Navigating Data Categories in Customer Data Platform for effective CRM!

Why did the CRM need a therapist? Because it couldn't handle all the emotional baggage from the preference data - it was constantly asking, "Why do customers unsubscribe? Do they not like me anymore?"

Data Drives Everything

Data is the backbone of our modern existence, permeating both personal and professional spheres. Whether it's choosing groceries or deciding on entertainment options, data underpins every choice we make. However, its significance is most pronounced in business, particularly for marketers who rely on it to inform every interaction with customers. Through data, marketers can foster loyalty, nurture relationships, discern preferences, and strategize for the future. Ultimately, leveraging data enables enhanced communication and the cultivation of genuine connections with customers, echoing the essence of personal relationships. This approach is crucial for successful marketers, as it demonstrates an understanding and genuine concern for their customers' needs and desires.

Types of Data

Data is not uniform; its value and insights vary significantly. Within the realm of data, two primary categories emerge: Identifiable and Non-Identifiable data. Each type offers distinct revelations and serves unique purposes.

Identifiable Data

It refers to information that is explicitly linked to an individual or a family account. This could include identifiers such as a driver's license, email address, or a phone number, which are utilized during onboarding the customers in a platform.

Anonymous Data

Anonymous Data presents a more nuanced scenario. It lacks direct association with a known individual, introducing an element of inference. For instance, if a user explores android phones on a website prior to logging in, the business may deduce their potential interest in ordering a phone.

Identifiable and Anonymous Data can also be categorized in four different categories: Foundational Data, Interaction Data, Behavioral Data, Preferential Data

Foundational Data

This encompasses fundamental details, serving as the building blocks of subscriber information. This may include personal identifiers like the full name of a customer. Such data can either be distinct to an individual, such as their zip code, or shared among multiple individuals, like a office address.

What is Identifiable Foundational Data? It refers to precise details retained within the Customer Data Platform, such as phone numbers and email addresses, which can be leveraged to exactly identify the data.

Then what is Anonymous Foundational Data? It would be the IP address associated with the browsing interaction when a customer logs into their account. We cannot directly identify a customer with the IP address (as it will change) but we can predict the location biasness from the same.

Interaction Data

Interaction data encompasses the activities individuals engage in and their engagements with websites, applications or even with customer support executives. For instance, clicking on a link or opening an email constitutes interaction data.

What is Identifiable Interaction Data? Identifiable interaction data stems from the actions individuals undertake within a known data context, such as their email inbox or phone number.

What about Anonymous Interaction Data? It arises in situations where it's challenging to discern the actor behind the action, such as clicking on a webpage link by a visitor who hasn't logged in.

Behavioral Data

Behavioral data lays the groundwork for understanding customers more deeply, enabling marketers to attribute characteristics to them gradually. This process unfolds over time as marketers accumulate an increasing amount of foundational and interaction data. By leveraging pertinent behavioral data, marketers can discern optimal communication strategies and pinpoint customers' areas of interest.

An Identifiable behavioral data can be a customer's repeated purchases of organic and sustainable products from an online grocery store.

Whereas an overall trends in website browsing patterns, such as the most visited pages or common navigation paths, without tracking specific users can be categorized as Anonymous behavioral data.

Preferential Data

Preference data delves into the core of customer preferences, revealing their sentiments and beliefs regarding various aspects, such as their communication frequency preferences. The most effective methods to collect attitudinal behaviors involve direct inquiries from customers or procuring the information from external sources. Next-gen CDP platforms started using artificial intelligence to infer preferences based on the customers' Foundational, Behavioral and Interaction Data.

Example of Identifiable Preferential Data can be anything related to a customer's preference for example preferred methods of contact or choice of brands.

For Anonymous Preferential Data, the example can be general patterns in customer feedback collected through surveys or reviews, such as most preferred brands without linking them to specific customer profiles.

What to Do with All That Data

In the era of big data, the challenge isn't just collecting information, but also making the most of it. Here's what we can do with all that data:

Data Storage and Organization: Establish robust systems to store and organize data effectively. This involves choosing appropriate databases, cloud storage solutions, or data warehouses to ensure accessibility and reliability.

Data Analysis: Utilize data analysis tools and techniques to derive actionable insights. Whether it's through descriptive, diagnostic, predictive, or prescriptive analytics, mining data for patterns, trends, and correlations can inform decision-making and strategy.

Personalization and Targeting: Leverage data to personalize customer experiences and target specific audience segments. By understanding customer preferences, behaviors, and demographics, businesses can tailor products, services, and marketing campaigns to meet individual needs more effectively.

Optimization and Efficiency: Identify areas for improvement and optimization across various business processes. From supply chain management to marketing campaigns, data-driven insights can streamline operations, reduce costs, and enhance overall efficiency.

Innovation and Product Development: Use data to fuel innovation and drive product development. Analyzing market trends, customer feedback, and competitive insights can inspire new ideas and guide the creation of products and services that resonate with consumers.

Risk Management and Compliance: Mitigate risks and ensure compliance by monitoring and analyzing relevant data. Whether it's identifying potential security threats, detecting fraud, or adhering to regulatory requirements, data analysis plays a crucial role in safeguarding business interests.

Continuous Learning and Adaptation: Embrace a culture of continuous learning and adaptation based on data-driven insights. By regularly evaluating performance metrics and monitoring key indicators, businesses can adapt strategies and tactics to evolving market dynamics and consumer preferences.

While you recognize its immense value, without a robust system for storage, organization, and analysis, its potential remains untapped. In the upcoming newsletter, we'll delve into strategies to effectively integrate our data and leverage its power to drive results.

Please share your feedback on your thoughts of different kind of data categorization you faced while implementing a CDP for effective CRM.