From Fragmented Campaign Data to AI-Driven Next Best Action

Mobiz operates at the intersection of large-scale campaign execution and customer engagement, processing millions of interactions across SMS sends, clicks, replies, page visits, and conversions.

As the platform scaled, valuable customer signals existed across multiple systems but were fragmented, difficult to trust, and hard to operationalize consistently. Marketing investment was increasing, yet targeting decisions still relied on static rules, manual intuition, and disconnected reports. At the same time, compliance risk and customer fatigue were becoming harder to manage.

Mobiz needed a way to turn engagement data into reliable, explainable intelligence without sacrificing transparency, governance, or control.

The Challenge

Objective

• Engagement and intent data lived in silos across campaigns and channels
• Move toward intelligent, AI-driven decision-making, but in a way the business could understand, trust, and scale safely.
• Targeting logic was rule-based and difficult to evolve
• There was no consistent explanation for why customers were prioritized or suppressed
• Compliance and fatigue rules required manual oversight
• Introducing AI carried risk without a trusted data foundation

Cyann’s Approach

Cyann designed a phased, trust-first architecture using Microsoft Fabric as the unifying analytics and intelligence platform.

Rather than deploying black-box automation, the solution focused on delivering incremental value at each stage while laying the groundwork for advanced AI capabilities. Each phase was designed to stand on its own, ensuring the business could realize benefits early and build confidence over time.

Phase 1.1: Unified Data Foundation

Every intelligent system starts with reliable data. Using Microsoft Fabric, Cyann unified campaign and engagement data including sends, clicks, page views, replies, and conversions into a standardized, analytics-ready structure.

This created:

• A single, trusted customer activity view
• Consistent schemas across campaigns
• A shared feature foundation for analytics and machine learning

Outcome: Reporting accuracy improved immediately, and the organization became ML-ready without deploying any predictive models.

Phase 1.2: Explainable Lead Scoring

Before introducing machine learning, Cyann implemented a transparent, rule-based lead scoring layer.

Engagement and intent signals were converted into interpretable scores, with every point earned for a clear reason such as recent clicks, page visits, replies, or sentiment indicators.

Business users could:

• Adjust scoring weights through a simple interface
• Instantly re-score large volumes of leads
• Classify leads as Hot, Warm, Cold, or Blocked

Most importantly, every score was explainable.

Outcome: Faster follow-ups, improved budget allocation, and strong internal trust in the system’s outputs.

Phase 2: Machine Learning in Shadow Mode

With a trusted baseline in place, Cyann introduced predictive machine learning using Microsoft Fabric’s data science and orchestration capabilities.

Models were trained to predict:

• Likelihood of conversion
• Probability of click or reply
• Risk of opt-out or disengagement

To reduce risk, models ran in Shadow Mode, generating insights without triggering live actions. Results were compared directly against rule-based decisions.

Explainable AI techniques highlighted which factors influenced each prediction, ensuring transparency at every step.

Outcome: Smarter targeting insights validated safely, with zero operational disruption.

Phase 3: Compliance and Guardrails by Design

To ensure intelligence never compromised governance, Cyann embedded compliance controls directly into the decision pipeline.

Before any action was taken, the system enforced:

• DND and STOP compliance rules
• Fatigue thresholds to prevent over-messaging
• Deliverability protection based on opt-out trends
• Content risk checks for regulated keywords

These guardrails acted as hard overrides that no model score could bypass.

Outcome: Reduced legal risk, protected sender reputation, and confidence to scale personalization.

Phase 4: Next Best Action Intelligence

With data, scoring, machine learning, and governance aligned, the platform evolved from scoring leads to recommending actions.

Using Microsoft Fabric as the intelligence layer, the system began recommending the Next Best Action for each customer:

• Whether to message or pause
• Which channel to use
• When to engage
• What type of follow-up was most appropriate

Recommendations were context-aware, explainable, and aligned with business rules.

Outcome: Personalized customer journeys at scale, driven by intelligence rather than manual rules.

Phase 5: Autonomous Campaign Orchestration (Future Vision)

The long-term vision is a self-optimizing campaign engine.

In this phase, Microsoft Fabric enables continuous learning loops where each interaction improves the next. Campaign strategies adapt automatically to customer behavior, channel performance, and risk signals while remaining fully governed and auditable.

Outcome: Millions of individualized journeys optimized in real time, with marketers focused on strategy rather than execution.

Business Outcomes

By adopting this phased approach, Mobiz achieved:

• Higher targeting precision with reduced wasted spend
• Faster, safer decision-making at campaign scale
• Built-in compliance and fatigue protection
• Transparent, explainable AI adoption
• A future-ready foundation for autonomous marketing

Most importantly, trust was maintained at every stage of the journey.

Conclusion

AI-driven marketing success does not come from rushing into automation. It comes from building intelligence on a foundation of trust, clarity, and governance.

By reimagining the campaign pipeline using Microsoft Fabric, Cyann helped Mobiz transform fragmented engagement data into a scalable, explainable, and future-ready Next Best Action platform one phase at a time.

About Cyann
Cyann partners with organizations to design and deliver practical AI and data platforms that prioritize transparency, governance, and measurable outcomes. Our approach ensures that intelligence scales responsibly and delivers real business value.

Learn more at cyann.ai.

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