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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to Create Engaging Customer Experiences with GenAI?

Devashish Mamgain
Devashish Mamgain 30 Jul, 2024
4 min read

Introduction

While customers have always preferred brands that prioritize an excellent customer experience, it’s only in recent years that it’s become a standard for every business. Customers are now used to it and expect better experiences. Over 71% want personalized services, and around 60% will abandon a brand that doesn’t provide them with these experiences.

Thankfully, this attitude shift has come with (and because of) an unprecedented technological advancement. With modern AI/ML techniques and Generative AI, businesses can deliver engaging and dynamic customer experiences at scale. Because of the nature of customer engagement, most of the focus has been on generative AI chatbots.

However, since the technological rise has been rapid, pinpointing the areas where you should invest is challenging. So, let’s create a plan with the basics of customer experience.

Engaging Customer Experiences with Generative AI

Table of Contents

Using the 4 R’s of Customer Experience

If you’re starting to leverage Generative AI chatbots for your business, it’s essential to understand how you can use them. The key to understanding this is through the 4 R’s of Customer Experience.

  1. Recognize: First, recognize the areas where your customer experience is lacking. For example, if they need easy access to certain services, it will be prudent to program these services into your Generative AI chatbot.
  2. Request: Secondly, while doing customer research, carry out an audit of common customer service complaints. Most enterprises use AI to address these common complaints at scale. Proper documents around standard practices and introductory videos about troubleshooting processes will be immensely helpful when you train your AI.
  3. Respond: When you have a core idea around the experience you want to create, start noting down your best-rated responses for them. This could be anything from replies that solved previous issues to videos that customers have enjoyed.
  4. Repeat: While the first three steps are enough to get you started, repeating the process periodically will help you delight customers every time. Keep refining your process and recognizing gaps in the customer experience that AI can address, and you’ll create a state-of-the-art customer experience.

This iterative practice lets you understand the customer demands unique to your business. Further, the media and documents you create become the material that guides the training and deployment of your chatbots.

In the following section, let’s discuss how AI chatbots are being used to create engaging customer experiences.

Also Read: The Psychology of Human-AI Collaboration in Customer Service Teams

Creating Engaging Customer Experience with Generative AI

A lot of businesses are currently investing in AI chatbots. The most common use cases we’ve seen are as follows:

24/7/365 Customer Care

Ai-based customer service got its fame because of its capability to provide 24/7 availability at scale at a low cost. This works because AI customer support can function without any human intervention and is only limited by your servers’ bandwidth.

Customization at Scale

One of the leading theories of multinational marketing rests on the idea of “Noon Nopi,” or that you must meet your customers at the “eye level.” AI chatbots are experts at these because they can be multilingual and understand cultural contexts better.

Create Engaging Forms and Surveys

Our experience says that many customers hate filling out forms and surveys. This is a hindrance to marketing efforts. However, AI chatbots create better, more interactive form fill-up experiences and can engage users in filling out more forms.

Generate Personalized Content

Every generative AI chatbot can personalize at scale because of its capability to create content. Generative AI can create context-aware and data-accurate personalized content for customers and help them solve their problems quickly.

Contextual Conversations

When IVRs were common, customers often had to re-explain their issues whenever their calls were rerouted. AI avoids this by remembering the context of a conversation for longer and giving better, contextual answers to your customers at every step.

Additionally, you can use AI to get customer feedback and create information-rich marketing campaigns (by adding a chatbot to the target landing page).

Generative AI chatbots are a great complement to existing business use cases. They can be leveraged to augment customer experiences at scale while increasing the productivity of your current customer support and sales agents.

Also Read: AI for Customer Service | Top 10 Use Cases

Conclusion

More and more enterprises are adopting generative AI chatbots to deliver better customer experiences. However, it’s crucial to strategize before using these chatbots. We recommend the 4R method, where you iteratively recognize requests and respond to them to understand customer expectations.

Companies that use this strategy harness the power of generative AI creatively to drive customer satisfaction. They leverage AI’s 24/7 availability and multilingual capabilities to approach new markets. Additionally, they create customizations and personalization for their customers at scale, engaging them with new and improved content.

In our experience, this strategy and these capabilities have driven massive results for multiple enterprises. And we predict that generative AI will be a key driver of customer delight in the future as well.

Frequently Asked Questions

Q1. Can Generative AI Improve Customer Experience?

A. Yes! Generative AI can analyze customer conversations to provide timely and personalized solutions. It is also available 24/7 and can be trained to provide proactive support.

Q2. Can Generative AI Replace Human Customer Support Agents?

A. AI can’t handle the complex tasks that a human agent does. We recommend that AI supplement human customer support agents by automating and filtering out repetitive queries so that they can focus on critical issues instead. We also recommend a “human in the loop” system where a chatbot hands off the chat to a human when there’s a problem it can’t solve.

Q3. How do I Build an AI Chatbot for Customer Support?

A. First, identify the gaps in customer experience that you want to fill using an AI chatbot. Once you’ve determined that, build a chatbot that answers repetitive questions. Take periodic feedback and train the chatbot to improve customer experience over time. You can train and deploy your chatbots without coding using third-party providers like Kommunicate.

Q4. Will Generative AI Protect My Customer’s Privacy?

A. Yes, modern, enterprise-grade Generative AI is built to protect customer privacy. This is enabled by using methods like RAG, differential privacy, and others. If you’re unsure about a Generative AI provider, look for certificates like SOC2, GDPR, and HIPAA to confirm if they maintain data confidentiality at scale.

Q5. How do I Ensure that my Chatbot is Providing Accurate Data?

A. Your AI chatbot will only learn from the data you provide. Keep your data up-to-date and ensure correctness by testing your chatbot periodically. We also recommend keeping a human agent in the loop so that you can answer the more critical questions that AI cannot handle.

AIai chatbotBeginnerbusinesscustomer experiencecustomersGenerative AImarketing
Devashish Mamgain
Devashish Mamgain 30 Jul, 2024

Devashish is the CEO & Co-Founder of Kommunicate, with 15+ years of experience in building exceptional AI and chat-based products. Devashish believes the future is humans and bots working together and complementing each other.

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