What is predictive analytics?

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

Today, companies today are inundated with data from log files to images and video, and all of this data resides in disparate data repositories across an organization. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. Some of these statistical techniques include logistic and linear regression models, neural networks and decision trees. Some of these modeling techniques use initial predictive learnings to make additional predictive insights.

Predictive analytics models are designed to assess historical data, discover patterns, observe trends, and use that information to predict future trends. Popular 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 given 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 answering yes or no or true and false; popular use cases for this are fraud detection and credit risk evaluation. Types of classification models include logistic regression, 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 using Gaussian Mixture Models (GMM), and hierarchical clustering.

Time series models use various data inputs at a specific time frequency, such as daily, weekly, monthly, et cetera. It is common to plot the dependent variable over time to assess the data for seasonality, trends, and cyclical behavior, which may 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 in across various industries for different business problems. Below are a few industry use cases to illustrate how predictive analytics can inform decision-making within real-world situations.

• Banking: Financial services use machine learning and quantitative tools to make predictions about their prospects and customers. With this information, banks can answer questions like who is likely to default on a loan, which customers pose high or low risks, which customers are the most lucrative 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, as well as to 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 particularly useful when labor markets are volatile.
• Marketing and sales: While marketing and sales teams are very familiar with business intelligence reports to understand historical sales performance, 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 leverage predictive data analysis for cross-sell strategies, and this commonly manifests itself through a recommendation engine on a brand’s website.
• Supply chain: Businesses 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 given product becomes more expensive to import, companies can project the long-term impact on revenue if they do or do not pass on additional 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.

An organization that knows what to expect based on past patterns has a business advantage in managing inventories, workforce, marketing campaigns, and most other facets of operation.

• 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 end user behavior can trigger specific security procedures.
• Risk reduction: 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 may use predictive analytics to better understand whether their insurance coverage is adequate.
• Operational efficiency: More efficient workflows translate to improved profit margins. For example, understanding when a vehicle in a fleet used for delivery is going to need maintenance before it’s broken down on the side of the road means deliveries are made on time, without 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.