Overview

Overview #

What We Are Solving For #

In the B2B landscape, understanding and retaining customers is crucial for long-term success. Churn—when a customer stops doing business with a company—can significantly impact revenue and growth. Traditional methods of analyzing churn risk often fall short because they rely solely on structured data, missing the nuanced insights that can be derived from unstructured data such as customer conversations and interactions.

Our domain focuses on providing a comprehensive solution to predict and mitigate churn risk by leveraging both structured and unstructured data. This enables businesses to gain a deeper understanding of customer behavior, sentiment, and overall health, leading to more effective retention strategies.

Overview of the Product #

Data Sources #

Our product collects data from a variety of sources to provide a holistic view of customer interactions and behavior:

  • Structured Data:

    • Customer demographics
    • Transaction history
    • Financial data
    • Business intelligence data (e.g., funding rounds, location, employee growth rates)
    • Data from CRMs, support systems, and marketing platforms

  • Unstructured Data:

    • Conversations (support tickets, call transcripts, meeting notes, chats)
    • Social media posts
    • Surveys (NPS, CSAT)
    • Reviews

Types of Data #

  • Categorical Data: Attributes like customer type, industry, region
  • Numerical Data: Metrics like transaction amounts, interaction frequencies
  • Textual Data: Content from conversations, social media, and surveys
  • Multimedia Data: Tone of voice, gestures, and facial cues from recordings

Two Main Parts of Our Product #

Our product is primarily divided into two main components: Journey Analysis and Churn Prediction.

1. Journey Analysis #

Overview: Journey Analysis focuses on mapping and understanding the customer’s journey through various touchpoints with the business. This includes pre-sales, sales, onboarding, usage, and support interactions.

Key Features:

  • Interaction Mapping: Tracks all customer interactions across different channels (email, phone, chat, in-person meetings).
  • Sentiment Analysis: Gauges customer sentiment at various stages of the journey using advanced NLP techniques.
  • Key Event Identification: Identifies critical events (e.g., first purchase, feature usage, support ticket creation) that impact the customer journey.
  • Visualization: Provides visual representations of the customer journey, highlighting key interactions and sentiment trends.

Benefits:

  • Helps businesses understand where customers are experiencing friction or satisfaction.
  • Identifies opportunities for intervention and improvement in the customer journey.

2. Churn Prediction #

Overview: Churn Prediction uses machine learning models to forecast the likelihood of a customer churning. It integrates both structured and unstructured data to provide accurate and actionable insights.

Key Features:

  • Data Integration: Combines structured data (demographics, transaction history) with unstructured data (conversations, social media).
  • Machine Learning Models: Utilizes algorithms like logistic regression, random forest, and neural networks to predict churn.
  • Feature Engineering: Creates meaningful features from raw data, such as interaction frequency, sentiment scores, and key event occurrences.
  • Real-Time Predictions: Provides up-to-date churn risk scores for customers.
  • Actionable Insights: Offers recommendations and tasks for intervention (e.g., follow-up calls, targeted marketing campaigns).

Benefits:

  • Enables proactive measures to retain at-risk customers.
  • Helps in resource allocation by identifying high-risk accounts.
  • Continuously improves prediction accuracy through feedback loops and model retraining.

Detailed Breakdown of Each Component #

Journey Analysis #

Data Collection:

  • Gathers data from CRMs, support systems, marketing platforms, and customer interactions.
  • Extracts textual data from conversations, emails, and social media.

Processing:

  • Preprocesses data to calculate summary statistics and handle missing values.
  • Uses NLP techniques to analyze sentiment and identify key events.

Output:

  • Provides a visual map of the customer journey.
  • Highlights sentiment trends and critical interaction points.

Churn Prediction #

Data Collection:

  • Collects both structured (demographics, transaction data) and unstructured data (conversations, surveys).

Data Processing:

  • Cleans and preprocesses data, including handling outliers and ensuring consistency.
  • Engineers features like interaction frequency, sentiment scores, and key event occurrences.

Model Training:

  • Selects appropriate machine learning algorithms.
  • Performs hyperparameter tuning and cross-validation to optimize model performance.
  • Evaluates models using metrics like accuracy, precision, recall, and AUC-ROC.

Prediction Generation:

  • Generates churn risk scores using ensemble models and unstructured data analysis.
  • Calculates a composite score that combines structured and unstructured insights.

User Interface and Automation:

  • Provides a user-friendly interface for viewing predictions and insights.
  • Automates tasks for intervention based on churn predictions (e.g., scheduling meetings, sending emails).

Continuous Improvement:

  • Refines models based on real-world outcomes and user feedback.
  • Adapts to changing customer behaviors and preferences over time.

Conclusion #

Our product offers a comprehensive solution for understanding and predicting customer churn by integrating structured and unstructured data. Through Journey Analysis and Churn Prediction, businesses can gain deep insights into customer behavior, sentiment, and risk, enabling them to take proactive measures to enhance customer retention and drive growth.