Gazing into crystal balls is fun, but we know it isn’t serious. After all, it isn’t a science. But predictive analytics is, and this discipline drives academic research and enterprise across domains, helping businesses stay agile.

Whether you’re a student eager to explore this exciting field or a business professional seeking insights, this article is for you. By the end, you’ll have a better understanding of the science behind the magic of business forecasting and the BI tools that make it happen.

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Roadmap

• What Is Predictive Analytics?
• Benefits
• Role of Big Data
• How It Works
• Use Cases
• Features To Consider
• Future Trends
• Next Steps

Imagine you’re a chef running a popular restaurant. Every day, you cook up delicious dishes and hope people will flock in. But some days, you end up with leftovers, disappointed customers and wasted ingredients.

Wouldn’t it be great to know how many people will show up, what they’ll order and how much to prepare beforehand?

That’s where predictive analytics comes in. Looking at past data — the number of orders, popular dishes and busy days can help you anticipate customer footfall.

It’s not magic, but it’s pretty close!

What Is Predictive Analytics?

Predictive analytics is a branch of computer science for making educated guesses about the future using the available data. The technology behind it involves statistical data models, algorithms and machine learning.

If these terms seem daunting, hang in there. For now, know this — these techniques help predict values for new, unseen data using past data.

Think of predictive analytics as a smart recipe book. Aside from listing ingredients and steps, it helps you adjust the recipe based on the day, the weather and special events. Additionally, you’ll build on experience.

On Fridays, people love fish tacos, so you prepare extra. Or if it’s raining, you make more comforting stews. This way, you avoid the leftover blues and keep your customers happy.

Primary Benefits

Now that you know what predictive analytics is, let’s consider its business benefits.

Gain a Competitive Advantage

You can establish your brand identity by differentiating your offerings with predictive analytics. In sync with market trends, you can determine which products to launch and when.

Will print ads or social media work better? Scenario simulation can help you decide upon the best outreach channel without spending on expensive marketing campaigns right off the bat.

Customer insights are gold for improving services and gaining loyal customers and excellent reviews — earned media that garners more publicity for your brand.

Don’t just react to trends, be the trendsetter. Develop innovative products and services — use deep learning to analyze vast data volumes, identifying emerging consumer preferences, technological advancements and societal shifts.

Maximize ROI

Several external factors can influence demand and shape dynamic pricing strategies. It could be the time of day, day of the week, holidays, special events, weather conditions and local traffic patterns.

Bundling slow-moving products with top sellers can boost sales, but only if the products align with market demand. Predicting demand peaks and valleys helps online retailers, airlines and hotels position their offerings.

Anticipating trends can help you plan product development, marketing campaigns and operations.

Boost Efficiency

Modern data tools provide the complete picture by combining data from many departments — marketing, sales, operations and finance.

Silos crumble as teams huddle around shared insights, discovering common goals and forging a united roadmap. It creates a ripple effect, maximizing efficiency and minimizing waste.

Cross-functional teamwork becomes a reality — marketing works with sales to target leads while operations fine-tune production.

Discussing and strategizing on shared insights leads to better decision-making and a unified organizational outlook.

Anticipating challenges, adapting to market shifts and innovating seem within reach with data-driven collaboration.

Control Costs

Plan where to allocate resources by anticipating future business needs and potential challenges. With timely risk assessment, you can ensure smooth sailing even in choppy waters and pivot in the face of change.

Inventory management, a primary cost center, benefits greatly from demand forecasts. You can plan procurement better when you know what you need and how much.

Purchase recommendations help reduce inventory holding costs and boost sales. Enhancing the customer experience is a definite plus.

Preventive maintenance is another cost-effective benefit of business forecasting. You can extend the lifespan of machines by planning repairs after tracking performance and wear and tear.

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Key Components

Data Models

They enable mapping the relationships and patterns within data, unlocking hidden insights. Data models are of many types — relational, dimensional, document, graph and hierarchical.

Some elements of data models might follow industry best practices or established modeling principles. But, the specific design and details of a data model will be specific to your business needs and how you plan to use the data.

But with so many types of models around, choosing the right one can feel like picking a route in a bustling metropolis.

Let’s delve into some key types and see how they can guide you:

• Classification Models: They group objects into distinct categories for target marketing or curating personalized experiences. Imagine sorting customers into loyal segments or classifying website visitors as potential buyers.
• Regression Models: They delve into your data to identify relationships between variables and predict future outcomes. Are sales affected by weather patterns? Will housing prices rise in a specific area? Regression models unveil these connections, allowing you to forecast revenue, optimize resources or make informed investment decisions.
• Clustering Models: They automatically group similar points, revealing distinct customer segments with shared characteristics. It allows you to personalize marketing strategies and fine-tune recommendations.
• Time Series Models: They track data changes, like daily website traffic or monthly sales figures, to predict future trends and patterns.

By identifying seasonal fluctuations or anticipating peak demand, time series models equip you to optimize inventory, schedule maintenance or ride the waves of market cycles.

Choosing the Right Model

Picking the optimal model is like selecting the perfect travel mode. Consider your destination (desired prediction) and the road conditions (data characteristics).

Simple models, like regression, offer clear interpretations but might not handle complex relationships. Deep learning, while powerful, can be opaque and require hefty data volumes.

Ultimately, the best model balances accuracy with interpretability, matching the data with your objectives.

Predictive Algorithms

If data is the ingredient, an algorithm is a chef. They’re the instructions that put the data to work, enabling a peek into the future or educated guesses about what might happen next.

These algorithms use mathematical formulas and calculations to determine data associations. They’re self-learning, which is great, except that inherent biases can corrupt results, so high-quality data is a must for accuracy.

Types

• Linear Regression: It models the linear relationship between a dependent variable and one or more independent variables and generates estimation and revenue forecasts.

• Logistic Regression: Predicts the probability of a binary outcome — yes/no and churn/stay. Is customer churn expected? What’s the probability of credit card fraud?
• K-Means Clustering: Imagine you have a messy room full of toys, books and clothes. K-means clustering is like a magical helper who groups your belongings into neat piles based on their similarities. It supports market segmentation, document clustering and effective treatment by grouping patients with similar symptoms.

• ARIMA: It aims to achieve stationary values across time series data while factoring in correlation, seasonality, trends and moving averages (values prone to changes over time). Think back to our cooking analogy. Today’s menu is the stationary data we want to curate.
  • You can predict what your guests might like today based on yesterday’s popular dishes and leftovers (autoregression).
  • You incorporate seasonal trends into your calculations, for instance, pumpkin pie in the fall season.
  • You adjust today’s menu to avoid a repeat (moving average) of the pizza dough running out the previous night.

Forecasting sales, energy demand and inventory levels are some use cases of the ARIMA algorithm.

• Neural Networks (Deep Learning): These models employ layers of interconnected nodes that learn from vast amounts of data, often surpassing traditional models in accuracy. They enable image recognition, natural language processing and sentiment analysis.

Choosing the Right Algorithm

It depends on the type of prediction — do you want a yes/no outcome, a trend or a category-wise prediction? It also depends on whether the data is structured/ unstructured and the available processing power and memory.

Machine Learning

The key characteristic of machine learning is its ability to learn from data without explicit programming. AI, on the other hand, can mimic human intelligence but relies on predetermined rules and knowledge.

An AI (artificial intelligence) chess program might use predefined algorithms to evaluate moves, while an ML program could learn how to play winning strategies by analyzing thousands of past games.

Cassie Koryzkov explains it best. She says training a model to select between two choices, for example, white and red wine, is an example of preprogramming, and hence AI.

Training a computer program to recognize a cat’s image by feeding it several cat photos is an example of deep learning. The training data for deep learning is iterative and open-ended rather than a fixed set of rules.

Training involves feature engineering — determining the necessary features to train the ML model and make predictions. It’s the bridge between raw data and usable knowledge.

Feature engineering encompasses various techniques.

• Data Cleaning: Removing noise, inconsistencies and missing values.
• Dimensionality Reduction: Converting complex data into a more manageable format.
• Feature Creation: Deriving new features from existing data to capture the relevant information for analysis.
• Normalization: Scaling data to a standard range for better comparison and performance.

Industries across the board use machine learning for anticipating events, optimizing financial investments, personalizing buyer experiences and automating processes.

Applicable across domains, machine learning is a valuable skill for data students.

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Role of Big Data

Big data refers to huge volumes of complex datasets that are challenging to process with traditional data management tools. Big data is the fuel that powers predictive algorithms — the more data, the better.

Transaction logs, social media chatter, sensor readings and weather patterns feed sophisticated algorithms, generating near-precise business forecasts.

Big data systems continuously refine predictions as new data emerges, allowing businesses to stay ahead of the curve.

Challenges of big data analytics include complying with industry regulations like the GDPR and CCPA and enforcing organization-level security protocols while managing vast volumes.

Techniques like data anonymization and pseudonymization can help protect individual identities while making data available for analysis. Organizations need to be transparent about how they collect, use and store data.

SageMaker, Alteryx, Dataiku, Spark and H2O.ai are some predictive analytics platforms. Refer to our Predictive Analytics Tools product directory to learn more.

How It Works

The specific forecasting process can vary depending on your data, goals and chosen tools.

But the core principles remain the same: by analyzing historical data, identifying patterns, and building predictive models, we can peek into the future and make informed decisions.

Define the Project

Determine what you want to achieve, which data you’ll need and how you’ll measure success.

This Atlassian article gives an excellent example. A company wants to generate a forecast if it can hire a specific number of employees by a certain date.

Instead of measuring the number of hires per week leading up to the date, the company decides to use the screened resumes as a predictive metric. Why? Because it measures the behavior that drives the hiring process.

Looking beyond the obvious will you measure success accurately.

Gather Your Data

Think of this step as building a recipe box. You need all the ingredients (data points), like past sales figures, customer demographics, weather patterns or equipment sensor readings.

The more data you have, the more accurate your predictions will be.

Clean and Prep

Just like washing vegetables, you need to clean and transform your data. It means fixing errors, formatting data consistently and selecting the relevant features that will contribute to your prediction.

Choose Your Tools

Select the data models and algorithms — kitchen tools — to use.

You have a toolbox full of algorithms and models like regressions, neural networks and decision trees. Each algorithm is like a unique recipe suited for different types of data and predictions.

Train Your Model

Imagine teaching a cooking assistant. You feed the model sample data and explain what you want it to predict (customer churn, equipment failures or stock prices).

Through iterative learning, the model starts to recognize patterns and relationships in the data.

Test and Refine

No good chef serves a dish without tasting it first! Test your model’s predictions for accuracy on independent data. If it’s off the mark, you can adjust the ingredients (data features) or even switch recipes (models) to improve accuracy.

Deploy, Score and Predict

Once your model is confident, it’s time to put it to work! You can integrate it into existing applications to receive real-time predictions and decision support for marketing campaigns, resource allocation, and risk management.

Determining the outcome involves model scoring — assigning a score to the new data after running the predictive algorithm. The score could be the probability of an event happening, a high or low-risk classification label or a numerical value.

A high score for customer churn indicates buyers are likely to move away from your product, and it’s time to start planning how to retain them.

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Use Cases

Netflix

Statista predicts Netflix viewership will increase to 780 million by 2026.

The platform’s secret sauce? Data-driven decisions, thanks to predictive analytics.

They analyze every click, scroll and binge to recommend shows you’ll love. Your personalized feed is a result of algorithms crunching your viewing history, ratings and even pauses.

Netflix uses data to identify what content will skyrocket, guiding their productions and acquisitions. They analyze user behavior to understand what resonates, informing the type of shows and movies they greenlight.

They anticipate who might cancel the subscription and woo them back with targeted offers and personalized recommendations.

This data-driven approach is key to Netflix’s success, keeping users glued to their screens and raking in billions. So next time you devour a captivating series, remember: it’s not just entertainment. It’s the power of analytics at work.

Amazon

An early AI/ML adopter, Amazon defines industry standards in retail by creating a personalized shopping experience. Advanced mathematics, statistics, AI and machine learning drive the platform.

It offers tailored recommendations based on your past purchases and browsing habits. Additionally, its email marketing strategy is on point, with customized emails nudging you into the buying cycle.

Did you abandon the cart? Maybe you don’t need that camping gear right now. Amazon notices and follows it up with a reminder email. Or it’ll suggest similar items the next time you browse.

Amazon uses product reviews to collect your preferences and attract new buyers. Wish lists keep you coming back for more, even if items are out of stock.
Forget waiting by the window for your order to arrive. Amazon anticipates user needs by location, shipping popular items in advance to a nearby distribution center.

Features To Consider

Though the required capabilities will depend on your unique business needs, look for the following advanced analytics attributes in predictive analytics software.

• Hyperparameter Tuning: This involves determining the variables, or hyperparameters, for each dataset and model by running a more extensive set of variables through the data model.
• Explainable AI (XAI): It refers to the tools and techniques for understanding the reasoning behind an AI-generated outcome.
• Ease of Implementation: Your predictive platform should deploy and scale flexibly, providing model management and monitoring via an intuitive workflow editor.
• Streamlined Workflow and Collaboration: A visual workflow builder allows more people to build analytics pipelines. Version control encourages collaboration while retaining all changes to the pipeline.
• Security and Compliance: Role-based access control, encryption and audit trails prevent unauthorized data access. Compliance certifications are a must for regulated industries like finance, healthcare and cybersecurity.
• Additional Considerations
  • NLP (natural language processing) is the branch of computer science that deals with processing human speech and text.
  • Integrations with business systems and databases, including CRM, BI, accounting and ERP systems, provide comprehensive data for forecasts.

Consider open-source BI tools for predictive analytics, including RapidMiner, KNIME, Scikit-learn, H2O and Weka. Paid predictive software examples include IBM SPSS Modeler, SAP Analytics Cloud and Einstein Analytics.

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Future Trends

Automation, customer-focused predictions, the cloud and real-time insights will rule the predictive BI domain in the coming years.
Here’s a list of watchable trends.

• No-code/low-code platforms will enable more people to participate in data tasks. Self-service BI with automated data storytelling will make a formidable combination.
• Cloud services will offer access to powerful analytics tools, including forecasting systems. Multicloud deployments are widespread, with over 98% of public cloud users opting for more than two providers.
• Automated data preparation will speed up pipelines, though identifying and correcting inconsistencies will always require human judgment and expertise.
• Customer insights will be central to product development and promotion, and buyer-centric predictions will drive better user experience.
• As decision intelligence comes into focus, real-time market response will drive business agility.
• AI-related data security concerns will overshadow the attempt to foster user trust with explainable AI.

Read our BI trends article for more information.

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Next steps

Embracing a data-driven approach requires promoting data literacy, breaking down silos and recognizing the power and limitations of predictive analytics.

Alongside data privacy considerations, continuous learning, model improvement, and data quality investments are crucial for removing bias and promoting transparency.

By combining data insights with human judgment, we can unlock the true potential of a data-driven future.

Looking for predictive analytics software? Get our free requirements template to define your unique business needs and select a suitable platform to grow your business.

Have you used predictive platforms? How was your experience? Let us know in the comments.