Is it really possible to automate a repetetive customer-focused task like lead engagement without losing the human touch?
Leads Wizard (LW) is a groundbreaking AI system that redefines lead engagement with innovation, empathy, and measurable business impact. Developed in a fast-paced startup environment, LW is designed to bridge the gap between automation and human-centric engagement.
What LW changed: Automated lead engagement and conversions where manual & templated efforts fell short.
- 🧠 Chain-of-Thought (CoT) Reasoning: Advanced step-by-step processing for complex scenarios.
- 💡 Dynamic Emotional Scoring: Real-time sentiment analysis for empathetic responses.
- 🔄 Filtered Few-Shot Learning (fFSL): Cost-effective iterative improvements without traditional fine-tuning.
- 📈 Strategic Response Planning: Aligns conversations with business outcomes.
- +56% engagement improvement in the Intake stage.
- +45.9% sentiment improvement in the Engaged segment.
- +17% higher lead qualification rates than templated methods.
- Interactive Visualizations to see the real-world impact at a glance (Flourish Interactive Visuals):
- Features and Supporting Tools showcasing technical excellence: Jump to tools
- Introduction
- Features Summary
- Key Metrics and Visualizations
- Supporting Tools
- Timeline
- Explore dataset, Data Handling protocols and privacy
- License
Leads Wizard (LW) is an AI-powered innovation designed to revolutionize lead engagement and qualification. LW employs advanced techniques like Chain-of-Thought (CoT) reasoning, filtered Few-Shot Learning (fFSL) dynamic emotional scoring, and strategic planning to deliver empathetic, business-driven results. Developed and iterated in a high-pressure startup environment, LW transitioned from a custom GPT workflow to a streamlined API-integrated system, delivering tangible value to both users and businesses.
Leads Wizard (LW) incorporates cutting-edge AI techniques and systems-level innovation to deliver exceptional performance in lead engagement. Below is a high-level overview of its core features:
- Inner Monologue and Chain-of-Thought Reasoning (CoT)
Enhances response quality by simulating step-by-step reasoning for tailored and intelligent conversations. - Filtered Few-Shot Learning (fFSL)
Introduces a cost-effective, systems-level feedback loop that iteratively improves LW’s performance by leveraging real-world data. - Dynamic Emotional Scoring and Context Analysis
Analyzes user sentiment and contextual cues to deliver empathetic, validating, and context-aware responses. - Strategic Response Planning
Dynamically adjusts tone, framing, and goals to align lead interactions with high-level business objectives. - Modular Architecture
Enables seamless integration with APIs and external data sources, offering flexibility and scalability. - Iterative Improvement Ecosystem
Integrated workflows with open-source tools like the meta-labeller and LW chrome extension to extract, analyze, and refine responses continuously.
By leveraging these advanced techniques, Leads Wizard delivers measurable business outcomes that surpass traditional methods. Below, are the tangible improvements LW achieved.
LW demonstrates groundbreaking improvements across critical performance metrics when compared to traditional methods (Manual and Templated). These metrics, derived from over 250+ anonymized conversation segments, underscore LW’s ability to seamlessly balance empathetic engagement with strategic business outcomes.
This comparison highlights LW's significant impact on lead sentiment, engagement, and customer effort:
Figure 1 - Key Metrics Bar Graph | Click for interactive visual (Flourish)
- Leads Wizard improved engagement by 56% over templated methods in the Intake segment. This directly translates into higher user satisfaction and more meaningful lead interactions.
This visualization emphasizes LW’s superior ability to provide empathetic and effective user interactions compared to traditional approaches.
The slope graph below tracks lead retention as they progress through the sales funnel stages (Intake, Engaged, Qualified):
Figure.2 - Sales Funnel Stages Slope Graph | Click for interactive visual (Flourish)
- LW retained a remarkable 95.12% of leads, compared to Manual (83.61%) and Templated (63.29%) responses—outperforming manual approaches by over 11 percentage points, and increasing conversion rates as a result.
Why this matters: These metrics demonstrate LW’s strategic ability to guide leads through complex sales funnels effectively, ensuring higher lead retention, qualification and subsequently, conversion rates.
The innovative chord diagram below showcases LW’s superiority in composite metrics like Emotional Engagement Index (EEI) and User Perception Index (UPI) compared to traditional methods:
Figure.3 - Chord Diagram with Composite Metric Comparison | Click for interactive visual (Flourish)
- LW scored 57.2, significantly outpacing Manual (47.9) and Templated (47.4) approaches.
- LW achieved a score of 54.7, emphasizing its ability to deliver responses that resonate with users.
This visualization highlights how LW’s dynamic emotional scoring and contextual analysis set it apart from traditional methods, leading to more impactful and engaging interactions.
- 45.9% improvement in sentiment for the Engaged segment.
- 56% boost in engagement scores for the Intake segment over templated methods.
- 17% higher lead qualification rate compared to templated methods.
These metrics and visualizations illustrate Leads Wizard’s transformative potential, merging cutting-edge AI with empathetic user design to achieve superior business outcomes.
- Meta-Labeler: An open-source Python-based tool with user-friendly UI for conversation labeling and anonymization. Try it here
- LW Chrome Extension: Simplifies JSON conversation data extraction from Meta Business Suite. More details here
- Development: Conceptualized and prototyped in 12 days.
- Iterative Improvements: Added CoT reasoning and dynamic emotional scoring based on real-world data.
- Adoption: Collaborated with leadership for API integration and multi-team automation. Find the Detailed Timeline here
(Data handling, Privacy and Bias management)
Figure 4 - Interactive Dataset Visualizer - Each circle represents a conversation with a unique ID and attributes. Feel free to interact further: Explore the dataset on Flourish
- The dataset used was from a real-world startup, with 250+ datapoints (conversations with leads).
- In light of maintaining privacy and protecting proprietary information, all data was anonymized and meta-labeled first. Each conversation maintains the unique conversation ID generated during the process of downloading using the extension, and maintains transparency.
- The dataset was downloaded with LW-Chrome-Extension and manually labelled with Meta-Labeler (Both are open-source tools). Despite following strict labelling protocols and blinded cross-testing, it's important to note that manual labeling inherently adds bias to the data. Therefore the metrics above are a pilot scale proof of concept which can easily be automated moving forward to mitigate manual biases.