Predictive Analytics: What It Is and How to Use It Effectively

The business landscape is more competitive than ever. That means organisations need to be proactive rather than reactive when it comes to strategising and decision-making, both for gaining new customers and retaining the ones they already have.

This is where predictive analytics comes into play. When used correctly, its data-driven insights can help businesses stay ahead of the curve and anticipate changes, often giving them a vital head start in competitive areas.

In this article, we’ll explore exactly what predictive analytics is, how predictive analytics models work and how to use it to get an edge over your competitors. Let’s dive in.

• What is predictive analytics?
• Predictive vs. prescriptive analytics
• How predictive analytics works
• How is predictive analytics used?
• Why is predictive analytics important?
• Types of predictive analytics models
• Five examples of predictive analytics in action
• How to leverage predictive analytics with Salesforce
• FAQs

Predictive analytics is a form of artificial intelligence (AI) analytics that uses in-depth datasets, statistical modelling, and machine learning to forecast potential outcomes linked to various decision-making areas. It leverages AI to explore past data on things such as customer behaviour, market trends and competitor research to map out where things are going and where a business can take advantage of opportunities.

AI in predictive analytics is having an enormous impact on businesses; the global predictive AI market is projected to reach US$108 billion by 2033.

And the reasons are obvious. Businesses that use predictive analytics can minimise unpredictability and indecision, reduce risk and optimise resources, helping them make faster and more confident decisions that drive growth.

As AI use increases, various terms become more familiar. Another term people are using more regularly is ‘prescriptive analytics’. While the two terms sound similar, it’s essential to understand the distinct functions that predictive and prescriptive analytics perform for businesses.

A simple way to think of it is that predictive analytics gives businesses insight into what is likely to happen, using data and modelling. Prescriptive analytics takes that one step further by providing insights on what should be done about those predictions. It supports data-driven decision-making, recommendations and solutions.

As you can see, the two terms are closely linked, but there are differences. Here’s a table that sums it up:

The basic thinking behind predictive analysis is simple: You use data to help forecast future outcomes. It’s all about using patterns and data to make informed predictions, regardless of your industry or sector.

Here, we’ve created a step-by-step guide to explain how predictive analytics works and what you need to do to achieve it.

Predictive analytics is only effective if you have a clear reason for using it. You’ll need to establish an objective to inform which types of data you’ll be using.

This objective needs to be specific to achieve the most useful outcomes. For example, you might want to predict anticipated sales of a new product line over a three-month period using historical launch metrics of your business and those of rival companies.

Once you’ve established your objective, identify and collect the most relevant datasets that will help you fulfil it. This data could come from internal sources (databases, CRM systems, transaction records) or external sources (demographic data, competitor research).

Your business will likely have multiple datasets across a vast range of sources, which can be difficult to manage if they aren’t integrated. Data 360 is designed to solve this issue by helping you standardise data across your enterprise and unify it into a single source of truth.

For predictive analytics to be accurate, the data you feed into algorithms and machine learning models needs to be clean and consistent. Data preparation and processing involves removing datasets with missing values, extracting anomalies and deleting duplicated entries to keep everything consistent and ready for analysis.

If your datasets aren’t clean, you could end up basing your decisions on flawed data, which could have a huge knock-on effect on your success.

Also known as data transformation, the goal of this step is to transform raw data into meaningful information that algorithms recognise. This makes it far easier for machine learning models to identify repeated patterns, which will then form the basis of reporting and analytics.

Some common predictive analytics techniques used at this stage of the process include data aggregation (totals and averages over specified periods), recency and frequency metrics and other figures, such as moving averages and ratios.

With the transformed data now in place, it’s time to build the AI models and algorithms that will produce your analytics and insights. We’ll cover these models in more detail a little later, but some of the most common models used for predictive analytics include decision trees, regression and cluster analysis.

Once you’ve built the analytics model, it’s time to put it to work. You should have a clear understanding of how often you plan to run predictions and be confident in your preprocessing and feature logic.

Predictive models will degrade over time, and they’re not able to adapt to changes in market or business conditions automatically. Therefore, it’s vital that you monitor performance over time and account for potential changes with retraining schedules.

Predictive analytics was previously out of reach for most organisations. However, recent advancements in the technology that underpins it (including machine learning and AI) have made it far more accessible.

It’s also something that all businesses can utilise, no matter their industry. Recent research indicates that more than 95% of companies have adopted predictive analytics in some capacity, with the banking, financial services and insurance (BFSI) sector currently securing the biggest market share at 23%.

But how exactly are businesses using predictive analytics? Here are some of the most common use cases.

A common entry point is to use predictive analytics in conjunction with a business’s customer relationship management (CRM) system. By integrating with a CRM, businesses are able to make predictions about customer behaviour across sales, marketing and service channels. This might include analysing customers’ past buying habits to identify opportunities for cross-selling.

At Salesforce, our Customer 360 platform provides the perfect system for smooth integration with our data and analytics solutions, including Data 360 and Tableau. This gives our customers full control and visibility over their unified data, allowing for superior predictive analytics in terms of forecasting.

Organisations can use predictive analytics to help reduce risk. For example, banks use a mortgage applicant’s data (such as their employment status, income, savings-to-debt ratio and credit score) to predict whether they would be a low- or high-risk borrower. They also use the information to determine how much and what interest rate they should realistically be offering.

In addition, banks and other financial institutions use machine learning to spot patterns that could indicate fraud.

Healthcare providers also use predictive analytics in several ways. Many are now using machine learning algorithms to examine a patient’s historical records. The purpose of this approach is to identify patients who are potentially at higher risk of developing certain conditions, such as diabetes. They can also use these models to help develop diagnoses, based on symptoms and other core health metrics, for their patients’ current issues.

Another common use of predictive analytics for ecommerce businesses is supply chain management. Predictive models can help a business estimate future customer demand for certain goods based on historical sales data, which can help inform a more optimised set of inventory. They can also help companies evaluate supplier performance and risk prediction based on things such as lead-time variability and quality consistency.

The data that businesses and governments generate is a gold mine of information. Budgets and margins are often much tighter than they used to be, with both domestic and geopolitical factors often causing a great deal of unpredictability. Having a tool that can help you navigate the choppiness of the business landscape is often a crucial advantage.

Some of the key reasons why predictive analytics is so important include:

• Improved decision-making
• Enhanced efficiency
• Better customer understanding and engagement
• Proactive risk management
• Increased profitability and competitive advantage
• Personalised and improved services

There are several types of analytics software models that businesses can deploy for their forecasting. Each has a particular set of use cases that they’re best suited for. And often, they can be used in tandem as part of a wider forecasting objective.

The main types of predictive analytics models are as follows.

This type of predictive analytics model is geared towards binary decision-making to arrive at predictions. It’s often represented as a flow chart that closely resembles a tree (which is why this type is also referred to as a decision tree). Compared to other types, this is a simplistic model; the outcomes are easier to understand as a result.

With a regression model, the outputs are usually numerical figures rather than binary choices. It’s used to uncover patterns within large datasets. Regression analysis is the most commonly used type of analytics model. Stock market algorithms will use this type of model to forecast potential share prices based on dozens of metrics.

A business will use this type of modelling if it wants to generate a continuous set of predictions over stated time intervals (daily, weekly, monthly, etc.). The outcomes are based on historical, time-ordered data. It’s a particularly crucial model for ecommerce businesses, which will use the input data to forecast things, such as anticipated product demand and inventory management.

This is a model that identifies and clusters data entries based on shared characteristics. Using historical customer data, a business will use this model if it wants to explore whether certain demographics are more or less likely to engage with what the company offers. It can then tailor its marketing efforts accordingly.

As the name suggests, this model identifies key outliers in the data that may suggest unusual behaviours or errors in workflow. In certain cases, businesses will train models to exclude this form of data during the data cleanup process. However, in cases where a business may need to monitor for critical hardware performance, for example, this model is a crucial tool.

Let’s discuss five predictive analytics use cases where businesses have provided more personalised experiences to their customers, increased revenue and built systems to make more consistent, accurate decisions.

Home appliance manufacturer Fisher & Paykel struggled to cope with the high volume of calls its customer support teams were fielding. Many of these calls were related to appliance troubleshooting and requests for technician visits, something the business was looking to reduce.

With Agentforce, Fisher & Paykel was able to integrate and standardise data and information from over 10,000 help articles, which agents could instantly retrieve based on the issues the customer was reporting. Service agents were then able to relay clear and accurate troubleshooting steps over the phone rather than sending out a technician.

Agentforce also extended Fisher & Paykel’s customer support, allowing for 24/7 monitoring and a dramatic increase in online service bookings that did not require an in-house rep.

One of the key benefits of predictive analytics from the customer’s perspective is the increased level of personalisation they receive from their favourite brands.

Netflix, along with other high-tech companies like Amazon, use historical behavioural data to anticipate what customers might look at or buy next. Netflix, for example, can offer personalised recommendations and playlists based on what individuals have already watched.

In Melbourne, St Vincent’s Hospital is currently using AI and predictive analytics in a groundbreaking way with their BRAIx project. Through this initiative, the hospital is developing AI models that will lead to improved accuracy in breast cancer screening appointments, with the goal of dramatically increasing the overall efficiency of this procedure.

The AI model is intended to be used in conjunction with existing procedures, where each mammogram is evaluated by two radiologists, an essential step in maintaining the human-centric side of medical diagnoses.

Commonwealth Bank has embedded predictive analytics into its HR processes, giving the HR team a better perspective of how employees are doing.

They’ve used the technology to deal with a number of issues, including keeping more accurate records of sick leave, employee disillusionment and the symptoms that often precede resignations. This provides them with an opportunity to reverse these trends.

Many governments are also deploying predictive analytics to help improve public services and efficiency. Victoria, for instance, is using predictive models as part of their Sustainability Victoria initiative.

In particular, the agency is working with a number of key environmental agencies to build data projection models that will help predict future waste amounts, especially those linked to hazardous waste. The goal is to help improve collection management efficiency.

Predictive analytics has been a game-changer for thousands of businesses. It has turned uncertain forecasting into a data-driven and efficient process that delivers actionable insights. And it’s not just limited to one type of business. Its versatility and scalability will ensure that more businesses will use it in the years to come.

Salesforce offers customers a complete journey of predictive analytics, with all aspects working together seamlessly. With our extensive range of products and applications, you can:

• Collect and integrate your data into a single source of truth with Data 360
• Analyse data, build predictive models and access advanced visualisation with Tableau
• Turn analysis into action and deploy evidence-based strategies within Agentforce

It’s all there, in one place, ready to help your business take advantage of the full power and potential of predictive analytics.

Start your free 30-day trial today.