Chances are, if you’re in marketing, sales, or one of the more technical aspects of business, you’ve used predictive analytics in some part of your job. Retail giant Amazon uses predictive analytics to study the behaviors of over 200 million customers who produce over 1 billion GB of website data per year, which results in tailored product suggestions that earn the company over $2 billion in sales a year. But your company doesn’t have to be a retail giant to use predictive analytics. You just need data, well-defined goals, and the willingness to iterate.
Companies of all industries and sizes use predictive analytics to understand stored business data on customer actions or business processes. That could be a B2B marketing firm using predictive lead scoring to generate better leads, or a manufacturing company using predictive analytics built into their supply chain analytics software.
When companies feed their customer and process data into predictive models, they better understand future behavior and make decisions based on hard data, rather than gut feelings.
Predictive Analytics in a Nutshell
Predictive analytics is a subset of business intelligence that focuses specifically on learning past behaviors to predict future behaviors. Most people have interacted with a predictive model when applying for a credit card or loan. Financial institutions use predictive analytics to assign credit scores. These scores take data on past financial interactions and use them to predict future financial behavior: if you take out a loan and never miss a payment, your score will be higher because the predictive model suggests you’ll also repay future loans.
This type of modeling can be extended to almost any business process. Retailers use purchasing behaviors to understand which customers are likely to come back into the store or make an online purchase, while manufacturers can use modeling to understand when machines will need repairs. Many versions of marketing automation software use data collected from your interactions with a website to predict how likely you are to purchase a product. By adding distinct and measurable data from across your business into a predictive model, you can better understand your customers’ future behavior.
Best Practices for Predictive Analytics
Many teams use the Cross Industry Standard Process for Data Mining (CRISP-DM) model to guide their predictive analytics systems. This multi-industry framework will help your team build predictive models based on the data you have, and the circular framework will help you implement your findings into daily business processes. We also suggest the following best practices as you’re building your predictive analytics system:
Clarify your objectives: When you define the outcomes you’re hoping to predict, whether it’s sales, equipment repairs, email sign-ups, or some other metric, you’re better able to understand the metrics that will get you to that point.
Build your infrastructure: Predictive analytics runs off a lot of data, so make sure your team makes that data available to you. This may mean making API connections or importing data from otherwise siloed departments and tools. Communication between tools and teams is of utmost importance in this stage and throughout the process.
Define success: What is an acceptable bar for success for your company? Give your teams some margin for error and unknowns.
Build from Proof of Concept: Start your predictions from data you already have. If your model predicts outcomes you’ve already proven, you can show stakeholders that the technology works before digging into unknowns.
Maintain your data: Getting your processes up and running is a great start, but ‘garbage in, garbage out’. Don’t underestimate the importance of data quality and maintenance as your models progress.
Use what you know: Predictive analytics without action is useless. Let your models build predictions, and then modify your business plans accordingly.
Predictive analytics may help you move the needle on your campaigns or processes, but the more data you use, the more you’ll want to feed into your machine. It’s a circular process, but because predictive analytics learns from new information, it should get better and better with each cycle.
Just remember, all increases in outcomes have an upper limit. While your efforts may improve with particular data sets in the short term, new data may be needed to continue to build your business. Stay on the lookout for new data to add to your predictive models as you move through your cycles.
It’s also good to remember that you and your analytics teams should act as the experts when building and implementing models. Computers can construct pictures of customer behavior, but they work with a limited number of possible factors. You know your customers better than the computer can, but computer models will take out some of the guesswork. Check your models against your industry knowledge, and test before you make sweeping changes.
Think of predictive analytics as making ever more educated guesses based on the data you have. This means that your predictions will fit into statistical models, and that just because all the data points to a particular outcome, it may not happen that way in reality.
By necessity, your models are built with a finite number of variables at play, where an infinite number of variables can compound within actual human behavior. No model will accurately predict all behaviors, but the more data you use the better your models will be at predicting.
Interested in learning how to select the right advanced analytics model? Here’s a two-minute guide to understanding and selecting the right descriptive, predictive and prescriptive analytics for use across your supply chain. (links here: https://www.logility.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/)