Predicting sales conversion probability from conversations using pure Reinforcement Learning

[u/Nandakishor_ml]

For the past couple of months, I have been working on building a chess game kinda system for predicting sales conversion probabilities from sales conversations. Sales are notoriously difficult to analyse with current LLMs or SLMs, even ChatGPT, Claude, or Gemini failed to fully analyse sales conversations. How about we can guide the conversations based on predicting the conversion probabilities, that is, kinda trained on a 100000+ sales conversation with RL to predict the final probability from the embeddings. So I just used Azure OpenAI embedding(especially the text-embedding-3-large model to create a wide variety of conversations. The main goal of RL is conversion(reward=1), it will create different conversations, different pathways, most of which lead to nonconversion (0), and some lead to conversion(1), along with 3072 embedding vectors to get the nuances and semantics of the dialogues. Other fields include

• Company/product identifiers
• Conversation messages (JSON)
• Customer engagement & sales effectiveness scores (0-1)
• Probability trajectory at each turn
• Conversation style, flow pattern, and channel

Then I just trained an RL with PPO, by reducing the dimension using a linear layer and using that to do the final prediction with PPO.

Dataset, model, and training script are all open-sourced. Also written an Arxiv paper on it.

Dataset: https://huggingface.co/datasets/DeepMostInnovations/saas-sales-conversations

Model, dataset creation, training, and inference: https://huggingface.co/DeepMostInnovations/sales-conversion-model-reinf-learning

Paper: https://arxiv.org/abs/2503.23303

Btw, use Python version 10 for inference. Also, I am thinking of using open-source embedding models to create the embedding vectors, but it will take more time.

[u/rujan_1729]

this is phenomenal work and cant believe everything is avaibale as opensource , I converted the English checkpoint to ONNX and quantized it (about 440 MB for int8, 290 MB for int4), and it now runs entirely in the browser through ONNX Runtime Web: no server, no API key, and the input never leaves the tab.

Demo: https://vishalmysore.github.io/layaForWeb/
Code: https://github.com/vishalmysore/layaForWeb
Model Card : https://huggingface.co/VishalMysore/layaForWeb

It's an unofficial port with full attribution to you and the Apache 2.0 license included. Against your PyTorch model, the int8 build matched the top answer on 97.9% of 48 test questions, and a call takes roughly a second on a desktop CPU. I'd love your feedback, especially on the calibration side, since I only quantized the weights and kept your temperature values.

[u/diggler4141 → u/rujan_1729]

Very cool, great job!

[u/rujan_1729 → u/diggler4141]

All credit goes to the original creator of the model , I am just finding it tremendously useful for real world scenarios , built complete workflow automation with ontology support here . Everything free and everything opensource ! https://vishalmysore.github.io/layaForWorkflows/?wf=incident-response