With predictive analytics, you can study your organization’s historical data to predict future events; a tactic that’s especially useful for anticipating customer needs with more accuracy than just relying on a hunch.
In this guide, you’ll learn what predictive analytics is, why it’s important, and how to harness the power of predictive analytics to drive growth within your organization.
Why is predictive analytics important right now?
It used to be that data collection existed so humans could analyze and make decisions about it. These days, we’re living in the era of big data, and technology like artificial intelligence (AI) and machine learning (ML) does the heavy lifting for us – even going so far as to take the next step and make decisions or take actions based on analytics.
Today’s customers have higher standards than ever, and understanding their behavior helps you make predictions to better serve them in the future. This is important for improving the customer experience and boosting revenue.
The state of predictive analytics in 2023
From marketers to data scientists, everyone has ambitions for AI-powered predictive analytics. But manual data science is costly, and the sheer volume of data being created, along with variations in how it’s structured and organized, create obstacles for businesses to truly leverage data’s potential.
84% of marketers today say they are still relying heavily on guesswork rather than predictive analytics. But guesswork isn’t good enough, and manually analyzing historical data is laborious. Data professionals, product managers, and engineers have an opportunity to use machine learning to overcome these data obstacles.
Which industries are using predictive analysis?
Chances are you’ll recognize a few of the companies that are utilizing predictive analytics to deliver top-notch customer experiences:
Amazon studies customers’ past buying patterns to create personalized product recommendations for future purchases.
Netflix analyzes the viewing habits of its customers to make recommendations presented to subscribers in the "Because you watched..." list.
Benefits of predictive analysis
There are several benefits to predictive analytics, from improving efficiency in operations to driving more revenue.
Staffing is an ongoing challenge in the hospitality and entertainment industries. Predictive analytics can look at factors like customer influx, holidays, and more to help businesses find the right level of staffing.
Cash flow forecasts
By using historical data from past financial statements and current data from the finance industry as a whole, you’re able to forecast revenue, sales, and expenses to help you plan for the future.
Marketing & targeted communications
Predictive analytics helps you create marketing campaigns that will reach the right audience based on trends from seasonal sales data. It also helps you predict the likelihood a lead will move down the sales funnel.
Learn how Kristin Maa, Senior Vice President or Growth at Saks, uses predictive analytics to identify customers with the potential for the highest lifetime values based on their behavioral data. (It’s a tactic that’s led to both higher engagement and retention rates.)
One of the most common ways predictive analytics is used for risk reduction is with credit scores. When a business or consumer applies for credit, the applicant's credit history data is used to predict their credit risk and determine if they’re a good candidate for a loan or credit line.
Detecting patterns via predictive analytics can identify abnormalities in your data, helping you pinpoint criminal behavior and prevent fraud.
The healthcare industry uses predictive analytics for numerous purposes, including detecting fraudulent claims and identifying at-risk patients for certain diseases. There’s even technology that uses PA to detect allergic reactions faster than humans can.
9 types of predictive models and techniques
Models serve as the building blocks of predictive analytics. Think of them as templates that enable you to take your past and current data and use those insights to drive progress. Here are a few examples.
1. Customer lifetime value model
A customer lifetime value (CLV) model helps you identify customers who are most likely to spend more on products and services.
Learn how to build machine learning models to deliver predictive analytics across your company without writing code, spinning up data pipelines, or needing significant ML experience. In this recipe, we show how through the example of an e-commerce store looking to predict customer lifetime values.
2. Customer segmentation model
A customer segmentation model groups customers together based on traits and shared behaviors.
3. Predictive maintenance model
Predictive maintenance uses data analysis to detect anomalies and defects, so you can fix them before they fail.
4. Quality assurance model
Quality assurance helps you spot and prevent defects to avoid disappointments and extra costs when providing products or services to customers.
5. Decision trees
Decision trees look just how you think they would, with branches and leaves (picture a flow chart, for example). This type of model takes data and puts it into different sections based on certain variables like price, which helps you evaluate and choose between several options or courses of action. Decision trees are a good way to explore varying outcomes.
6. Regression analysis
Regression analysis is the most commonly used model in statistical analysis. It determines patterns in large sets of data, letting you know when there's a linear relationship between the inputs.
7. Principal component analysis
Principal component analysis studies a dataset to determine the most relevant variables. This helps reduce the number of features describing your dataset when building predictive models. This is sometimes necessary because data can be complex, and this process makes it easier to understand without losing information in the process.
8. Ensemble models
Ensemble modeling is the process of creating several models and combining their results to predict an outcome. This improves accuracy by identifying the best model to use with new data, reducing the need to generalize a prediction.
9. Neural networks
A neural network is a modeling technique that imitates the way humans think. Neural networks can deal with complex data relationships using AI and pattern recognition, and they work well when there's no known mathematical formula.
Drive tomorrow’s profit with Segment
A customer data platform (CDP) like Twilio Segment combines your customer data from each touchpoint, whether it’s social media, your website, or in-store locations to provide a single, unified view of your customers.
Collect data analytics from each touchpoint
The more data you have access to, the more accurate forecasts you can make for future decisions. Connections collects data from every touchpoint, giving you a full view of your customers and optimizing your analytics.
Data-first customer engagement improves CLV
Twilio Engage is an omnichannel marketing automation and customer engagement platform that helps you deliver data-driven personalization and drive growth.
Using Segment’s Predictive Traits feature, teams can predict the likelihood of a user performing an event (e.g., their propensity to make a purchase, a referral, or their lifetime value). These Predictive Traits can be added to a user profile and then used to build out highly specific audiences or trigger an action via Journeys (e.g., sending a promo code when they show a high propensity to buy).
Accurate data leads to better predictions
Twilio Segment’s Protocols feature ensures your data is clean, so the forecasts you make are based on accurate data.
Real-time data provides more personalized experiences
Unify provides access to real-time customer data across every platform and channel, giving a 360-degree view of the customer journey and providing the ability to personalize customer experiences at scale.