Core data model

The ModernLTV platform is built on a scalable, componentized architecture that is comprised of a set of primary data objects. When syncing data to ModernLTV, it helps to have an understanding of our underlying data model. ModernLTV considers the following objects to be primary data types, since ModernLTV is designed to natively handle these forms of data. This is a non-exhaustive list of data types in ModernLTV:

Conceptual flow of data

Zero copy, no override

We never replicate or override your existing customer data.

Real-time sync

Your sources & destinations always receive a live stream of data.

Full access & control

Manage your end-to-end data workflows without an engineer.

Primary data objects

Object

Description

Resources

Client

An individual business entity that purchases and uses the ModernLTV platform.

Starter Guide

Customer

Customer represents people, using either an email (email) or a specified customer ID (id) as a primary identifier.

Customer API

Source

System, database, or application that sends customer data to ModernLTV.

Destination

System, database, or application that receives customer data from ModernLTV.

Attribute

Unique trait or data property associated with a Customer.

Attributes API

Core intelligence

ModernLTV automatically generates a set of out-of-the box customer attributes.

Attributes API

Model

An AI, ML or predictive model that is integrated with ModernLTV and powers a specific Attribute

Models API

Sources

To use the ModernLTV platform, you need to connect your various sources of customer data. You can connect sources in a few clicks using our pre-built integrations or build a custom source by calling our Sources API.

Here is an overview of the types of data sources that you can connect to the ModernLTV platform.

Source

Description

Examples

3rd-party

Attributes generated from an external system, database, or app.

Stripe, Klaviyo, Snowflake, Zendesk

Custom

Attributes generated by combining or calculating existing attributes and/or ingested from an in-house system , database, or application.

Production DB, MySQL

ModernLTV

Pre-built attributes automatically generated by ModernLTV based on data ingested from existing sources.

'Total revenue'

Destinations

In order to sync real-time to any of your tools and teams, you need to connect destinations. You can connect destinations in a few clicks by using our pre-built integrations or build a custom destination by calling our Destinations API.

Below is an overview of the types destinations you can connect to the ModernLTV platform.

Source

Description

Examples

Marketing

Sync data to marketing automation tools to drive engagement, retention, etc

Klaviyo, Hubspot, Braze

Advertising

Sync data to search, display networks, social media channels, etc, to optimize ROAS

Google, Meta

Support

Sync data to customer support tools to enable more effective and personalized experiences

Zendesk, Intercom

Analytics

Sync data to analytics tools to expand & deepen the data available to analyze performance & behavior

Mixpanel, Amplitude

Data platform

Sync data to a customer data platforms, data warehouse, or data lake to maintain source of truth

Segment, Databricks, Snowflake

AI / ML

Sync data to internal or external models to fine tune and maximize intelligence

TensorFlow, Llamma-3, internal model

Attributes

A customer attribute, also known as a"user property" or "user trait", is a data property that describes a unique customer. Every attribute has an attribute_id, attribute_name, and value and. The value is delivered as one of several data types:

Integer
Float
String
Date
Boolean
Array / list
JSON

After connecting one or more data sources, ModernLTV unifies, normalizes, and automatically generates all identified attributes, which are surfaced in the Data Manager. You can also create one of several types of custom attributes right from your Data Manager with no limits and zero code. If you need a greater degree of customization, you can also create attributes by calling our Attributes API.

Type

Description

Example

Base

Attributes automatically generated and passed through directly from a a connected source.

'Email address', 'Total revenue'

Segment

A custom attribute created by defining a group of customers based on one or more attributes

'High value customers'

Calculated

A custom attribute created by defining a formula and using one or more existing attributes

'Total engagement'

Intelligent

Created by leveraging an internal or external AI/ML or predictive model

'Lifetime value'

Segments

The ability to build and target key customer segments is a critical function for any business. In the ModernLTV platform, a Segment is a type of boolean Attribute that is applied to any customer with a defined set of attributes.

Core intelligence

Based on the data sources you connect, ModernLTV automatically generates a set of intelligent, out-of-the-box customer attributes.

Attribute

Definition

Example value

Total lifetime revenue

Sum of all payment transactions to date.

$150.55

Average monthly revenue

The revenue per month generated by the customer over lifetime.
Total lifetime revenue / Tenure (months)

$55.23

Projected lifetime value

Sum of total revenue and projected future revenue collected.
‍Total lifetime revenue + Forecasted revenue

$250

Tenure (months)

Total months as an active customer.

12

High value customer

Active customers in the top 5% of total lifetime revenue with one or more transaction in last 30 days.

True, False

Average days between purchases

The average number of days between purchases or payments over lifetime.

15

High risk customer

Active customers with no payment method or multiple payment failures.
Payment method = null or Total payment failures > 1

True, False

Average order value

Average value across all lifetime transactions.
Total lifetime revenue / Total lifetime transactions

$25.55

Recently churned

Customers that churned in the last 7 day‍

True, False

High intent customer

Active customers with multiple website or app visits but no transactions in last 30 days.

True, False

When processing your data, ModernLTV takes exhaustive measures to identify and generate all available attributes. However, there are a number of potential reasons that ModernLTV is unable to do so and may require manual configuration. Below are the most common reasons:

Reason

Recommended action

Required source not connected

Connect new source

Unable to verify input data

Map correct attributes

Insufficient or unavailable input data

Connect new source or map correct attributes

Models

At the core of ModernLTV's platform is the ability to easily transform all your customer data into actionable customer intelligence. ModernLTV's value proposition is not limited to just unlocking existing data but also the generation of net new intelligence, powered by the leading AI/ML innovation. ModernLTV provides turnkey access to the latest and greatest AI, ML and predictive models to power your customer attributes and segments.

Below are several of the most common use cases:

Use case

Description

Examples

Forecast a value

Build a projection of a certain key customer metric

Lifetime value

Predict behavior

Predict a certain customer action or inaction to deliver proactive experience

Churn probaility

Make recommendation

Deliver a more hyper-personalized experience to drive engagement and ROI

Next best action

Segment customers

Identify more sophisticated groups to drive more targeted engagement

Valuable customers

Optimize allocation

Identify the optimal allocation of time, capital, and resources

Marketing mix

Analyze sentiment

Extract actionable insights from every customer touchpoint

NLP / text analysis

Core Concepts

ModernLTV Platform

Core data model

Zero copy, no override

Real-time sync

Full access & control

Primary data objects

Object

Description

Resources

Sources

Source

Description

Examples

Destinations

Source

Description

Examples

Attributes

Type

Description

Example

Segments

Core intelligence

Attribute

Definition

Example value

Reason

Recommended action

Models

Use case

Description

Examples

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