Computed Traits for Telecom

Telecom apps generate some of the richest behavioral data in any industry: recharges, data usage, VAS activation, roaming, and device profile. Computed traits turn that raw activity into live user attributes you can segment on, personalize with, and branch Flows on, without engineering support or CSV uploads. This page is a menu. Each row tells you what to build, which computation method to pick, what event data it needs, the business outcome, and how to use it. New to computed traits? Computed Traits - Overview explains what they are and how each computation method works, and Create a Computed Trait walks through building one.

How Computed Traits Work

Every trait on this page follows the same four-step model. Once you recognize it, the tables below read at a glance.

Start with an event

A user behavior you already track, such as Recharge Successful or Daily Usage Summary.

Choose a method

Count, Aggregation, First/Last Value, or SQL.

Get a trait

A live attribute like Last Recharge Date that updates automatically.

Activate it

Use it in segmentation, analytics, Jinja personalization, and Flows.

Once a trait runs, it becomes a standard user attribute you can use anywhere in MoEngage: build segments from it, group and filter it in analytics, insert its value into message copy with Jinja personalization, or branch a Flow on it.

Most traits here are no-code. The Count Aggregation and First/Last Value methods need no SQL. Reach for SQL only for composite scores that combine multiple events, or for ratios such as redemption or completion rate. You can build every no-code trait on this page yourself.

Start Here

Build these three first. They give you the fastest return and the most reuse across campaigns.

Last Recharge Date

Last Value · The highest-impact telecom trait; unlocks all prepaid retention.

Preferred Recharge Denomination

Last Value · Transforms home-screen and SMS personalization.

Total Data Consumed

Aggregation · Powers plan-fit recommendations, no SQL needed.

Browse Traits by Goal

The event and property names in these tables are examples. The exact names in your account depend on how your app tracks data, so you might not find an event named Recharge Successful as written here. Check your tracked events and properties under Data before you build a trait, and ask your development team to track any that are missing. For how tracking works, see A Complete Guide to Event Tracking.

Recharge & Renewal

Every prepaid user has a recharge rhythm. Capture it, and each reminder lands at the right moment.

Computed Trait	Method	Data Source (Events / Properties)	Business Use Case	How to Use It
Total Recharges Last 90 Days	Count	Event: `Recharge Successful`	Tell heavy recharge users apart from light ones for VIP treatment and offer strength.	Run loyalty-tier promotions for heavy users, and skip low-value users from cashback-heavy offers.
Total Recharge Amount Lifetime	Aggregation (Sum)	Event: `Recharge Successful`; Property: `amount`	The prepaid equivalent of wallet share: rank users by revenue.	Segment top-revenue users for VIP roaming or device offers, and exclude them from margin-eroding promos.
Average Recharge Amount	Aggregation (Average)	Event: `Recharge Successful`; Property: `amount`	Recommend the right pack denomination without over-upselling.	Personalize in-app pack cards to around each user’s average recharge.
Preferred Recharge Denomination	Last Value	Event: `Recharge Successful`; Property: `pack_type` (Voice, Data, Combo, Unlimited)	Surface the exact pack the user actually buys, reducing friction.	Personalize the home screen, and send an SMS with a pre-filled recharge deep link.
Last Recharge Date	Last Value	Event: `Recharge Successful`; Property: `timestamp`	Detect users about to enter grace or disconnect state.	Build segments with date operators to send an escalating reminder sequence: 3 days before, 1 day before, on expiry, and 3 days after.
Recharge Cycle (Days)	SQL Computation	Average interval between `Recharge Successful` events	Per-user recharge cadence, the master trait for prepaid retention.	Run a Flow that sends a reminder at 90% of each user’s cycle length.

Data & Plan Fit

Match users to plans that fit their real usage, which cuts bill-shock complaints and drives voluntary upgrades.

Computed Trait	Method	Data Source (Events / Properties)	Business Use Case	How to Use It
Average Daily Data Usage (GB)	Aggregation (Average)	Event: `Daily Usage Summary`; Property: `data_gb`	Recommend the right data pack; mismatched users churn fastest.	Send a pack-upgrade offer when usage runs above 1.5x the plan, and a downsell suggestion when it runs well below.
Total Data Consumed (30 Days)	Aggregation (Sum)	Event: `Daily Usage Summary`; Property: `data_gb`	Absolute usage that sizes plan recommendations correctly.	Personalize pack-fit calculator emails with the value.
Data Overage Incidents	Count	Event: `Data Limit Exceeded`	Repeat overage users are prime upgrade candidates, or churn risks.	After 2 overages in 30 days, send an unlimited-pack offer and skip non-data upsells.
Streaming Data Ratio	SQL Computation	`Video Streaming GB` / `Total Data`	Ratio that flags streaming-heavy users for OTT bundle cross-sell.	Send an in-app OTT bundle offer, and a co-branded partnership push.

VAS & Roaming

Value-added services and roaming lift ARPU. These traits show who is ready for what.

Computed Trait	Method	Data Source (Events / Properties)	Business Use Case	How to Use It
Total VAS Services Activated	Count	Event: `VAS Activated`	Measure VAS adoption and spot saturation vs. opportunity.	Skip cross-sell when a user is already at 3 or more services, and upsell caller tunes or entertainment when they are at zero.
Preferred VAS Category	Last Value	Event: `VAS Activated`; Property: `category` (Entertainment, Utility, Regional)	Personalize the VAS carousel and recommend the right category.	Send a category-based email digest, and promote regional-language content.
Total Roaming Sessions	Count	Event: `Roaming Session Started`	Frequent travelers are prime targets for pre-departure roaming packs.	Send a roaming-pack push before the user’s typical travel window, and auto-enable premium roaming for frequent travelers.
Last Roaming Country	Last Value	Event: `Roaming Session Started`; Property: `country`	Localize roaming-pack offers per traveler.	Recommend region-specific packs, and run travel-partner co-marketing.

Cross-sell

Most telco customers hold a single product. These traits identify who is ready for a second.

Computed Trait	Method	Data Source (Events / Properties)	Business Use Case	How to Use It
Distinct Products Held	Aggregation (Count Distinct)	Event: `Product Owned Snapshot`; Property: `product_type`	A single-product vs. multi-product indicator.	Cross-sell to single-product users, and skip anyone already holding 3 or more products.
Last Broadband Interest Event	Last Value	Events: `Broadband Page Viewed`, `Callback Requested`; Property: `timestamp`	Surface hot leads for field sales quickly.	Run a Connector campaign that notifies your sales team when a user shows recent interest.
Prepaid-to-Postpaid Propensity	SQL Computation	Average recharge above a threshold, recharge consistency, and tenure over 12 months	Composite score identifying prepaid users ready to convert to postpaid.	Run a postpaid-offer campaign with a personalized bill estimate.
Broadband Cross-sell Score	SQL Computation	Home Wi-Fi usage patterns, no active `broadband_id`, and an available address	Composite score that prioritizes the field-sales pipeline for home broadband.	Send a webform and callback booking, and hand off to sales through a Connector campaign.

Churn & Retention

Prepaid churn is the biggest leak. These traits give you enough early warning to intervene.

Computed Trait	Method	Data Source (Events / Properties)	Business Use Case	How to Use It
Last Complaint Date	Last Value	Event: `Support Ticket Raised`; Property: `timestamp`	Customers who recently complained are at higher risk of porting out.	Run a Flow that sends an NPS survey and a goodwill offer within 7 days of a resolved ticket.
Total Complaints Raised	Count	Event: `Support Ticket Raised`	Serial complainers need dedicated support routing, not more marketing.	Route users with more than 3 complaints in 90 days to a support escalation, and exclude them from campaigns.
Days Since Last Recharge	Last Value	Event: `Recharge Successful`; Property: `timestamp`	The earliest recency-based churn indicator.	Build segments with date operators for an escalating win-back: a reminder push, then an offer, then a deeper offer.
Port-Out Risk Score	SQL Computation	Recharge delay, complaint recency, and competitor-site visit signals	Composite churn score for retention teams.	Trigger a retention call-back, and send a win-back offer over SMS.

Computed traits are used across MoEngage. These guides cover the surfaces referenced in the tables above:

Segments: build audiences from a computed trait’s value.
Analytics: group and filter user behavior by a computed trait’s value.
Campaigns and channels: the channels you can reach users on.
Flows: branch users down different paths based on a trait.
Message personalization: insert trait values into your content with Jinja.
Data: how MoEngage collects and manages the event data these traits build on.

​How Computed Traits Work

​Start Here

Last Recharge Date

Preferred Recharge Denomination

Total Data Consumed

​Browse Traits by Goal

​Recharge & Renewal

​Data & Plan Fit

​VAS & Roaming

​Cross-sell

​Churn & Retention

​Related Documentation

How Computed Traits Work

Start Here

Browse Traits by Goal

Recharge & Renewal

Data & Plan Fit

VAS & Roaming

Cross-sell

Churn & Retention

Related Documentation