Predictive analytics guide: How to make it work for you

Predictive analytics keeps surprising us — in the best way. More and more companies are leaning into it, not just to stay competitive, but to meet the growing demand for faster, smarter decisions. We’ve seen teams use it to predict future sales or personalize the customer experience, and help their stakeholders make more confident decisions. In this post, we’ll share some of our favorite real-world examples and what you can take away from them.

What is predictive analytics?

Predictive analytics uses data analytics, statistical modeling, and artificial intelligence to predict future events. Retailers, like Huckberry, for example, can use predictive models to analyze the past five years of summer sales and estimate the demand for the upcoming summer. 

How does this help? If the model predicts strong sales, you don’t need to run a clearance sale to sit on profits. If the inverse is true, you should be generous with discounts to keep sales moving. 

Maybe you’ve been using business intelligence for some of these decisions. While traditional business intelligence tools are good at examining what has happened in the past, predictive analytics (and tools like Hex) answer the question, “What will happen next?”

It leverages historical and real-time data to forecast the future, enabling proactive decision-making.

The different types of predictive analysis models

Different types of data require different statistical techniques. In this section, we’ll explore the main machine learning models behind predictive analytics, plus guidance on when each one fits best.

Classification

Classification models learn from categorical data and predict which group a new data point falls into. The outcome is always a categorical variable. For instance, in healthcare, classification models determine whether or not a patient has a disease based on medical data. 

Tip: These work best for “yes” or “no” questions, like fraud detection (whether a credit transaction is fraudulent or not) or customer churn. They’re also useful for product recommendations — predicting whether a customer is more likely to buy a Mercedes, Tesla, or BMW. 

Want a detailed guide on applying classification models? Here is a complete tutorial for using classification models to determine your customer tone: positive, negative, or neutral.

Time series

Time series data is a sequence of data points collected at regular intervals. Time series models learn from this data to identify trends, cycles, and seasonality. They can also predict an outcome over a given period — for example, weekly sales over the next quarter or daily average temperature. 

Tip: When time is an important factor, such as analyzing website traffic over the last six months, time series forecasting models take the lead.

Regression

When your data contains outcomes and the factors influencing them (independent variables), a regression model is trained to find a strong relationship between them. Once trained, the model uses that relationship to predict the outcomes for unseen data. 

Regression analysis always gives a number as the output. 

Tip: Use regression models to predict continuous variables, like a person's income. The input variables can include the person's profession, experience, education, industry, and skills. 

Clustering

Clustering aggregates data points that share similar attributes into a single group. For example, you can group customers based on their reviews and tailor discounts accordingly, offering generous discounts to dissatisfied customers to win them back. 

Tip: Clustering is useful when you want to group similar data points and make decisions at that level. Want to get hands-on? Check out our guide to master the most effective clustering techniques.

Neural networks

Neural networks learn data patterns similarly to how our brain processes information. Much like human brain cells, neural networks have interconnected artificial neurons that learn and improve on their own. 

Tip: This machine learning technique is better suited for complex datasets. It’s widely used in image and speech recognition. 

Common and emerging use cases for predictive analytics

Predictive analytics is transforming every industry. Any domain with enough relevant data can use it to set itself ahead. Here’s how predictive analytics has created waves in a few industries and how companies have utilized it. 

Retail

The e-commerce giant Amazon drives 35% of its sales through its recommendation algorithm, which is built using predictive analytics techniques. It personalizes the online store for each customer based on their browsing history and buying patterns. 

The algorithm groups customers based on their previous orders and displays similar products and offers to those in the same group — a proven use case of clustering (grouping customers that share similar interests). 

Customer retention

Research shows that acquiring a new customer costs more — four to five times more, on average — than retaining an existing one. Churn prediction models are essential for forecasting which customers will likely discontinue purchasing your product or cancel a subscription. Since the goal is to determine whether a customer will stay or leave, classification models are commonly used for churn prediction.

ClickUp put this into action by turning a robust churn model into a multi-dimensional data app with Hex. Stakeholders could dig deep into the model and data segments with this self-service platform and proactively shape customer retention marketing strategies. ClickUp has saved more than $1M with this churn model-turned-data app.

Health prediction 

Predictive models run on data collected from electronic health records, laboratory tests, intelligent medical devices, and patient surveys to predict potential health risks. 

LLR ICS, a healthcare organization, transformed healthcare funding with a predictive model powered by patient-level data and the Johns Hopkins ACG System. This new model allocates resources based on the region, patient-to-population ratio, illness percentage, and other factors. 

The impact? A 10.7% funding increase for an underserved practice that previously received insufficient funds.

HR 

Predictive HR analytics forecasts employees’ performance, team outcomes, and workforce needs. HR teams can use these forecasts to make better hiring, training, and compensation decisions. 

A trending use case? Navigating post-pandemic shifts in how and where people work. Companies can use data to forecast:

• Employee productivity in remote vs hybrid roles

• Cost comparisons of remote and on-site business operations

• ROI of an on-site office setup

Predictive analytics spans numerous industries. Supply chain and logistics can benefit from AI-powered delivery route optimization. Credit card companies can detect and suspend fraudulent transactions in real time. The Zebra auto insurance comparison site has enriched its analytics practices with less manual lift, using Hex and Eppo to help its customers find the right insurance plan using data.

The secret sauce behind successful predictive analytics

We’ve covered a few types of predictive analytics models and where you might see them out in the world. Now, let’s look under the hood at some key factors that set effective predictive models apart and boost your chances of success. 

Data quality 

Businesses benefit most from accurate, reliable, consistent, and complete data sets. But, you cannot ignore hidden bias in data. 

Say your data contains 98% legitimate transactions and only 2% fraud cases. Your model will have little exposure to fraudulent data, making it difficult to detect suspicious transactions in the future. So, the takeaway is to ensure quality from all different angles. 

Model interpretability

Model interpretability impacts how easily anyone can understand a model’s mechanism in predicting future outcomes. High model interpretability allows non-technical teams to understand a model’s functionality and confidently apply its insights.  

Why does this matter? With enterprises actively adopting gen AI, model interpretability is needed more than ever. It explains to stakeholders how the technology works in simple terms, building trust and driving smoother adoption.

Technical infrastructure

Data processing requires computational resources, especially when the dataset is large. We recommend cloud resources over on-premises since they scale on demand and follow a pay-per-use pricing model. 

Plus, integrating multiple data sources into a cloud warehouse is often seamless with pre-built APIs or connectors. Hex partners with various cloud platforms so its users can reap the benefits of the cloud.

Continuous monitoring

People are constantly evolving, and so is data. Continuously monitoring key metrics and improving your predictive models to suit recent data patterns preserves your competitive edge. 

While we’re sure some of your KPI dashboards will be unique to your org, some common metrics worth tracking include customer experience, ROI of marketing campaigns, revenue growth, and net profit margin.

How to get started with predictive analytics

If you’re ready to implement predictive analytics, here’s an approach we recommend: 

1. Define your business objectives

Predictive models are built to optimize business processes. Align them with the company's goals to drive real impact. For example, if your business aims to increase cross-selling by 20%, build models that identify customer behavior and personalize marketing campaigns. 

A great way to find these opportunities is by revisiting key business questions. Pay close attention in meetings where company performance is reviewed — especially those quarterly reports that break down wins and losses. The "losses" section is a goldmine for analytics use cases. Pick a problem and ask: “How can predictive analytics help solve this?”

2. Assess data readiness

After defining your goal, the next step is to collect relevant information. That means integrating data from various business tools. For instance, if you’re trying to increase customer retention, pull CRM data, sales history, and customer survey documents into your analytics workspace.

It's not just about having data — you need enough data to train accurate models. Establish solid ETL pipelines that regularly collect, clean, and store data for easy access. To ensure quality, implement integrity checks at every stage of your data journey to address missing values, duplicates, incomplete records, and biased data.

Be sure not to overlook security and governance. Your data infrastructure should comply with privacy regulations like GDPR and CCPA. Set up the correct access control permissions so the right people have access to the right data. All these measures prepare your data for modeling. 

3. Choose the appropriate tools

Select the appropriate predictive analytics tools for your data, use cases, and desired output. Look for these features:

• Intuitive UI: Notebook-like cells to simplify creating data analysis and prediction workflows

• Live data connections: Real-time access to databases and warehouses 

• No-code facilities: Drag-and-drop and visual elements for business users to explore data on their own

• Secure collaboration: Controlled data access and easy sharing capabilities

Fortunately, you can find all of these features (and more) in Hex’s unified workspace, where you can use SQL, Python, and AI to analyze data and securely collaborate, all in one place.

4. Select your model

Choosing the right model for the right data is always a game-changer. Here’s a quick recap of some of the tools we’ve introduced so far and some additional selection criteria to guide your search:

• Forecasting future trends over time? Use time series models. These models capture trends and seasonality (like holiday sales spikes). Go with ARIMA or Exponential Smoothing for short-term forecasts. For longer horizons, deep learning models like LSTM work well.

• Need to predict categories? Use classification models. Perfect for “yes/no” questions or multi-class labels. Logistic regression and decision trees are popular choices here.

• Analyzing text or images? Use neural networks. Their interconnected neurons handle high-dimensional data well, making them great for sentiment analysis or image recognition.

Overall, consider factors like available resources, costs, and speed. Deep learning models are resource-intensive and are often costlier than traditional ML models. 

Bonus tip: No matter which model you select, try to avoid biting off more than your data and business teams can chew — using complex models unnecessarily can overwhelm your teams and make adoption difficult. Keep interpretability in mind. 

5. Experiment and iterate 

Predictive analytics models are never perfect on the first try. Evaluate how your model performs using metrics like accuracy, precision, recall, and AOC-ROC curves. Here’s a sample iterative approach to improve your results:

• Refine inputs: Revert back to your EDA workflow and extract more meaningful features, reduce dimensionality, and standardize the data if needed. 

• Hyperparameter tuning: Find the optimal set of values for an algorithm’s input parameters using techniques like grid search or randomized search. 

• Try different algorithms: If models are too simple, try advanced techniques from deep learning or ensemble algorithms and compare performance.

Maximize your ROI with predictive analytics

If you want actionable business insights, you need predictive analytics, not just traditional business intelligence. With the right multi-modal tools and interactive data apps, your data scientists can empower the business teams in your org to leverage insights from predictive models — all while developing and refining those models within the same platform.

Request a demo with us to learn more about how Hex can supercharge your predictive analytics goals, much like ClickUp — which used predictive analytics to prevent churn and saved $1M.