Agnetic

Agnetic analyzes customer interaction history, engagement patterns, and preference signals to dynamically generate and adapt marketing message copy in real-time. Rather than static template variables, the system uses behavioral data (email open rates, click patterns, support ticket sentiment, product usage) to select messaging tone, content focus, and call-to-action variants that match individual customer context. This operates across email, SMS, and web channels with unified customer profiles.

Agnetic coordinates campaign delivery across email, SMS, and web channels with AI-driven timing optimization that accounts for individual customer timezone, engagement history, and channel preference. The system learns which channels and send times yield highest engagement per customer segment and automatically sequences messages to avoid fatigue while maintaining campaign momentum. Orchestration rules can be conditional based on customer actions (e.g., if email not opened in 24h, send SMS reminder).

Agnetic automatically classifies customers into lifecycle stages (prospect, trial, customer, at-risk, churned) based on behavioral signals and engagement patterns, then triggers stage-specific automation workflows. Each stage has predefined campaign sequences, messaging tone, and channel preferences. When a customer's behavior indicates stage transition (e.g., trial signup to paid customer, active customer to at-risk), the system automatically moves them to the new stage and initiates the corresponding workflow. Workflows can include email sequences, SMS alerts, support escalations, or sales outreach.

Agnetic monitors email delivery metrics (bounce rate, complaint rate, spam folder placement) and automatically handles bounces and complaints to maintain sender reputation. Hard bounces (invalid email addresses) are flagged and removed from future campaigns. Soft bounces (temporary delivery failures) are retried with exponential backoff. Complaints (spam reports) trigger automatic list suppression and may trigger customer support outreach. The system tracks sender reputation metrics (SPF, DKIM, DMARC alignment) and provides recommendations to improve deliverability.

Agnetic ingests customer data from CRM, support ticketing systems, and product analytics into a unified customer profile that marketing and support teams access through a shared interface. The system normalizes customer records across sources (deduplicating on email, phone, company domain) and enriches profiles with support ticket sentiment, resolution history, and support agent notes. This enables marketing campaigns to reference recent support interactions and support teams to see active marketing campaigns affecting the same customer.

Agnetic analyzes customer engagement trends, support ticket frequency, product usage decline, and renewal date proximity to calculate a churn risk score for each customer. When risk exceeds a threshold, the system automatically triggers targeted retention campaigns with messaging tailored to the likely churn reason (e.g., feature request not addressed, competitor comparison, pricing concerns). Intervention campaigns can include special offers, feature education, or direct outreach from customer success managers.

Agnetic provides a template editor that supports dynamic variable insertion, conditional blocks, and AI-assisted copy suggestions. Templates can reference customer profile data (name, company, plan tier), behavioral data (recent product features used, support ticket topics), and campaign context (offer amount, expiration date). The system generates template preview variations showing how different customer segments will see the final message, enabling marketers to validate personalization before sending.

Agnetic tracks campaign metrics (open rate, click rate, conversion rate, revenue attributed) across all channels and provides dashboards showing performance by segment, channel, and campaign variant. The system supports multi-touch attribution modeling that credits multiple touchpoints in a customer journey rather than last-click attribution, enabling marketers to understand which campaigns and channels drive actual revenue impact. Attribution models can be configured as first-touch, last-touch, linear, or time-decay.

Agnetic provides a visual segment builder that enables marketers to create customer segments using behavioral rules (e.g., 'customers who opened email in last 7 days AND clicked link AND have plan tier = Enterprise'). The rule engine supports AND/OR logic, date ranges, numeric comparisons, and text matching. Segments are evaluated dynamically against the customer database, updating in real-time as new engagement data arrives. Segments can be saved as reusable filters for campaign targeting or audience export.

Agnetic includes an AI writing assistant that generates email subject lines, body copy, and SMS messages based on campaign context (product, offer, target segment). The assistant learns brand voice and messaging guidelines from historical campaigns and applies them to generated copy, ensuring consistency across campaigns. Marketers can provide feedback on generated copy (thumbs up/down, edit suggestions) which fine-tunes the model for future generations. The system supports multiple tone options (formal, casual, urgent, educational) and can generate multiple variants for A/B testing.

Agnetic calculates lead scores combining engagement signals (email opens, clicks, website visits, content downloads) with firmographic data (company size, industry, location, technology stack) and behavioral signals (product feature interest, support ticket topics). Scoring models can be configured with custom weights for different signals, enabling organizations to prioritize leads based on their specific sales criteria. Scores update in real-time as new engagement data arrives, and leads can be automatically routed to sales when they exceed a threshold.

Agnetic enables marketers to set up A/B tests comparing different email subject lines, body copy, send times, or channels. The system randomly assigns customers to test variants, tracks engagement metrics (open rate, click rate, conversion rate) for each variant, and calculates statistical significance to determine if observed differences are real or due to chance. Tests can be configured with minimum sample size requirements and confidence level thresholds (e.g., 95% confidence). Results are displayed with confidence intervals and recommendations for winning variants.