Executive Summary

Hightouch provides a Composable Customer Data Platform (CDP) built on top of your existing Databricks data warehouse, eliminating the need for manual segment management and enabling real-time, AI-powered offer recommendations at scale.

This implementation plan maps Hightouch's capabilities to your Offer Management Strategy requirements, providing a detailed roadmap for replacing manual processes with intelligent, self-service automation.

Quick Navigation
Feature Mapping Architecture Overview Solution Comparison Implementation Phases Databricks Integration AI Decisioning Setup

Requirement to Hightouch Feature Mapping

Your Requirement Hightouch Feature Business Benefit
Eliminate manual segment management (~70 segments) Customer Studio & Audience Builder
No-code audience building using all warehouse data		 Marketers build audiences independently; no IT tickets; infinite segmentation without manual overhead
Dynamic offer filtering based on customer attributes Real-time Personalisation
Filter offers using complete customer 360 data in <1 second		 Real-time eligibility checks, credit limits, device preferences applied automatically
Intelligent offer ranking & personalisation AI Decisioning
Reinforcement learning matches offers to customers by action probability		 1:1 personalisation at scale; no need to create more segments; continuous learning from interactions
Business rule management without IT Self-Service Platform
Governance, access control, and business rule configuration		 Business teams manage filtering rules, guardrails, and offer priorities independently
Real-time decisioning across all channels 250+ Integrations
Sync to CRM, mobile apps, marketing automation, outbound systems		 Unified offer recommendations across app, CRM, email, SMS, and call centre channels
Databricks integration for ML models Native Databricks Support
Built for data warehouses; direct connection to Databricks		 Leverage existing ML models, feature engineering, and Customer 360 data without data duplication
Multi-armed bandit for exploration/exploitation AI Decisioning Algorithms
Balances proven offers with testing new propositions automatically		 Automated A/B testing and exploration; addresses cold start for new offers
Continuous learning from customer interactions AI Decisioning Learning Loop
Studies customer responses and updates predictions in real-time		 Self-improving system; better recommendations over time; insights on customer patterns
Journey orchestration & campaign prioritisation Journey Builder
Plan customer journeys and ensure cohesive messaging		 Coordinate offer presentation timing; prevent message conflicts; optimise customer experience
Campaign performance measurement Intelligence & Analytics
AI-powered measurement and analysis of campaign performance		 Understand which offers work best; ROI tracking; data-driven optimisation

Hightouch Architecture Overview

Databricks

Customer 360, ML Models, Offer Data, Feature Engineering

Hightouch CDP

Composable CDP Layer: Audience Builder, Identity Resolution, Real-time Personalisation

AI Decisioning

Offer Ranking, 1:1 Personalisation, Reinforcement Learning

Activation Channels

CRM, Mobile App, Email, SMS, Call Centre, Outbound Campaigns

1

Data Warehouse Foundation

Hightouch reads directly from Databricks - no data duplication, no security risks. All your existing ML models, customer data, and offer feeds remain in Databricks.

2

Composable CDP Layer

Hightouch adds CDP capabilities on top: audience building, identity resolution, real-time personalisation - all using your warehouse data.

3

AI Decisioning Engine

AI Decisioning pulls from your content library (offers) and matches messages with each customer based on their probability of taking action, using reinforcement learning.

4

Multi-Channel Activation

Best offers are synced to all channels via 250+ integrations: Salesforce, HubSpot, mobile apps, Iterable, and more - all in real-time.

Solution Comparison: Pega CDH vs Hightouch

Pega Customer Decision Hub

Traditional
  • 🏗️Separate decisioning platform requiring integration
  • 💰Higher licensing costs for enterprise decisioning
  • ⏱️Longer implementation timeline (6-12 months)
  • 🔧Requires Pega-specific expertise and training
  • 📊May require data replication or ETL processes
  • 🎯Primarily focused on decisioning, less on CDP capabilities

Hightouch Composable CDP

Modern
  • 🏗️Built on your existing Databricks infrastructure
  • 💰More cost-effective, usage-based pricing model
  • ⚡Faster implementation (2-4 months typical)
  • 🎓Intuitive UI, minimal training required
  • 🔒No data storage - reads directly from warehouse
  • 🎯Combines CDP + AI Decisioning + Activation in one platform

Key Advantages of Hightouch

🏆

Databricks Investment

Hightouch is the only CDP with investment from both Databricks and Snowflake, ensuring deep integration and ongoing support.

🔒

No Data Duplication

Hightouch doesn't store your data - it reads directly from Databricks. More secure, no data silos, leverages existing governance.

📊

All Your Data, Not Just CDP Data

Traditional CDPs are limited to user and event data. Hightouch leverages ALL your warehouse data - offers, credit scores, transaction history, etc.

🤖

AI Agents for Marketers

Hightouch Agents help marketers answer questions, find opportunities, and automate workflows without waiting for data teams.

Implementation Phases

Phase 1
Weeks 1-4

Foundation & Setup

Databricks Connection: Connect Hightouch to Databricks, configure authentication and data access. Data Models: Create Hightouch models mapping to your customer 360, offer feeds, and eligibility data. Initial Audiences: Replicate existing ~70 segments as Hightouch audiences for validation.

Phase 2
Weeks 5-8

Offer Filtering Rules

Business Rules Configuration: Set up filtering rules for technical eligibility (Apple/Android), credit limits, affordability checks, risk-based filtering. Segment-Specific Rules: Configure different rules for EARLY_UPGRADER, LIGHT_SLEEPER, SLEEPER, HIBERNATOR segments. Testing: Validate filtering logic against existing manual processes.

Phase 3
Weeks 9-12

AI Decisioning Setup

Content Library: Load all available offers into Hightouch as content items. AI Model Training: Configure AI Decisioning with goals (upgrade conversion, revenue), guardrails (frequency limits, brand standards), and initial training data. Multi-Armed Bandit: Set up exploration algorithms for new offers and cold start scenarios.

Phase 4
Weeks 13-16

Channel Integration

CRM Integration: Connect to Salesforce/HubSpot for advisor-facing offer recommendations. Mobile App API: Integrate Hightouch API for real-time offer display in app. Marketing Automation: Connect to Iterable/email platforms for outbound campaigns. Journey Orchestration: Set up customer journeys to coordinate offer timing across channels.

Phase 5
Weeks 17-20

Optimisation & Rollout

Performance Monitoring: Use Hightouch Intelligence to measure offer performance, conversion rates, and ROI. Continuous Learning: AI Decisioning learns from customer interactions and improves recommendations. Gradual Rollout: Start with pilot segments, expand to full customer base. Training: Train marketing and business teams on self-service platform usage.

Databricks Integration Details

🔗

Direct Warehouse Connection

Hightouch connects directly to Databricks using SQL queries. No ETL required - queries run against your existing tables and views. Supports Unity Catalog for governance.

📊

Leverage Existing ML Models

Use your existing Databricks ML models (churn prediction, upgrade propensity, credit scoring) as features in Hightouch audiences and AI Decisioning. Models remain in Databricks.

🔄

Real-time Data Sync

Hightouch queries Databricks on-demand for real-time personalisation, or can sync data incrementally for batch processing. Supports both streaming and batch use cases.

🔒

Security & Governance

Uses Databricks authentication and access controls. No data leaves your warehouse. Supports multi-region deployments and compliance requirements (SOC 2, ISO 27001).

AI Decisioning Configuration

🎯

Define Goals & Outcomes

Configure AI Decisioning to optimise for upgrade conversion, revenue, or customer lifetime value. Define positive outcomes (purchase, upgrade) and negative outcomes (unsubscribe, churn).

🛡️

Set Guardrails & Rules

Define business rules: frequency limits (max offers per customer per week), timing rules (days of week, times of day), brand standards (only show offers customer can afford).

📚

Content Library Management

Load all available offers into Hightouch content library with metadata: device type, price, data allowance, eligibility criteria. AI matches content to customers automatically.

🧪

Exploration vs Exploitation

Configure multi-armed bandit algorithms to balance showing proven offers (exploitation) with testing new offers (exploration). Addresses cold start for new offers using synthetic data or similar customer matching.

📈

Learning & Optimisation

AI Decisioning continuously learns from customer responses. Updates millions of predictions in real-time. Surfaces insights on customer patterns and offer performance in dashboard.

Expected Success Metrics

⏱️

Time Savings

Eliminate 80%+ of manual segment management time. Marketers can build and test audiences in minutes instead of days.

📈

Conversion Improvement

1:1 personalisation typically increases conversion rates by 20-40% compared to segment-based approaches.

💰

Revenue Impact

Better offer matching and reduced churn through relevant recommendations. AI Decisioning optimises for revenue automatically.

🎯

Scalability

Handle infinite customer segments without manual overhead. System scales automatically as customer base grows.

Recommended Next Steps

1

Schedule Hightouch Demo

Request a personalised demo focused on offer management use case. See AI Decisioning and audience builder in action.

2

Technical Assessment

Review Databricks schema and data models with Hightouch technical team. Identify integration points and data requirements.

3

Proof of Concept

Run 4-6 week POC with subset of offers and customer segments. Validate filtering rules, AI Decisioning performance, and channel integration.

4

Business Case Development

Quantify ROI based on time savings, conversion improvements, and revenue impact. Compare total cost of ownership vs Pega CDH.