ReOnPlus – Agentic Profit & Growth Engine

ReOnPlus turns Tableau Next dashboards into an agentic profit engine, predicting high-impact opportunities, simulating outcomes, and triggering automated actions in Salesforce + Slack.

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🟢 ReOnPlus – Agentic Profit & Growth Engine

Inspiration

In many organizations, teams are drowning in data but starved for actionable insights. Traditional dashboards often show historical trends but fail to answer the critical question:

“What should we do next to maximize profit?”

We were inspired to create ReOnPlus to bridge this gap — a platform that not only predicts high-value opportunities but also executes actions automatically. The inspiration came from observing sales and marketing teams who:

  • Struggle to prioritize accounts efficiently
  • Spend excessive time manually translating insights into tasks
  • Lack real-time support for decision-making

We envisioned a solution where dashboards think, predict, and act, allowing teams to make profit-driven decisions instantly. This idea led to ReOnPlus: an agentic profit and growth engine.

What it does

ReOnPlus is designed to transform analytics into actionable insights:

  • Predict high-impact opportunities: Identify accounts, deals, or campaigns that will generate the most incremental profit using ML-based uplift modeling.
  • Simulate scenarios: Managers can adjust parameters like discounts, outreach frequency, or campaign budgets and instantly see predicted revenue impact.
  • Trigger automated actions: Decisions from the dashboard can automatically update Salesforce records or trigger Slack notifications, reducing manual effort.
  • Explain decisions: SHAP-based explanations help users understand why each recommendation is made, ensuring trust in AI-driven decisions.

Outcome: Faster, smarter, measurable decisions that directly impact revenue and growth.

How we built it

1. Data Sources

  • Salesforce CRM: Accounts, Opportunities, Contacts, and Activity data
  • Salesforce Data Cloud: Unified customer and transaction datasets
  • Marketing and engagement logs: Historical campaign data (synthetic for demo purposes)

2. Machine Learning Layer

We implemented uplift modeling to predict incremental revenue per action.

The general formula for uplift (U) for a given customer (i) is:

[ U_i = P(\text{Conversion} \mid \text{Treatment}_i) - P(\text{Conversion} \mid \text{Control}_i) ]

Where:

  • (P(\text{Conversion} \mid \text{Treatment}_i)) is the probability that customer (i) converts if targeted
  • (P(\text{Conversion} \mid \text{Control}_i)) is the baseline probability without intervention

We used:

  • LightGBM for uplift modeling
  • SHAP values for feature explainability, giving insight into which factors drive predictions
  • Scenario simulation by adjusting variables and recomputing expected profit:

[ \text{ExpectedProfit} = \sum_{i=1}^{n} U_i \times \text{Revenue}_i ]

3. Dashboard & Visualization

  • Built in Tableau Next, supporting interactive KPIs, prioritized accounts, and account drill-downs
  • What-if sliders allow managers to tweak discounts, outreach frequency, or campaign intensity
  • Displays both predicted uplift and actionable recommendations clearly

4. Agentic Automation

  • Salesforce Flow integration: Automatically updates CRM or launches tasks
  • Slack Integration: Real-time notifications for team members
  • Secure execution: Role-based permissions ensure only authorized users trigger actions

Challenges we ran into

  1. Synthetic Data Preparation: Limited access to real Salesforce data required creating realistic synthetic datasets
  2. Automation Integration: Linking Tableau buttons to Salesforce Flow + Slack Webhooks required careful API design and testing
  3. Explainable AI: Making ML predictions understandable to non-technical users required multiple iterations of design
  4. Time Management: Balancing ML model development, dashboard design, and automation within hackathon timelines was challenging

Accomplishments that we're proud of

  • Successfully implemented predictive + agentic dashboards that simulate revenue outcomes
  • Built a what-if simulation engine for real-time scenario analysis
  • Enabled one-click automation to act on insights in Salesforce and Slack
  • Created clear, interactive, explainable dashboards that demonstrate measurable business impact
  • Demonstrated full hackathon-ready prototype with actionable insights

What we learned

  • Agentic analytics requires a seamless combination of ML, visualization, and automation
  • Explainable AI is essential for user trust and adoption
  • Synthetic datasets can be used effectively for demo and testing purposes
  • Clear UX design is as critical as predictive accuracy for judges and end-users
  • Building for action, not just visualization, distinguishes exceptional projects

What's next for ReOnPlus – Agentic Profit & Growth Engine

  • Expand to multi-channel automation (email, SMS, ad campaigns) directly from dashboards
  • Add AI-driven recommendations to suggest optimal strategies for marketing and sales teams
  • Integrate sustainability / ESG metrics alongside profit for responsible growth
  • Implement real-time streaming predictions and continuous retraining for dynamic environments
  • Explore collaborative decision-making features for cross-team alignment

✅ Summary:
ReOnPlus is a next-generation agentic analytics platform. By combining ML-driven predictions, interactive dashboards, and automation, it transforms Tableau Next from a static visualization tool into a proactive, profit-generating engine, delivering measurable business impact while being user-friendly, transparent, and scalable.

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