Building A GPT-3 Twitter Sentiment Analysis Product
We’re going to walk through building a production level twitter sentiment analysis classifier using GPT-3 with the popular tweet dataset Sentiment140.
A painful reality of doing business is that customers leave.
Regardless of how successful your company is or how relevant your products or services are, you’ll invariably lose customers to your competition.
For that reason, companies have customer retention and loyalty programs to actively deal with risk of churning. In every industry or product segment from telecom to SaaS companies and consumer products, churn management has a direct impact on profitability.
Depending on the sources you look up, and what industry you’re in, the cost of acquiring a new customer is anywhere between five to 25 times higher than retaining an existing customer. Frederick Reichheld, the inventor of the net promoter score and author of “Loyalty Rules! How Today’s Leaders Build Lasting Relationships” found that a mere 5% increase in customer retention increases profits by 25% to 95%.
Understanding what causes customers to jump ship is crucial to building a sustainable business. We’ll explore how businesses can use machine learning to build a churn prediction model to improve top- and bottom-line growth. But before we dive into predicting customer churn, let’s take a quick look at what it actually is.
Customer churn rate is a business metric that represents the percentage of customers who terminate their relationship with a company in a particular period of time.
This time frame could be measured on a monthly, quarterly, or an annual basis, depending on the industry and product. Subscription-based companies (think mobile service providers, SaaS, and content platforms) typically measure churn over shorter periods of time.
Customer churn is also indicative of business health. While there are multiple reasons why customers may defect, some of the most common reasons include poor service or product quality, price, and other macroeconomic factors like a recession.
The ability to predict churn is key to preventing it. And that’s where machine learning comes in. Organizations relying solely on customer feedback for churn prediction often overlook other variables influencing churn.
With the amount of data available to companies today, developing machine learning (ML) models for churn prediction is much easier. Artificial intelligence (AI) or ML-driven churn prediction is more accurate than any other prediction models available today.
Companies today have a vast repository of data on how their customers interact with their product or services. From CRM systems to website analytics and social engagement, companies have multiple data sources that can provide valuable insights into churn rate.
Provided the right datasets, machine learning algorithms can help companies identify underlying behavioral patterns that customers who leave have in common. The algorithms can then be applied to existing customers to detect similar customer behaviors and churn indicators.
For instance, a mobile service provider that’s looking to predict churn can tap into historical customer data to identify which customers terminated service or reduced their monthly billing plans. The company can then use this data to train a machine learning model to compare behavioral traits between churners and non-churners.
The ML model will look at attributes like residence state, customer lifetime, active plans, daily calls, daily data consumption, monthly plans/billing amount, and number of customer service calls to determine the likelihood for churn.
It’s clear that historical data is a prerequisite to building a customer churn prediction model. However, in addition to data, there are several other factors that will also determine how you build your churn prediction algorithm. Here are the steps to creating it.
This step is simply understanding your desired outcome from the ML algorithm. In this case, the final objective is:
The next step is data collection — understanding what data sources will fuel your churn prediction model. Companies capture customer data across their lifecycle through software such as CRM, web analytics, sentiment analysis tools, social listening tools, customer service software, and more.
Building data capture services is one of the easiest and most effective ways to begin collecting data to power your churn prediction model. Turnkey solutions like automated data capture (ADC) can help you leverage your existing software to speed up relevant data collection and apply it to your churn prediction model. ADC eliminates the manual efforts required for data entry and frees up time for your data team to fine-tune your prediction model.
A big step of data preparation is transforming all this raw information into structured data to enable your ML algorithm to process it. Upload this data to a data analysis and manipulation tool to get a structured dataset for further predictive analytics and visualization.
Feature engineering is a crucial part of the dataset preparation — it helps determine the attributes that represent behavior patterns related to customer interaction with a product or service. Data scientists use feature engineering to assign measurable characteristics to data points that an ML model will process to predict churn probability.
These features could include customer demographics, behaviors (in the mobile phone example, these could be data consumption, calling customer service, using international roaming, etc.), and contextual features that describe other information about a customer like communication preferences, past buying behavior, or birthdays/anniversaries.
Next, feature extraction standardizes the variables (attributes) by only isolating the ones that contain meaningful information in context of the business case (churn). Feature extraction limits data dimensionality (columns representing attributes in a dataset) and only retains helpful data for the business case.
Feature selection refers to a data science technique that identifies previously extracted features and selects subgroups that most closely influence the target variable (churn). This leads to a data set that contains only the most relevant information on attributes that influence churn.
Data analysts typically approach churn prediction using multiple methods such as binary classification, logistic regression, decision trees, random forest, and others.
ML algorithms perform binary classification to slot the attributes of a target variable into two groups on the basis of a classification rule. In this context, the target variable is churn, the outcome of which can be classified as true or false. Binary classification helps us understand which customers churned and which ones stayed on.
Based on this information, data scientists can then run regression analysis to determine the relationship between the target variable (churn) and other data points that influence churn (monthly plan, data consumption, service calls, etc.), in weighted values.
This will provide information on whether variables have a positive or negative relationship with churn. A positive relationship indicates a higher probability for customers to leave and a negative relationship means that customers are less likely to churn.
A decision tree is yet another effective training model for churn prediction. The decision tree model uses available features and splits the data based on features values to provide unique resulting groups. Here’s a simple example of a decision tree:
Now, depending on the size of the dataset and the diversity of feature data, you may choose to use multiple decision trees or a Random Forest.
A Random Forest is a collection of multiple decision trees, where each individual tree splits out a classification. These classifications are binary in nature, so whichever classification receives the most number of votes, wins. So, if your Random Forest consists of five decision trees, and three of those provide the same classification, your final prediction will be determined by the majority.
Once you have developed the model, it needs to be integrated with existing software or serve as the base for a new program or application. You’ll need to pay close attention to the model’s accuracy and performance.
Testing and monitoring model performance to adjust features will help improve the model’s accuracy. From our mobile services example, monitoring and testing could mean logging customer interactions and reviews.
The most important factor in addressing churn is developing a churn prediction model. The model not only tells you who your at-risk customers are but also offers insights into reasons they will leave. For marketers and customer experience leaders, this is the holy grail to fixing the leaky bucket problem — discovering the underlying reasons for customer attrition.
Customer retention boils down to the ability of a company to analyze and predict the motivations behind churn and most importantly, acting on them. The larger your customer base, the higher the impact of churn.
Want to improve your revenue outcomes with churn prediction? Interested in tapping into missed data opportunities that drive business growth? Let’s talk about how we can use machine learning to deliver accurate churn predictions and design the right interventions at the right time.
Contact us to learn more.
We’re going to walk through building a production level twitter sentiment analysis classifier using GPT-3 with the popular tweet dataset Sentiment140.
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