Crafting the Future: CRM/CDP Strategies for 2024

Crafting the Future: CRM/CDP Strategies for 2024
Photo by Damian McCoig / Unsplash


Integrating Customer Relationship Management (CRM) and Customer Data Platforms (CDP) represents a pivotal evolution in how businesses approach customer engagement and data management. As we move into 2024, it becomes important to revisit and build upon the core concepts of CRM/CDP integration, setting the stage for an era where these technologies coexist and synergize to unlock unprecedented customer insights and personalization capabilities. The fusion of CRM and CDP technologies allows businesses to manage customer relationships and interactions across various channels while providing a unified, comprehensive view of customer data. This converged approach is instrumental in understanding past behaviors, predicting future actions, and tailoring engagement strategies to meet the ever-evolving customer expectations.

Throughout the article, CRM is mentioned. It will be used as a universal component in marketing across industries and to consolidate the terms below loosely and interchangeably when discussing CDP/CRM integration. All industries use some CRM-type solution as a container for profile data, whether as a source or a destination. Here are the typical traditional uses and meanings.

  • CRM - typically a Salesforce or Pipedrive-type solution. Often typical for Marketing to Sales stage approach - generally B2B or longer bigger ticket sales lifecycles
  • Customer DB Customer DB - Yester year, this was direct mail dB marketing; today, these are typically Snowflake, dBs on AWS, et. al... The owners are a CRM Manager or VP of CRM and managing marketing for D2C or retailer-based industries. Their titles are not indicative of the technology but the functional aspect.
  • MA - Marketing automation - older tools: Responsys, Adobe Campaign, Marketo - Newer tools: Braze, Adobe Journey Optimizer

The CDP facilitates the data and is a hub to keep them all in SYNC. It also provides other valuable tools like ID resolution and coordinated audience management that can be syndicated to any of these solutions or platforms.

The progression from traditional CRM to integrated CRM/CDP systems grew from adopting advanced analytics, AI/ML and combining a set of lifecycle process frameworks. This convergence began over 20 years ago - we started doing this at Omniture in 2005. SaaS CRM kicked off and filled a gap that dB marketing couldn't, and as internet marketing technologies matured, the modern marketing ecosystem stacks emerged. There are 10,000 plus solutions now for marketing, sales, and service. As these technologies advanced, they became more than just auxiliary tools but foundational elements that enhanced the predictive capabilities to streamline lifecycle workflows within CRM/CDP systems, enabling businesses to unlock deeper customer insights. As for the next evolution, it's clear that the synergy between CRM systems and CDP will become increasingly sophisticated, going beyond mere data collection and customer status stage changes to offer deeper strategic applications that drive personalized customer experiences at scale.

This shift concerns the three common business pillars: people, process, and technology, which will cause companies to adopt a combination of human-centered and customer-centric approaches that leverage data and content to deliver value at every touchpoint and integrate them into their traditional CRM/MA process the key will be to unlock the value of the data across the silos and the live events occurring at the touchpoints.

Anticipating Market Shifts

Everyone knows the digital environment is fast-paced, and emerging market trends in customer behavior patterns demand an adaptive approach to newer CRM/CDP strategies. The rise of human-centered marketing, driven by brand advocates and personalized customer experiences, underscores the need for businesses to align their CRM/CDP strategies with evolving customer expectations and market dynamics. This alignment requires a deep understanding of emerging trends, such as the increasing demand for privacy, the shift toward more ethical use of data, to interface more deeply with brand advocates and influencers who act as the new sales team, and the ability to translate insights into actionable strategies that resonate with target audiences.

As we look towards leveraging AI and Machine Learning in refining CRM/CDP strategies, the significance of anticipating market shifts becomes even more pronounced. Using advanced technologies to sift through data, help with increasing taxonomy changes, identify trends, and predict future customer behaviors is a game-changer. It represents a shift from traditional manual or rules-based CRM methods to more dynamic, real-time data pipelines where the CDP/CRM convergence can adapt to new competitive information and customer insights. This forward-looking approach is essential for businesses aiming to meet and exceed customer expectations in the coming years.

Leveraging AI and Machine Learning

AI and ML stand at the forefront of transforming CRM/CDP strategies from static source databases to dynamic, predictive engines capable of personalizing customer interactions at an unprecedented scale. They will enable businesses to anticipate customer needs and tailor their offerings more effectively by analyzing customer data, identifying patterns, and predicting future behaviors. Most vendors will start or have begun integrating AI and ML into their integrated CRM/CDP systems to enhance their capabilities, facilitating sophisticated segmentation, targeting, and personalization that improves the efficiency of marketing campaigns and the overall customer experience. This shift towards more intelligent systems marks a significant evolution in how businesses approach customer engagement, offering a pathway to increased loyalty and lifetime value. If these vendors lack AI, they should have APIs for the customer data team to BYOM(bring-your-own-model) capability.

The potential of AI and ML in CRM/CDP systems extends beyond mere personalization. These technologies also play a crucial role in automating repetitive tasks, updating lifecycle stages, validating taxonomy in real-time for better quality data, optimizing marketing efforts, and providing actionable insights that drive strategic decision-making. For instance, AI-powered chatbots can improve customer service by providing instant responses to inquiries and updating the CRM records through CDP integration at the same time, while predictive analytics can help businesses anticipate customer churn and take proactive steps to retain customers and send those insights up to the personalization engines. This level of automation and insight transforms CRM/CDP systems into invaluable assets for businesses, enabling them to operate more efficiently and effectively.

The move towards leveraging AI and ML in CRM/CDP strategies has challenges, including the need for robust data privacy and ethical considerations. Financial services and Pharma/Health Science have strict compliance rules and security gating to prevent black-box marketing established from the federal laws they must adhere to within their marketing initiatives. Therefore, as these AI technologies advance, the vendors must keep the algorithms open and transparent to help enable and support these industries as vertical sales channels.

As businesses harness the power of these technologies to gain deeper insights into customer behavior, the responsibility to use this information ethically and transparently becomes paramount. Let's examine the importance of data privacy and ethics in CRM/CDP convergence and highlight strategies for maintaining customer trust in an increased data collection and analysis era.

Data Privacy and Ethics in CRM/CDP

As customers become increasingly aware of and concerned about how their data is used, transparency and ethical data practices have become non-negotiable components of any effective CRM/CDP strategy. Governance should be the starting point of every data platform and company mandate. TOGAF and DAMA, with their DMBOK, i.e., Data Management Book of Knowledge, are but two enterprise and information architecture frameworks that exemplify this approach where "Data Governance" is the hub for all things data. Governance will drive good privacy and ethics stewardship. Think about companies like Factor Firm and their information processes to help you build these semantic governance frameworks.

Recently, as of 2023, with the advent of integrating LLM(Large Language Models) and AI, the intersection of data privacy and ethics in CRM/CDP systems adds another layer of complexity. As businesses deploy these technologies to enhance customer engagement and predictive capabilities, ensuring that AI models are unbiased and algorithms are transparent becomes critical.

This commitment to ethical AI involves regular audits of AI models for bias, adherence to ethical guidelines in data analysis, and clear communication with customers about data use. These practices safeguard customer trust and protect businesses from reputational damage and legal risks.

The emphasis on data privacy and ethics becoming habits in the company will set the stage for hyper-personalization at scale, a strategy that relies heavily on the ethical use of customer data to tailor experiences with precision.

Hyper-Personalization at Scale

Hyper-personalization represents the next frontier in customer engagement, leveraging the power of CRM/CDP systems, AI, and ML to deliver uniquely tailored experiences to each customer. Unlike traditional personalization tactics that might categorize customers into broad segments, hyper-personalization drills down to the individual level, analyzing specific behaviors, preferences, and interactions to craft personalized messages, offers, and experiences. This approach increases customer engagement and satisfaction and drives higher conversion rates and loyalty by making each customer feel uniquely valued and understood.

Achieving hyper-personalization at scale requires sophisticated technology, data, and strategy orchestration - the illustration below helps visualize what it might look like. CRM/CDP systems serve as the backbone, collecting and integrating customer data from various touchpoints, while AI and ML provide the analytical power to process this data in real-time, identifying patterns and predicting behaviors.

The current personalization engines use traditional ML models or static rules-based algorithms with primitive decision attributes, but other vendors have incorporated deep learning and reinforcement models, as noted below in the diagram, and these work as ensembles and consolidated on the fly based on the user's profile context at that moment in time. However, even newer models are being discussed in the research community due to the LLMs and new AI advancements, such as Direct Preference Optimization (DPO. These potential innovations will enhance CDP/CRM capability by dynamically tailoring content and recommendations based on real-time data and predictive analytics but lowering the latency even further while being more accurate.

Deep learning ML models and strategies empower hyper-personalization at scale for recommendations, offers, and content. Personalization engines like this always have embedded profile databases to reduce the latency. They store event interactions that trigger the content. These Profile databases store product, content, and user attributes. They can get fed updates from other channels via the CDP/CRM combination.

The transition to hyper-personalization at scale is not just a technological shift but a strategic one. All companies must look closely at these tools; the CDP/CRM integrations will be required to power these new solutions, potentially adding them as composable components at the edge. It requires businesses to rethink how they engage with customers, moving away from one-size-fits-all approaches to genuinely personalized experiences - Journey Orchestration (a new name for Marketing Automation) is part of this, so perhaps new capabilities will get released where the journeys are predicted and sent to these hyper-engines. If composable, CDPs integrated with CRMs(remember our consolidated terms) can orchestrate dynamic sequences and build content UIs on the fly for each user. This approach is proposed by FLOWRL to do just this with reinforcement, but what would it look like with DPO per above? It will become a critical competitive advantage for businesses in 2024 and beyond to seamlessly tie all these tools together.

Real-Time Analytics and Decision-Making

Real-time analytics and decision-making capabilities transform how businesses use CRM/CDP systems to engage with customers. The ability to analyze customer data in real-time, drawing actionable insights that inform immediate strategic decisions, is revolutionizing customer engagement. This capability enables businesses to respond instantaneously to customer actions and market trends, from personalizing offers on the fly to adjusting marketing campaigns in response to emerging customer behaviors. Integrating real-time analytics into CRM/CDP systems enhances customer experiences and empowers businesses to be more agile and responsive in their strategies.

For example, if a customer abandons a shopping cart, a company can immediately trigger a personalized email or offer to encourage completion of the purchase. This level of real-time engagement is critical in today's fast-paced digital marketplace, where customer expectations for immediate and personalized interactions are higher than ever.

CRMs often are more connected with dynamic analytics than CDPs but CDPs have a critical role in updating these tools from other channels. The role of real-time analytics in driving informed decision-making and personalized customer interactions will only grow. NLP(Natural Language Processing - aka. text-based Chat) will allow all companies to leverage these insights more quickly to orchestrate customer journeys in real-time. It is not just about enhancing the customer experience; it's about transforming CRM/CDP systems into proactive tools that drive engagement, loyalty, and revenue. I allude to this type of premise in my AI Control Plane article.

Let's look at customer journey orchestration. Integrating real-time analytics will play a pivotal role in enabling businesses to navigate complex customer pathways and deliver personalized experiences at every touchpoint.

Customer Journey Orchestration

The orchestration of customer journeys represents a strategic evolution in how businesses approach CRM/CDP integration, moving beyond static interactions to dynamic, personalized engagements that guide customers through their unique paths to purchase. Advanced CRM/CDP systems, bolstered by real-time analytics and AI-driven insights, enable businesses to map out complex customer journeys, identifying key touchpoints and opportunities for personalized engagement. This level of sophistication allows businesses to understand how customers interact with their brand, anticipate needs, and tailor experiences that resonate deeply with each individual.

Strategic customer flow and journey orchestration involve leveraging data analytics to comprehensively understand customer behaviors, preferences, and decision-making processes. This insight enables businesses to design and implement targeted interventions at critical touchpoints, enhancing the customer experience and driving engagement. For example, by recognizing when a customer is most likely to make a purchase decision, a business can deliver personalized recommendations or offers that are timely and relevant per our hyper-personalization capability. These steps and stages are becoming inter-connected and more automated so the marketer can speed up the productivity and velocity of content delivery. This proactive approach to customer engagement ensures that companies are not just responding to customer actions but are actively guiding the customer journey toward desired outcomes.

These updates on both sides of the external and internal sides of the fence inform the analytics and the journey flows. What becomes even more relevant is the change in how sales are realized and content is deployed beyond the triggered actions via 3rd-party brand advocates. These new groups are the new sales teams driving leads and customer stage updates. The ability to orchestrate these experiences, tailoring them to each customer's unique needs and preferences, including the brand influencers, is the essence of human-centered revenue growth—a strategy that drives sales and fosters customer loyalty and more brand advocacy.

CRM/CDP as a Human-Centered Revenue Driver

CRM and CDP system integration with AI can transform businesses into revenue-generating powerhouses focusing on human-centered marketing, sales, and engagement. By prioritizing the customer experience and leveraging data to inform personalized engagement strategies, companies can create a virtuous cycle of customer satisfaction, loyalty, and advocacy that directly contributes to revenue growth. This approach goes beyond traditional sales tactics and stages to recognizing that the actual value of CRM/CDP integration lies in its ability to foster deep, meaningful connections with customers.

By understanding and anticipating customer needs, businesses can design engagement strategies that meet and exceed expectations, turning every customer interaction into an opportunity to reinforce value and deepen the relationship. This focus on delivering exceptional experiences at every touchpoint sets apart businesses that thrive from those that merely survive in the competitive digital marketplace.

Benefits of Integrated CRM/CDP

An integrated CRM/CDP solution provides several critical benefits for companies looking to enhance their customer engagement and experience.

  • 360 Customer View: This ability is a tablestake. A comprehensive customer profile emerges by merging CRM transactional insights with the CDP's behavioral data from various platforms. This holistic view fosters personalized interactions and seamless engagement across multiple touchpoints.
  • Personalized Engagement: A CRM/CDP linkage empowers marketers to harness detailed audience insights for crafting bespoke campaigns and messages via diverse channels, enhancing the relevance and impact of marketing efforts.
  • Unified Data: The fusion of CRM's detailed customer interactions with the CDP's broad digital behavior insights results in a consolidated data panorama. This synergy drives smarter marketing strategies and diminishes isolated data pools. It ultimately drives omnichannel marketing transparency.

These benefits prepare companies for new combined human-centric lifecycle marketing approaches. These include brand advocates and influencers, which trend within most industries and drive bigger sales than the single lifecycle stages of traditional CRM/Marketing automation approaches.

This LIFECYCLE MARKETING APPROACH is a modification of SmartInsight's RACE method. Source:

Top CRM/CDP Integration Strategies

Integrating CDP and CRM can provide a powerful one-two punch for customer data management and engagement. There are a few key integration strategies to consider:

Strategy 1: CDP Layered with CRM

One approach is using the CDP as a customer data layer on the CRM. The CDP ingests data from all channels, stitches together unified customer profiles, and activates the data across channels. The CRM remains the system of record for customer interactions. This approach allows you to leverage the CRM's strengths, like sales automation, while empowering it with the CDP's comprehensive view of each customer.

Strategy 2: CRM as a System of Record

Continue maintaining the CRM as the central system-of-record for all customer data and storing unified profiles assembled by the CDP. The CDP ensures alignment between marketing and sales interactions from the other marketing automation system solutions and customer analytics. It focuses on collecting, unifying, and activating data from all touchpoints. At the same time, the APIs and integrations enable the bidirectional data flow between the CDP and CRM.

Strategy 3: Unified vs. Composable Stack

Some vendors now offer unified CRM/CDP solutions rather than integrating separate components within the platform, often called composable. While not typical, this combination combines strengths into a single, tightly integrated stack monolithic of sorts - which would typically benefit smaller enterprises or companies with light infrastructure and low-tech debt, and minimal data silos. Customer data flows freely between modules for a seamless profile-building, analytics, and engagement workflow. These systems can reduce complexity but may limit flexibility compared to best-of-breed composable CDP/CRM integration. They become more difficult to manage as you grow, but they are a viable solution for most companies today.

Here is a table to break down the capabilities of each and how they can complement each other.

Capability CRM Limitation CDP Data Capabilities
Centralized Data Management CRM alone can be restrictive due to siloed data, leading to incomplete customer profiles. CDP unifies data across systems, creating comprehensive customer profiles accessible in real-time.
Omnichannel Customer Identification Limited to recognizing customers who have self-identified within the CRM system. CDP enables creation of identity resolution, linking customer identities across devices and platforms.
Cross-Channel Data Analysis Insight generation is often restricted to the scope of data within the CRM. CDP provides a singular platform to analyze cross-channel data, offering a holistic view of customer behavior.
Real-Time Personalization Personalization efforts are often based on outdated data, hindering relevance. CDP allows real-time personalization by utilizing current behavior data to tailor customer journeys.
Behavior Prediction Predictive capabilities are constrained by the CRM data's limitations. CDP employs AI and machine learning on comprehensive data sets to forecast customer behavior accurately.
Data Integration and Accessibility Connecting CRM to other data sources is a manual and labor-intensive process. CDP automates data integration, making all customer data accessible across systems and departments instantly.
Anonymous Customer Tracking CRM systems can't track or engage with anonymous customers effectively. CDP can provide tools for tracking anonymous customer activities and connecting them to known customer profiles. CRMs/especially if Snowflake is used as a system-of-record can act as a clean room connected to the CDP for anonymizing profiles.
AI-Driven Insights CRM-based AI models are limited by the data they can access within the CRM environment. CDP leverages AI to process vast datasets, providing insights and predictions based on centralized customer data.
Journey Mapping Across Touchpoints CRMs often have limited visibility into the customer journey across all touchpoints. CDP or a Journey platform maps customer journeys across all touchpoints, offering a complete view of the customer lifecycle.
Marketing Automation CRM systems may offer basic marketing automation without personalization and timeliness. CDP automates marketing efforts precisely, ensuring timely and personalized customer interactions.

Implementation Best Practices

A successful CRM/CDP integration requires careful planning and execution. Here are some essential best practices to follow during implementation:

  • Secure executive buy-in. Gaining support from leadership is crucial for aligning teams and securing budgets and resources. Make a strong business case highlighting the value of an integrated CRM/CDP in achieving growth and engagement goals.
  • Prioritize change management. A CRM/CDP integration represents a significant change. Prepare stakeholders through training and communication. Have champions across teams encourage adoption. Empowering staff will smooth the transition and secure user adoption.
  • Integrate data sources. Connect siloed data within marketing, sales, and service applications. Build connectors and pipelines to feed data from touchpoints into the CDP and synchronize with the CRM. 
  • Train employees at all levels. Provide tailored training on new roles, responsibilities, processes, and systems. Hands-on support accelerates proficiency. Measure adoption metrics and refine training as needed. Ongoing learning secures maximum ROI.

The Future of CRM/CDP

The future of CRM and CDP looks bright, with innovations in AI, machine learning, predictive analytics, and omnichannel engagement paving the way for more personalized and seamless customer experiences.

AI and machine learning will enable CRM/CDP platforms to process data and generate insights faster. Rather than relying solely on rules-based models, machine learning algorithms will continuously improve their ability to predict customer needs and behavior patterns (Source). AI and ML will empower marketers to deliver highly tailored messaging and offers in real-time across channels.

Advanced analytics will move CRM/CDPs beyond reactive campaigns towards proactive, predictive interactions. By analyzing historical data and identifying macro trends, platforms can forecast customer churn risks, upsell opportunities, and future needs. Marketers can then take preemptive actions to nurture their most valuable customers.

Omnichannel functionality will become essential, providing a seamless experience for customers across devices and touchpoints. Rather than working in silos, emerging CRM/CDPs will have unified data and insights that intelligently adapt messaging to the customer's current channel and context. This contextual personalization will improve conversion rates and satisfaction.

Hopefully, this exploration of the best CRM/CDP best practices, frameworks, and strategies for 2024 makes it clear that the future of customer engagement lies in the seamless integration of technology, strategy, and a profound understanding of customer needs. The strategic imperatives for CRM/CDP success are no longer just about managing data, updating Sales Stages, or automating processes; they are about leveraging these tools to create experiences that are deeply personalized, immediately relevant, and profoundly human. The businesses that embrace this approach, investing in CRM/CDP integration and the advanced technologies that enhance these systems, will not only stay ahead of the curve but will redefine it, leading the way in customer engagement and driving unparalleled growth in the years to come.

By adopting the strategies outlined in this article, businesses can confidently navigate the complexities of the digital age, delivering personalized, engaging customer experiences that drive success in 2024 and beyond. The call to action is clear.

  • Invest in CRM/CDP integration,
  • Embrace the power of AI, ML, and real-time analytics and
  • Commit to a customer lifecycle strategy that places the customer at the heart of every decision.

The future of CDP/CRM customer engagement is not just digital; it's personal, predictive, and profoundly human.

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