sub-modules

Marketing & Campaigns

Campaign automation, omnichannel delivery, and marketing mix modeling for data-driven marketing.

Campaign automation, omnichannel delivery, marketing mix modeling, and multi-touch attribution for data-driven marketing at scale. Marketing & Campaigns connects creative output to audience segments to delivery channels with closed-loop measurement.

Campaigns That Learn and Adapt

subtitle: Visual campaign builder with automated optimization, multi-channel orchestration, and closed-loop attribution.

Visual Campaign Builder

desc: Drag-and-drop campaign flows with triggers, conditions, delays, and A/B splits.

Omnichannel Delivery

desc: Email, SMS, push, in-app, social, and webhook delivery from a single campaign definition.

Marketing Mix Modeling

desc: Statistical models that quantify the incremental impact of each marketing channel and tactic.

Multi-Touch Attribution

desc: Shapley value attribution across all touchpoints for true ROI measurement.

marketing-that-measures-everything

Marketing That Measures Everything The visual campaign builder lets marketers compose multi-step flows without engineering support. Campaigns start with a trigger (time-based, event-based, segment entry, or manual), flow through conditions and delays, and branch into A/B splits with automatic winner selection. Omnichannel delivery routes messages across email, SMS, push notifications, in-app messages, social channels, and custom webhooks from a single campaign definition. Send-time optimization selects the best delivery time and channel for each individual recipient based on historical engagement patterns. Marketing Mix Modeling uses Bayesian media mix models to quantify the incremental contribution of each channel. Unlike attribution models that assign credit to touchpoints, MMM answers the question 'What would have happened if we hadn't spent on this channel?' — enabling true ROI optimization.

data Viz

Marketing & Campaigns Specifications

Campaign Triggers: Time, event, segment entry, API, manual
Delivery Channels: Email, SMS, push, in-app, social, webhook
A/B Testing: Multi-variant with automatic winner selection
Send-Time Optimization: Per-recipient based on engagement history
MMM Approach: Bayesian media mix with incrementality testing
Attribution Windows: Configurable 1-90 day windows per channel
Throughput: 1M+ messages/hour across all channels

faq section

Section 1

How does send-time optimization work?

DXP analyzes each recipient's historical engagement patterns — when they open emails, when they're active in-app, when they respond to push notifications — and schedules delivery at the optimal time per individual, not a blanket send time for the entire list.

Section 2

What is the difference between attribution and marketing mix modeling?

Attribution assigns credit to individual touchpoints in a customer journey. MMM uses aggregate statistical models to measure the incremental impact of entire channels. Attribution tells you which touchpoints contributed; MMM tells you which channels are actually driving lift.

Section 3

Can campaigns target specific customer segments?

Yes. Campaigns can be triggered by segment entry, meaning they automatically activate when a customer enters a defined segment. They can also be filtered by segment membership at each step of the campaign flow.

Section 4

How do A/B splits determine winners?

Statistical significance testing (Bayesian or frequentist, configurable) determines winners. You set the confidence level, sample size, and success metric. Winners are automatically promoted and losers are stopped.

features

mmm: true

campaigns: true

attribution: true

omnichannel: true

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deployment types

Cloud (SaaS)