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MarTech

Stop managing tools. Start running campaigns that convert.

We build campaign orchestration platforms, attribution engines, AI content tools, and customer analytics systems for marketing teams. The software that makes your team 3x more productive without adding headcount.

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85%

Traffic increase

28%

Conversion lift

Overview

Your marketing stack became the bottleneck

MarTech software development at 1Raft focuses on the three areas that most directly impact pipeline: attribution accuracy, content velocity, and segmentation precision. We bring patterns from 100+ products across adjacent industries to build multi-touch attribution engines, AI content platforms, predictive segmentation tools, and campaign orchestration systems - each engineered to drive measurable CAC reduction and conversion lift.

Marketing teams use an average of 12 tools. They spend more time moving data between platforms, building reports, and managing integrations than actually running campaigns. The stack that was supposed to drive growth became the bottleneck.

We build the connective tissue. Campaign orchestration that unifies channels. Attribution models that actually tell you which spend drives revenue. Content systems that produce on-brand assets in minutes instead of days.

Every product we build reduces tool count, automates repetitive workflows, and gives marketing leadership the metrics they need to make confident budget decisions.

Experience Signal

1Raft builds attribution engines, campaign platforms, AI content tools, and analytics systems for SaaS companies, D2C brands, and multi-brand retailers. Our engineering draws on patterns validated across 100+ products in adjacent industries.

85%

Traffic increase

28%

Conversion lift

Industry Pain Points

What's broken in martech

01

Marketing teams manage 10-15 disconnected tools and spend 30% of their time on data wrangling instead of strategy

02

Multi-touch attribution is a spreadsheet exercise - nobody trusts the numbers, so budget allocation is based on gut feel

03

Content production bottlenecks marketing velocity - every campaign waits for design and copy resources

04

Personalization requires engineering tickets that take weeks, so most customers see the same generic experience

05

Customer segmentation relies on static lists that are outdated by the time campaigns launch

06

CAC keeps rising because teams optimize channels in isolation without understanding the full conversion path

Solutions

Problems we solve in martech

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Campaign Orchestration

Unified platform that plans, executes, and optimizes campaigns across email, paid, social, and web. AI adjusts send times, audiences, and creative based on real-time performance signals.

02

Multi-Touch Attribution Engine

Connects ad platforms, CRM, and revenue data to build a unified attribution model. Shows which touchpoints drive pipeline and revenue - not just clicks. Marketing and finance trust the same numbers.

03

Content Generation and Optimization

Generates on-brand copy, email variations, ad creative, and social posts using your brand voice and past performance data. Includes A/B testing at scale - test 20 variants instead of 2.

04

Predictive Customer Segmentation

AI segments customers by purchase propensity, lifetime value, churn risk, and engagement patterns. Segments update in real time as behavior changes - no more stale lists.

05

Real-Time Web Personalization

Personalizes website content, CTAs, and product recommendations based on visitor behavior, firmographic data, and intent signals. No engineering tickets required - marketing controls the rules.

06

Marketing Analytics and Reporting Automation

Pulls data from every tool into a single reporting layer. AI generates weekly performance summaries, flags anomalies, and recommends budget reallocation based on ROI trends.

Use Cases

Real-world use cases

Attribution Engine for a B2B SaaS Company

Problem

A $30M ARR SaaS company spent $4.2M annually on marketing but couldn't attribute pipeline to specific channels. The CMO made budget decisions using last-click data from Google Analytics.

What we built

We built a multi-touch attribution system connecting their ad platforms, HubSpot CRM, and Stripe revenue data. Implemented data-driven attribution models with pipeline and revenue credit across all touchpoints.

Result

The team discovered LinkedIn drove 3.2x more pipeline per dollar than Google Ads. Budget reallocation decreased CAC by 28% and increased marketing-sourced pipeline by 41% in two quarters.

AI Content Platform for a D2C Brand

Problem

A direct-to-consumer brand needed 200+ content assets per month across email, social, and web. Their 3-person content team was a bottleneck. Campaign launches delayed an average of 8 days.

What we built

We built an AI content platform trained on their brand voice, past campaigns, and product catalog. Generated email, social, and ad copy with automated A/B variant creation and performance-based optimization.

Result

Content production increased 4x without adding staff. Campaign launch delays eliminated. Email open rates improved 22% through AI-optimized subject lines and send-time personalization.

Campaign Orchestration for a Multi-Brand Retailer

Problem

A retailer with 4 brands managed campaigns separately in each brand's Mailchimp, Meta, and Google accounts. No cross-brand audience insights. Duplicate ad spend targeting the same customers.

What we built

We built a unified campaign orchestration platform with shared audience data, cross-brand suppression rules, and centralized performance reporting. Each brand maintained creative control with shared operational intelligence.

Result

Eliminated $380K in duplicate ad spend annually. Cross-brand customer identification increased remarketing efficiency 35%. Campaign setup time dropped from 3 days to 4 hours.

Our Approach

How we approach martech projects

1
Phase 1· Weeks 1-2

Marketing Stack Audit and Metrics Mapping

We audit your current tools, data flows, and reporting. We identify redundancies, data gaps, and the metrics that actually matter for your business model.

Deliverables

  • Tool stack audit with redundancy and gap analysis
  • Data flow map showing where attribution breaks
  • Metrics framework tied to revenue outcomes, not vanity metrics
2
Phase 2· Weeks 3-4

Product Design and Data Architecture

We design the product with your marketing team, not just your engineering team. Data architecture keeps attribution clean, real-time segmentation, and reliable reporting.

Deliverables

  • Product wireframes validated by marketing operators
  • Data architecture connecting all marketing and revenue sources
  • Integration specifications for ad platforms, CRM, and analytics
3
Phase 3· Weeks 5-10

Build, Integrate, and Validate

We build in sprints, integrating with your live marketing stack. Your team uses the product on real campaigns before launch to validate workflows and data accuracy.

Deliverables

  • Working product integrated with live marketing tools
  • Data validation report comparing new system to existing metrics
  • Marketing team feedback incorporated into final iteration
4
Phase 4· Weeks 11-12

Launch, Training, and Optimization

We launch with team training and documentation. Ongoing optimization uses performance data to improve AI models, attribution accuracy, and campaign recommendations.

Deliverables

  • Full launch with marketing team trained and self-sufficient
  • Performance baseline for continuous improvement tracking
  • Optimization playbook for the marketing team to run independently

Outcomes

Measurable outcomes

25-40% reduction in customer acquisition cost through better attribution and budget allocation
3-5x increase in content production velocity without additional headcount
30-50% reduction in campaign setup and launch time through automation
15-25% improvement in conversion rates through real-time personalization
Unified reporting that marketing and finance trust - one source of truth for ROI
Elimination of 3-5 redundant tools from the marketing stack, reducing SaaS spend by $50K-$200K annually

Pattern Transfer

1Raft built predictive segmentation for an insurance risk scoring product before applying the same behavioral clustering approach to marketing audience segmentation. Both require grouping entities by behavioral patterns to predict future actions. Insurance predicts claims risk; marketing predicts purchase intent. Same math, different labels.

Services

Services for martech

Frequently asked questions

Projects range from $50K-$200K. An attribution engine starts around $50K. A full campaign orchestration platform with AI content runs $120K-$200K. We define scope and pricing in a strategy session before work begins.

Next Step

Every dollar of CAC you can't attribute is a dollar you're probably wasting.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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