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In today’s digital world, customer data isn’t just getting bigger—it’s becoming more complex and layered. Brands are capturing information that goes beyond basic interactions; each data point can hold a treasure trove of insights. Take a purchase, for instance: it’s not only about the items in a customer’s cart; each item comes with its own unique attributes that tell a richer story about the customer’s preferences and behavior.
To tap into this valuable, nested data, brands are turning to advanced data structures that make it easier to store and utilize complex information across various platforms, such as Customer Data Platforms (CDPs) and data warehouses. By leveraging these structures, brands can achieve better organization and visibility of interconnected data, allowing them to activate this information for a range of purposes.
One of the most effective data structures brands are using is Object Data
Before we explore how brands are using this powerful data type, let’s take a moment to understand what Object data is and how it works through a relatable example.
Object Data is a flexible data type that organizes information as key-value pairs. This means you can create a set of attributes that describe another attribute. In simpler terms, when you define a custom attribute as an object, you can include additional details that provide a deeper understanding of that object.
Let’s say we have a pet products brand that offers a wide variety of pet pedigree options. This brand would maintain customer profiles that include standard details like name, demographics, and contact information. But they would also track information about the pets each customer owns, which could vary widely. Each pet can have its own unique set of attributes, such as species, age, breed, and favorite treats.
This pet-related information can be neatly organized under a single attribute called ‘Pets,’ which is categorized as an Object type. Inside this ‘Pets’ object, you can store multiple attributes—‘species,’ ‘age,’ ‘breed,’ and ‘treats’—all in one cohesive structure, as shown in the image below.
Brands today are increasingly using Object data to store and work with complex information across their systems. They rely on this data to feed into their CEP platforms, like MoEngage, to create meaningful customer segments and deliver personalized experiences. But when the platform doesn’t support Object data, it can lead to significant challenges.
With the growing expectation that CEP tools should support Object data, the lack of this capability creates several obstacles for brands:
A common challenge is the manual transformation of Object data into simpler, primitive formats. Brands are often forced to break down complex, interrelated attributes into separate, flat ones to pass this data into their CEP platforms. For example, rather than storing an object like “item,” which may have attributes such as “color,” “size,” and “brand” nested within it, brands have to transform these into standalone attributes like “item-color,” “item-size,” and “item-brand.”
This process not only requires additional time and effort but also leads to a bloated data structure with redundant attributes, complicating data management and making it more difficult to maintain consistency across the tech stack.
This disjointed approach affects the accuracy and depth of insights, as brands can’t easily access a unified view of how these attributes relate to one another. Without this consolidated view, understanding customer behavior becomes a fragmented process, making it difficult to drive meaningful engagement strategies.
MoEngage enables seamless ingestion, storage, and activation of Object data, allowing brands to bypass the challenges previously mentioned and leverage this rich data for impactful engagement.
What This Means for Brands:
E-commerce:
An e-commerce brand tracks items added to the cart, including their attributes, as ‘Object’ data. They can create a targeted audience consisting of customers who have added apparel items to their carts with a total value exceeding $200 and extend personalized discount offers to them to drive conversion
BFSI:
A bank captures customer profiles along with account details as ‘Object’ data. They may create a targeted audience of customers who have active loans over $50,000 and savings balances under $10,000, and offer them tagrted solutions for better loan management.
Support for Object Data is critical for brands eager to seamlessly ingest and activate their rich data. It allows for a smooth flow of information from various tools into MoEngage, enabling brands to gain a unified view of customer data that enhances understanding and insights. Moreover, the easy activation of this data empowers brands to engage their customers more effectively, helping them go above and beyond in delivering delightful experiences.
If you’re interested in getting started or want to learn how to leverage ‘Object’ data on the MoEngage platform, feel free to set up a quick demo.
Prateek, Senior Product Marketing Manager at MoEngage, leads the portfolio for Analytics, Segmentation, Data, and Data Science capabilities. Passionate about impactful product launches, he firmly believes that "One good product can change the world." He holds an MBA from Xavier Institute of Management, Bhubaneswar.
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