“What will happen if…?”
“Which is more likely, A or B?”
From presidential elections to weather forecasts and the stock market, humans have always been fascinated with predicting future outcomes. They feed our curiosity and help us plan ahead. But for businesses and organizations, making the right predictions can provide a serious competitive advantage.
So, how can your business use predictions to save money and get superior results? Here, we’ll cover some popular use cases, plus steps for getting started with predictive analysis.
Predictive analysis uses historical information and past patterns to predict outcomes or trends that could occur down the road. The method combines techniques like data modeling, data mining, statistics, regression analysis, artificial intelligence, and machine learning to answer specific questions about future possibilities.
Predictive analysis can be beneficial for a wide range of individuals, companies, and organizations that want to identify impending threats and opportunities. In business, everyone from product marketers, CX experts, operations teams, and data governance specialists stand to benefit.
New use cases for predictive analytics emerge every day. Across industries like ad tech, media, entertainment, online gaming, and more, companies are already using them for:
The end result of the above applications? Increased profit margins. For example, one study estimates companies lose $35.3 billion annually to customer churn.
Getting started with predictive analysis can be overwhelming, given the complexity of predictive models and the range of potential applications. To help you out, we’ve broken the process down into 6 major steps:
The first step is identifying a problem you want to solve or a result you want to achieve. For example, you might want to learn:
Achieving the objectives you’ve laid out will require significant amounts of data. But not just any data. Assess your available sources to make sure that they’re accurate, complete, relevant to your goal, and large enough for predictive modeling.
As much as possible, data collection should involve people from different roles and departments. Collecting cross-functional data from varied sources, platforms, and perspectives will give you a richer and more complete analysis.
Most data requires preparation before you can work with it. Data cleansing includes removing errors, handling missing or duplicate values, and fixing formatting or other inconsistencies. It might seem small, but a misspelled name or an old email address can skew your outputs and eventual insights.
Data cleansing is tedious, sucking up large amounts of skill and labor. Most enterprise software solutions offer data cleaning features to save your teams time and effort.
Just like the use cases, potential techniques for predictive analysis vary widely. Choosing the best predictive model to use will depend on the problems or questions you defined in step one.
Options include:
Or others, depending on your data and goals. If you don’t have data scientists on payroll, you might consider outsourcing. Or, a software solution embedded with predictive modeling tools, such as a decision intelligence platform, can make this step automatic.
Once your models are running, the fun begins. Putting your newfound insights to use is the crux of a fruitful predictive analysis. So, how do you do that?
First, share your insights with others. Then gather ideas and make an action plan. For example, let’s say your analysis found a set of customers who are at risk of churning soon. Share this information with sales, CX, product, and operations teams to get the best chance at a useful solution. This could mean offering at-risk customers promotional deals, tweaking the content they see, or enrolling them in a nurture workflow to prevent churn.
Brainstorm ideas and then outline next steps based on the information. You’ll evaluate the effectiveness of your actions in the next step.
Predictive analytics is a highly iterative process. Observe your testing group to review the results of your actions, track progress, and incorporate new data. As you receive feedback and test new changes, you can continually tweak your models and action plans to improve the overall product experience for your customers.
You may need to revisit these steps as you gain insights and refine your models to achieve better predictions. Ideally, analysis and iteration will become ingrained into business processes and culture as you see positive results.
Successfully implementing predictive analytics is not a simple task, especially if you have limited data management resources. But doing so is easier thanks to machine learning-powered software that handles the heavy lifting.
To learn more about ML-powered decision intelligence for better CX, customer retention, and omnichannel marketing, talk to an expert today.