to generate dynamic content based on a set of conditions or triggers, custom AI/ML will allow you to show products to your consumers relevant to their data such as location, user behavior, pages visited, etc. How does this happen? These algorithms rely on AI that creates a real-time propensity score for each user according to the chance each will follow a pre-set action i.e. predictive targeting. The machine learning algorithms analyze structured and unstructured data from all visitors to identify patterns and map similarities. This in turn, enables us to predict user behavior.
Another example is making recommendations based on the user group’s behaviors i.e. improved customer segmentation. As a use-case consider seeking financial services online. AI will propose product recommendations based on changing financial behaviors or market conditions that are selected as per your chosen parameters or customer preferences. The same will be the case in healthcare, AI will enhance patient engagement by personalizing content and suggesting treatment options based on your health data, thus enabling AI customer segmentation.
If you’re still wondering about how AI maps customer preferences, imagine AI playing a chess game. It can go straight for the win by predicting the best possible move because it has studied tens of thousands of individual chess matches. The same holds for a custom algorithm. It can analyze complicated sets of metrics, compare them to similar metrics from the past, and based on that comparison give an accurate recommendation. Their behavior can therefore be understood and predicted from data like:
The more data collected, the more accurate your customer predictions become, leading to better conversion rates.
A few well-known brands that employ custom algorithms include Netflix - a platform that has garnered more than 57 million users worldwide. It is nearly impossible to create a customized homepage for every visitor. Instead, it employs a complex set of algorithms that determine each individual’s preferences and highlights movies and TV shows just right for them.
Amazon, the globally used e-commerce website customizes its home page recommendations for each shopper in a way that what is recommended to you will not be recommended to someone else. It makes online shopping much easier despite offering limitless options.
In automobile manufacturing, the use of custom algorithms in Subaru's journey began with the adoption of a customer data platform (CDP) aimed at audience segmentation, targeted sales efforts, and personalized customer journeys to enhance the likelihood of conversions. Within a matter of weeks, the company successfully integrated the CDP and exceeded its initial objectives. Recognizing the untapped potential of their data, Subaru has since harnessed post-purchase customer data and predictive analytics to innovate new products and services, resulting in increased revenue generation and customer loyalty.
So why should organizations consider integrating AI-enabled custom algorithms with their CDPs? Here’s why:
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CDPs |
AI-enabled custom algorithms |
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Offer predefined set of features without the flexibility to integrate external components |
Collect, process, and analyze data to create comprehensive customer profiles |
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Can be costly custom development to connect with other systems |
Create a unified ecosystem by consolidating data from multiple sources and maximize the value of existing tech stack |
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Limit flexibility due to vendor lock-in |
Foster vendor independence, allowing businesses to choose the best tools for each aspect of their data management strategy |
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Riddled with high associated costs due to bundled features and limited customization options |
Employ a cost-effective approach by integrating only necessary components |
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Give basic insights into customer preferences |
Identify patterns, preferences, and behaviors, to segment audience into distinct groups |
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Offer limited insights to personalize experiences to individual tastes |
Provide highly targeted and data-driven CX |
Though the use of AI in CDPs offers significant benefits, it can be accompanied by a few challenges like - the explainability of “black-box” ML models, and the potential for biased decisions (unfair decisions that are amplifications of biases already present in the data ML learns from, or due to algorithmic inaccuracies), and few others drawbacks for the use of ML in automated decision making. However, once these concerns are addressed and mitigated with statistical techniques such as fairness audits and privacy-preserving AI, AI-powered CDPs bring several additional benefits:
Accurate customer profiles - One of the top AI/ML use cases for marketing is providing more visibility into customer data. With AI-powered identity resolution, duplicative data can be cleansed and consolidated into a single customer profile. This eliminates redundancies or inaccuracies and gives marketers greater visibility into the customer journey. Identity resolution also helps marketers link unknown customer data to known profiles, and identify audiences with similar affinities or attributes. This allows for greater personalization, segmentation, and improved data-driven CX for both known and unknown audiences.
Data-driven campaign optimization - AI segments customers by behavior, optimizing ad performance. Predictive analytics enhances segmentation by:
Orchestrating the customer journey - With an AI-powered Customer Data Platform (CDP), brands can personalize the entire customer experience across channels and touchpoints, going beyond optimizing ad campaigns. AI's key applications include predictive analytics for next-best-action recommendations. This AI model utilizes customer data and profiles to deliver tailored content and messaging to specific audiences, enhancing value and timing. Data-driven content aligns with customer journey orchestration, facilitated by the CDP's insights into customer behavior at different stages. Marketers can better plan and execute campaigns to guide customers through their journey. Quality recommendations based on search and purchase history provide buyers with highly relevant and personalized content, ultimately boosting conversions and ad campaign performance.
Increase efficiency through automation - According to a recent Hubspot survey, the average marketer spends around 16 hours a week on routine tasks – that’s about one-third of their work day. The types of routine tasks include tagging content and images, segmenting clients, and running manual campaigns. The same survey found that the process of creating and sending emails takes an average of 3.48 hours per week, while the process of collecting, organizing, and analyzing marketing data from disparate sources for about 3.55 hours per week. With a CDP equipped with AI tools, marketers can automate routine tasks and free up time to focus on more thoughtful, creative, and productive tasks.
Conclusion
AI is a game-changer/ indispensable tool for CDPs. By leveraging AI-powered CDPs, businesses can gain a competitive edge, make data-driven decisions, optimize their marketing efforts, and achieve several other business goals. With our comprehensive approach to custom CDP implementation, you can leverage precious data and activate superior insights in real-time. Don’t wait, get in touch with an expert customer data platform company today.