The Fifth Elephant 2020 edition

On data governance, engineering for data privacy and data science

Predicting Deal Closure in a Sales CRM using Email Sentiment

Submitted by Vishal Gupta (@vizgupta) on May 31, 2020

Status: Submitted

Abstract

Emails are the most common form of communication in a sale and can be used to actively determine the customer’s interest in purchasing a product/service. Statistically, deals with more email replies from the customer are more likely to win. Our project, Deal sentiment at Freshworks as a part of the Freshsales CRM involves predicting sentiment from customers’ and agents’ mails and using it to estimate the probability of the deal winning.

Outline

Cleaning and Parsing emails (Data cleaning)

  • Parsing HTML mails
  • Removing Signature from emails
  • Processing Zoom invites
  • Processing calendar invites
  • Converting emails to conversations

Annotating emails/conversations (Data annotation)

  • Sentiment tagging (-2 to +2) : Need for tagging conversations and why deal outcome cannot be used.
  • Intent tagging : One or more intents tagged from a pool of 50 intents picked by consulting salespersons

Instrumentation

  • Pipeline
    • Ingestion : Kafka consumer, followed by preprocessing and language prediction
    • Population and generation of conversations
    • Sentiment prediction : Get embedding and predict conversation sentiment
    • Use conversation sentiment scores and other features to predict deal sentiment
  • Multi-account models : Using clustering to pick models

Deploying Sentiment (Productionizing data science)

  • Scaling to multiple accounts
  • Clustering customer mails to create buckets

Speaker bio

Machine Learning Engineer, Freshworks

Links

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