Optimize sending sales campaigns by predicting the best time
Send a sales campaign at the time when your customers are most likely to react to it
Difficulty: Introductory · Modules: Automation Hub
Channels
Industries
- Books
- Cosmetics
- Electronics
- Fashion & Apparel
- FMCG
- Home & Furniture
- Jewelry
- Multi
- Retail
- Telco
- Travel
Challenges addressed
- Increase the revenue
- Optimize your website and campaigns
Tags
- Predictions
- AI
- Automation
- Email communication
- Time optimizer
Summary
This use case focuses on using Synerise's AI prediction models to forecast customer behavior such as churn risk, purchase propensity, lifetime value, and next best action. Specifically, "Optimize sending sales campaigns by predicting the best time" enables your team to send a sales campaign at the time when your customers are most likely to react to it. This implementation balances ease of setup with meaningful business impact.
Example Scenario
Your retention team identifies that 15% of your active customer base shows signs of churn risk. Using Synerise's prediction models, each customer receives a churn probability score updated daily. High-risk customers are automatically enrolled in a retention campaign with personalized incentives, while medium-risk customers receive proactive engagement through preferred channels.
Business Challenge
- Increase the revenue
- Optimize your website and campaigns
Solution
Synerise Predictions uses machine learning models trained on your historical customer data to generate individual-level probability scores for key business outcomes. Available prediction types include churn propensity, purchase likelihood, expected revenue, and customer lifetime value. Predictions integrate seamlessly with segmentation, automation flows, and reporting — enabling data-driven decisioning at scale.
This implementation leverages Synerise's Automation Hub for building event-driven workflows, multi-step customer journeys, and triggered actions that run 24/7 without manual intervention. The solution delivers results through email campaigns with dynamic templates, send-time optimization, and behavioral triggers.
Expected Impact
Implementing this use case delivers measurable improvements across multiple business dimensions. Organizations typically see results within the first 1-2 weeks of deployment, with compound gains as the system learns from additional data. The introductory-level implementation complexity means teams can quickly validate the approach and scale from there.
Key KPIs
- Prediction accuracy (AUC-ROC score)
- Churn rate reduction in targeted segments
- Revenue saved through predictive retention
- Campaign ROI improvement vs. non-predictive targeting
- Prediction-driven conversion rate
Data Inputs
- email — email campaigns with dynamic templates, send-time optimization, and behavioral triggers
- Automation Hub — Automation Hub for building event-driven workflows, multi-step customer journeys, and triggered actions that run 24/7 without manual intervention
Implementation Steps
- 1. Define the prediction target (churn, purchase, revenue) and select the relevant time window
- 2. Prepare training data by ensuring sufficient event history and customer attributes
- 3. Train the prediction model and evaluate accuracy metrics
- 4. Deploy predictions as customer attributes available in segments and automation
- 5. Create targeted campaigns using prediction scores as segmentation criteria
- 6. Monitor model drift and retrain periodically with fresh data
Technology Explanation
Synerise Predictions leverage gradient-boosted tree ensembles and neural network architectures trained on customer behavioral sequences. Models are auto-tuned using Bayesian hyperparameter optimization and validated with time-series cross-validation to prevent data leakage. Predictions are computed as batch scores refreshed daily and made available as real-time customer attributes.
Platform Components
- Automation Hub — Automation Hub for building event-driven workflows, multi-step customer journeys, and triggered actions that run 24/7 without manual intervention
Channels
- email — email campaigns with dynamic templates, send-time optimization, and behavioral triggers
Target Industries
- Books
- Cosmetics
- Electronics
- Fashion & Apparel
- FMCG
- Home & Furniture
- Jewelry
- Multi
- Retail
- Telco
- Travel
Functionality Used
- Predictions — using Synerise's AI prediction models to forecast customer behavior such as churn risk, purchase propensity, lifetime value, and next best action
- AI — harnessing Synerise's full AI capabilities including machine learning models, natural language processing, and computer vision to automate decisions and discover insights from customer data
- Automation — orchestrating complex, multi-step customer journeys using Synerise Automation Flows that trigger actions based on events, conditions, and time-based rules across all channels
- Email communication — creating and sending targeted, personalized email campaigns using Synerise's built-in email capabilities, including dynamic templates, behavioral triggers, and deliverability optimization
- Time optimizer
Implementation Complexity
Introductory — This is a beginner-friendly use case that can be implemented quickly with minimal configuration.
Time to Value
1-2 weeks
Documentation
Related use cases