Transform Raw Data into Insights Faster with MoEngage Computed Traits

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Modern brands swim in a vast ocean of customer data—from real-time events and user attributes to transactional metadata. This data is the lifeblood of personalized experiences that boost engagement and revenue. However, simply using raw data isn’t enough anymore. Forward-thinking brands need to create new, meaningful attributes that are derived and computed from their existing datasets.

Imagine a retail marketer who wants to identify his high-value customers based on their spending patterns. He wishes to compute a new attribute — “Average Order Value” — to target them with focused loyalty and retention campaigns. This average order value would be computed based on all the orders placed in a time frame and their value for each user.

In a similar fashion, marketers in different industries would like to compute:

• Average Watch Time in OTT
• Average Booking Value in Travel & Hospitality
• Average Transaction Value in Banking, and more

The challenge? Getting these sophisticated attributes created has traditionally been a major bottleneck.

The Bottleneck: Why Traditional Workflows Fail

Building computed attributes requires complex transforming, aggregating, and analyzing data across multiple variables. Historically, marketers have faced two painful paths:

Manual Workflows: Exporting raw data, performing tedious computations in an external tool like Excel, and then re-importing the results. This is time-consuming, error-prone, and severely delays your ability to personalize.

Engineering Dependency: Relying on already over-extended data or engineering teams to query, compute, and ingest attributes into the Customer Engagement Platform (CEP). This creates bottlenecks, slows down execution, and limits the marketer’s ability to be self-sufficient.

Every new attribute and periodic update requires repeating these hassles. What if you could automate this and empower marketers to create and manage these attributes independently?

Introducing MoEngage Computed Traits

Computed Traits enable brands to effortlessly create powerful new attributes from existing customer behavior, properties, and transactional data right within the MoEngage platform.

You can compute these traits directly using built-in aggregation functions or by running SQL queries to create highly tailored, dynamic attributes. This completely eliminates the need for manual data exports, external tools, and reliance on engineering teams.

What Computed Traits Deliver

Effortlessly Create Meaningful New Attributes

Use built-in functions or advanced SQL to instantly generate and activate sophisticated attributes. Eliminate tedious manual work and engineering bottlenecks to fuel smarter decisions faster.

Build Richer, More Complete Customer Profiles

Enhance user profiles with intelligent data points that go beyond basic attributes. This provides the holistic context needed for informed decision-making and more impactful personalization.

Create Better, More Granular Audiences

Segment your audience with unprecedented precision. Whether you’re targeting high-value customers or users at risk of churn based on a computed score, these derived attributes power more precise targeting strategies.

Deliver More Personalized and Meaningful Experiences

Leverage these rich attributes to personalize messages and campaign content at the user level. Tailor interactions with deep insights like “Total Savings via Membership” for retail or “Current Total Wealth” for fintech.

MoEngage Computed Traits on the Platform

Using Computed Traits is seamless. From defining and computing a new attribute to using it for creating granular segments and embedding it in campaigns, driving tailored engagement. Watch the demo below to see it on the platform.

From Raw Data to Real-World Value

Computed Traits helps brands drive critical business metrics:

• Higher Conversions and Customer LTV: Enables precise targeting and hyper-personalized experiences at every stage of the funnel, fostering deeper engagement and accelerating revenue growth.
• Stronger Retention & Lower Churn: Proactively identify at-risk users (e.g., using a “Churn Prediction Score”) and deliver highly relevant, timely experiences that boost loyalty.
• Enhanced Customer Experience: Accelerated time to market ensures timely engagement, and meaningful attributes are used not only for targeted but personalized and insightful engagement, ensuring a delightful customer experience.
• Effective Resource Utilization and Productivity: Eliminates manual workflows and reduces dependency on engineering, freeing up valuable marketing and technical bandwidth for higher-value initiatives.

Computed Traits in Action: Real-World Use Cases

Retail/E-commerce: A retail brand can compute an attribute “Total Savings via Membership” by adding the total value a customer has saved through discounts, free shipping, and exclusive offers. The brand can then send personalized messages celebrating the customer’s total savings and nudging them to renew membership.

BFSI: A financial platform can compute an attribute “Total Investment Portfolio Value”, by summing the ‘Current Value’ from all active ‘Investment Holding’ records for a customer. This can be used to target HNI customers with more than $500,000 in portfolio value, and engage them with persoanlized campaigns for exclusive investment planning programs.

Media/OTT: An OTT brand can compute an attribute called “Subscription Renewal Likelihood,” a score computed based on recent engagement, viewing patterns, and past renewal behavior, assigning weights. The brand can use this to target users with low likelihood scores with compelling offers or exclusive content teasers before their subscription expires.

Travel: A travel app can create an attribute “Preferred Travel Style” (e.g., “Luxury Traveler,” or “Budget Explorer”), computed based on total number of bookings, total booking value, the distinct destinations visited, and the amenities chosen, assigning weightages. This enables the brand to target different segments and personalize future travel offers and package deals based on their unique travel preferences.

Conclusion: Faster Data to Insights to Value

MoEngage Computed Traits delivers more than seamless attribute computation; it provides a strategic competitive advantage—significantly faster time to market with richer experiences. It fundamentally shifts sophisticated intelligence from being a bottleneck owned by engineering to a self-serve asset for marketers. This empowers your team to instantly build richer customer profiles, create granular targeting, and deliver experiences that truly resonate and convert.

Ready to stop wrangling data and start driving real business results? Request a demo today to see how you can create meaningful new attributes and accelerate your path to value.
Request a demo today.