Predictive analytics have been around for a while, but the concept only gained significant traction in recent years. Several factors contribute to this. Economic conditions are tougher, and competition is more intense, so companies are scrambling to differentiate. Predictive analytics reveal opportunities for that.
In addition, analytic capabilities are more accessible in today’s software systems and easier for the average business user to understand. The sheer volume of data in the Cloud has given immediate and widespread data access to internal teams, providing more fuel to burn the fires of predictive analysis.
So, what do we need to know about predictive analytics? How are industries using it to their advantage and how can we do the same? In this blog, we’ll try to get a grip on those answers.
The Importance of Predictive Analytics
Predictive analytics help businesses solve complex problems by revealing patterns not otherwise seen. These patterns help businesses detect both risks and opportunities.
For risk detection, it’s useful to enlist behavioral analytics. Behavioral analytics, a subset of predictive analytics, looks at behavioral patterns to uncover abnormal activities. The importance of this capability goes unsaid, as digital security is a serious concern for just about every business.
Predictive analysis also uncomplicates forecasting so businesses can better manage resources and guide thoughtful investments. The patterns can be used to make proactive decisions towards cross-sell and upsell opportunities, recognize new sales opportunities, and gauge customer satisfaction. All of which are valuable insights for companies operating in a demanding market.
Predictive Analytics in Practice
Predictive analytics has a nearly unlimited number of uses, so it’s easy to see how so many different types of businesses could tailor it for their objectives. Draw some inspiration from the examples below for ideas on how to put predictive analytics into practice in your organization.
- Health Insurance – Aside from using behavioral analytics to detect claims fraud, the health insurance industry also uses patterns to identify at-risk patients for chronic diseases.
- Government and Public Sector – The US Census Bureau has been using analytics to understand population trends for years. Tracking consumer behaviors also reveals fraud and security risks. Predicting equipment maintenance and future resource needs also is helpful for this industry.
- Automotive – Behavioral analysis strikes again! The automotive industry studies driver behaviors to improve autonomous vehicles and understand which technologies will be most useful in assisting drivers. They also use quality assurance models to pinpoint potential defects.
- Manufacturing – Analysis of past machine failures allows manufacturers to predict and avoid those failures. Predictive analytics also offer projections on future demand, so the manufacturer can optimize production to match.
- Energy – Analytics allow energy companies to forecast demand and adjust pricing based on past cycles. The impact of major weather events can be predicted and prepared for with analytics as well.
- Retail – Probably one of the most familiar models to today’s consumers, tracking customer behavior online is the quintessential calling card of the retail industry. Understanding buying habits helps the industry know which additional products the consumer is likely to purchase, and which incentives will increase the likelihood of a purchase.
- Law Enforcement – Crime trends help law enforcement pinpoint neighborhoods that may need more protection at certain times of the year or during certain events.
Common Models for Predictive Analysis
The model you choose for your predictive analysis is dependent on the goal you’re trying to accomplish. A while back, I wrote a post on predictive analytics as applied specifically to CRM. In that post, I explained how to use CRM data to reveal sequencing patterns, cross-sell opportunities, and lack-of-action data. Those objectives can each be achieved by using the appropriate model. Here are some of the most common ones:
- Customer Lifetime Value Model: The meaning is in the name. You calculate a customer’s lifetime value based on how many goods and services you predict they’ll purchase over time.
- Classification Model: This approach draws conclusions and makes predictions based on categories. Meaningful categories help organize and make sense of the data points. For example, classifying whether someone is a good or a bad credit risk.
- Decision Tree Model: These are classification models that break the data into subsets of information. These are most often used to understand a consumer’s decision path. Each branch represents a choice between several alternatives. The goal is to find the variable that splits the data and then create logical groups from that.
- Predictive Maintenance Model: When we talk about the manufacturing sector predicting machine failures, this is the model we’re referencing. It’s used to forecast the chances of equipment breaking down, aka predicting maintenance!
- Quality Assurance Model: Most of us have probably heard the phrase ‘quality assurance’ in car commercials, and it makes sense that we would. This model is aimed at finding and preventing defects that could add extra production costs or be a danger to consumers. The automotive industry uses it a lot.
- Neural Model: This is a sophisticated predictive analytics technique used for extremely complex relationships. The analytics are based on patterns and AI processes that create visual models. They are most often used when working with nonlinear data relationships. Researchers developed this model to mimic the neural functions of the human brain.
Getting Started with Predictive Analytics
Setting up and maintaining a predictive analytics model can be time consuming, so you must have a plan. Here are the key steps to keep in mind when getting started with predictive analytics:
Step 1: Define the problem. To understand which data sets you need to analyze, you must first know which outcomes you’re looking for. Define the business objectives you want to achieve with your analytics plan, first and foremost.
Step 2: Prepare your data. Does this take time? Yes. Is it worth it? You bet! Aggregate the data, clean it, and prepare it for analysis. Some software companies offer tools specifically for this.
Step 3: Build the model. Use software to build out the model that works best for your goals. Enlist the help of IT management and data analysts to deploy and refine the model. You’ll also tweak the model over time.
Step 4: Start small. Start your predictive analytics plan on a limited scale to reduce the time investment and costs until you know how well it’s working. You may consider running a few smaller models as a sort of A/B test to discover which approach delivers the most accurate outcomes.
Step 5: Keep the team involved. As they say, it takes a village. It’s important to have different minds involved in the modelling process. Most importantly, you need at least one person with a deep understanding of the business problem you’re trying to solve. It’s also crucial to keep IT in the loop for infrastructure suggestions, data preparation, and refining the models.
Step 6: Monitor the performance. When will you measure the performance of the model? How will you know it’s providing the expected results? Unfortunately, this might take a little time, which is another reason starting small is a good idea. If you realize five months in that your predictive models need an overhaul, you can pivot faster and cut your losses.
Thinking about getting started with predictive analytics? Wondering which CRM systems might offer the right tools and features? Contact us. We’ll help you figure out what you need.