What is predictive analytics?

Companies employ predictive analytics tools to find patterns in data that help identify risks and optimize opportunities. Predictive analytics is often associated with big data and data science.

Today’s companies generate enormous amounts of data—from log files to images and video—stored across many different systems and repositories throughout the organization. To extract real-time insights from this data, data scientists apply deep learning and machine learning algorithms that identify patterns and predict future events.

Common techniques include logistic and linear regression models, neural networks and decision trees. Some of these aspects are iterative, meaning early predictions are used to refine and improve future ones.

Predictive analytics works by using historical data and past patterns to forecast future outcomes. Predictive analytics draws on artificial intelligence (AI) and machine learning (ML) techniques to automate the data process and analyze large datasets quickly.

These analytics tools improve on traditional predictive analytics by helping organizations make better data-driven decisions and deliver more personalized customer experiences.

Predictive analytics is one of four types of data analytics:

• Descriptive analytics: Analyzing historical data to understand what happened in the past.
• Diagnostic analytics: Using historical data to learn why something happened in the past.
• Predictive analytics: Examining historical and current data to predict future trends and patterns.
• Prescriptive analytics: Analyzing historical and real-time data and recommending actionable steps to take to achieve an outcome.

The information presented ahead is a step-by-step process for building predictive analytics frameworks:

1. Define the problem: The first step in building a predictive analytics framework is clearly defining the problem that you are trying to solve. This definition might range from predicting outcomes after a severe weather event to optimizing inventory levels for particular seasons or high-demand periods. A well-defined problem sets the direction for every step that follows.
2. Collect data and establish a data management strategy: After the problem is defined, the focus shifts to gathering the data needed to make accurate predictions. This approach includes establishing a clear data management strategy to organize and maintain that data effectively. AI-powered migration services can simplify this process by modernizing data warehouses and data lakes, making large volumes of data more accessible and usable.
3. Prepare the data: Before any modeling can begin, both new data and historical data must be thoroughly cleaned and prepared. This step involves identifying and removing errors such as anomalies, duplicate entries and missing data points. Proper data preparation is critical, as the quality of the data directly impacts the reliability of the predictive models built on top of it.
4. Develop and deploy predictive models: With clean data in hand, data scientists can begin building the predictive models themselves. These models are trained to identify patterns and generate forecasts based on the prepared data. After it is developed and deployed, it is essential to continuously monitor the models and make adjustments as needed to maintain their accuracy and relevance over time.
5. Share results with stakeholders: Upon achieving acceptable results, the findings should be communicated clearly to stakeholders and relevant teams across the business. Sharing insights in an accessible way ensures that decision-makers can act on the predictions and that the value of the analytics framework is realized organization-wide.

Predictive analytics models are designed to mimic the functions of the human brain. The models assess historical data, discover patterns, observe trends and use that information to predict future trends and make informed decisions. Predictive analytics models include classification, clustering and time series models.

Classification models fall under the branch of supervised machine learning models. These models categorize data based on historical data, describing relationships within a dataset.

For example, this model can be used to classify customers or prospects into groups for segmentation purposes. Alternatively, it can also be used to answer questions with binary outputs, such as answering yes or no or true and false. Popular use cases for this method are fraud detection and credit risk evaluation.

Types of classification models include logistic regression, regression analysis, decision trees, random forest, neural networks and Naïve Bayes.

Clustering models fall under unsupervised learning. They group data based on similar attributes.

For example, an e-commerce site can use the model to separate customers into similar groups based on common features and develop marketing strategies for each group.

Common clustering algorithms include k-means clustering, mean-shift clustering, density-based spatial clustering of applications with noise (DBSCAN), expectation-maximization (EM) clustering with Gaussian Mixture Models (GMM) and hierarchical clustering.

Time series models use various data inputs at a specific time frequency, such as daily, weekly, monthly, and so on. It is common to plot the dependent variable over time to assess the data for seasonality, trends and cyclical behavior, which might indicate the need for specific transformations and model types.

Autoregressive (AR), moving average (MA), ARMA and ARIMA models are all frequently used time series models. As an example, a call center can use a time series model to forecast how many calls it will receive per hour at different times of day.

Predictive analytics can be deployed across various industries for different business problems. The next part presents a few industry use cases to illustrate how predictive analytics can inform data-driven decision-making within real-world situations.

• Banking: Financial services use machine learning and quantitative tools to make predictions about their prospects and customers by looking at things like income, employment status and credit score. With this information, banks can answer questions like who is likely to default on a loan or which customers pose high or low risks. They can also tell which customers are the most lucrative on which to target resources and marketing spend and what spending is fraudulent in nature.
  Healthcare: Predictive analytics in health care is used to detect and manage the care of chronically ill patients and track specific infections, such as sepsis. Geisinger Health used predictive analytics to mine health records to learn more about how sepsis is diagnosed and treated. Geisinger created a predictive model based on health records for more than 10,000 patients who had been diagnosed with sepsis in the past. The model yielded impressive results, correctly predicting patients with a high rate of survival.
• Human resources (HR): HR teams use predictive analytics and employee survey metrics to match prospective job applicants, reduce employee turnover and increase employee engagement. This combination of quantitative and qualitative data allows businesses to reduce their recruiting costs and increase employee satisfaction, which is useful when labor markets are volatile.
• Marketing and sales: Marketing and sales teams are familiar with business intelligence reports to understand historical sales performance. However, predictive analytics enables companies to be more proactive in the way that they engage with their clients across the customer lifecycle. For example, churn predictions can enable sales teams to identify dissatisfied clients sooner, enabling them to initiate conversations to promote retention. Marketing teams can use predictive data analysis for cross-sell strategies, and this aspect commonly manifests itself through a recommendation engine on a brand’s website.
• Supply chain: Businesses like retailers commonly use predictive analytics to manage product inventory and set pricing strategies. This type of predictive analysis helps companies meet customer demand without overstocking warehouses. It also enables companies to assess the cost and return on their products over time. If one part of a specific product becomes more expensive to import, companies can project the long-term impact on revenue based on whether they pass on more costs to their customer base. For a deeper look at a case study, you can read more about how FleetPride used this type of data analytics to inform their decision-making on their inventory of parts for excavators and tractor trailers. Past shipping orders enabled them to plan more precisely to set appropriate supply thresholds based on demand.

It’s crucial for an organization to be able to predict future trends and outcomes. A business that knows what to expect based on past patterns has an advantage in managing inventories, workforce, marketing campaigns and most other facets of operation.

• Improve security: Every modern organization must be concerned with keeping data secure. A combination of automation and predictive analytics improves security. Specific patterns associated with suspicious and unusual user behavior can trigger specific security procedures.
• Reduce risk: In addition to keeping data secure, most businesses are working to reduce their risk profiles. For example, a company that extends credit can use data analytics to better understand if a customer poses a higher-than-average risk of defaulting. Other companies might use predictive analytics to better understand whether their insurance coverage is adequate.
• Operational efficiency: More efficient workflows convert to improved profit margins. For example, understanding when a delivery vehicle in a fleet is going to need maintenance (before it’s broken down on the side of the road) means that deliveries are made on time. This approach avoids the additional costs of having the vehicle towed and bringing in another employee to complete the delivery.
• Improved decision-making: Running any business involves making calculated decisions. Any expansion or addition to a product line or other form of growth requires balancing the inherent risk with the potential outcome. Predictive analytics can provide insight to inform the decision-making process and offer a competitive advantage.