Traditional analytics tells us what happened in the past. It’s there in the dashboards, visualizations, and graphs. However, it doesn’t tell us what’s ahead.
For that, we need predictive analytics.
Predictive analytics is a powerful way to find business insights, by using historical data to make future predictions and take control of future outcomes. With the help of predictive analytics tools and models, organizations of all shapes and sizes can use their historical data to make educated decisions days, weeks, and even years into the future.
Predictive analytics is extremely versatile, and predictive models can be used for everything from measuring employee attrition to customer churn.
With a market cap of approximately $10.95 billion by 2022, predictive analysis is a must for businesses who want to get ahead of their competition.
With predictive analytics, organizations can find and use patterns within data to detect risks and opportunities. In short, it’s the future of business intelligence.
The difference between prescriptive analytics and predictive analytics is that predictive analytics focuses on forecasting possible outcomes while prescriptive analytics aims to find the best solution given a variety of choices. Predictive analytics sits under the umbrella of prescriptive analytics.
At its core, predictive analytics is about forecasting the future. With predictive analytics you can analyze trends in historical data to predict customer volume, average order size, and recurring revenue. All these use-cases can be used to optimize revenue and costs at every level, saving money.
Whether you’re a SaaS business or an eCommerce company, you can use data and predictive models to implement new techniques to engage with customers. By analyzing historical data, you can begin to better understand cyber attacks, credit card fraud, plan marketing campaigns, and more. Fraud analysis is a common use-case in the financial services industry in particular.
Without a predictive analytics approach, business leaders may opt to go by gut instinct, instead of a data-driven approach based on hard numbers. Predictive analytics is thus a powerful competitive advantage.
There are countless examples of predictive analytics, since it can be applied to virtually any tabular dataset with a KPI.
A few major examples include:
eCommerce may use predictive analytics for everything from measuring churn to trying to predict the sales of a new product. Airlines may look at how much people were willing to pay in the past to price future tickets. Hospitals can analyze big data, so doctors and nurses can understand which patients are at high risk and which ones can go home.
Predictive analytics can really help make any business decision truly data-driven, by using current data to plan for future events.
How can you get started? First, you must understand what you’re trying to optimize. The most important step of gearing up for predictive analytics is to make sure you’re properly tracking every piece of data you want to measure.
This might mean going into your Google Analytics account and confirming goals and conversions are set up or making sure Salesforce is connected properly. If you want to analyze more qualitative data, you might need to conduct a survey first.
Once you know your data is accurate, it’s time to make sure it is clean. We talk a lot about data transformation and how to create high-quality datasets. This is a critical step in the process for whatever type of predictive analytics tool you use from Excel to Apteo.
Classification models are one of the most common machine learning techniques, where the goal is to predict a “class” label, or discrete attribute.
That class could be an image, such as people wearing a mask and people not wearing a mask, and image classification could be used to automatically distinguish between the two. The class could also be a sound, like someone breaking a plate in a restaurant versus normal restaurant sounds.
Most commonly, however, that class is simply a text column in a tabular dataset, like churn or attrition. Many machine learning models can be used for classification, like decision trees, random forest, naive bayes, logistic regression, and others.
Another common technique is clustering, which identifies groups in data, rather than using predefined labels. In other words, clustering is an “unsupervised” technique, since there aren’t labels, while classification is “supervised.”
This is useful because we often don’t have labels in the real world. A common clustering use-case is customer segmentation, where the model finds groups of customers according to their attributes.
Another common technique is regression, which is when you need to predict a continuous label, rather than a discrete label like in classification. For example, you might use classification to predict whether a customer will churn or not, while linear regression might be used to predict how much a customer will spend.
There are countless use-cases for regression, but it’s often used when you have time series data, such as asset prices. For instance, I’ve used the Facebook Prophet library to predict Bitcoin prices, which are a continuous variable.
All the models we’ve discussed are traditional machine learning techniques. The phrase “deep learning” refers to neural network machine learning models that are trained on big data. This is more common at big companies like Facebook and Google, that have billions of users and therefore tons of data, or use-cases where big data is inherent to the problem, like self-driving cars. These techniques also fall under the umbrella of data mining, which refers to finding insights and patterns in data.
Traditionally, predictive analytics was in the domain of a team of expert data scientists, who would spend months building a data infrastructure and machine learning models using complex programming languages.
Today, you don’t need any data science or coding chops to implement predictive analytics, thanks to no-code tools like Apteo.
Apteo makes it easier than ever to use predictive analytics. Simply connect any data source with a KPI column, or values you want to predict, and attribute columns, or values that help describe your KPI. Then, we’ll show you how each attribute impacts your KPI, and what you can do to optimize it.
That KPI could be employee absenteeism, customer conversions, customer satisfaction, booking cancellations, churn, cross-sales, or any of a million other metrics you’re interested in. In the background, artificial intelligence models measure and predict the KPI.
SAS is a popular programming language used for analytics, although it’ll take a while to learn, and something as simple as connecting a dataset can feel pretty unintuitive.
That being said, it’s a very feature-rich tool, and the SAS software suite offers point-and-click functionality to make analysis easier for less-technical users. Other technical analytics tools include Hadoop and Python.
Cognizant has made a name for itself in data analytics consulting and services. Keep in mind that large consulting firms often charge by hour, so costs here will far exceed using a self-service tool Apteo. IBM and Microsoft are other major players in this area.
Predictive analytics used to be limited to those with data science expertise, and was a rigorous process requiring data infrastructure and code-based modeling. Today, with tools like Apteo, anyone can implement predictive analytics on tabular data for a variety of use-cases, in close to real-time. Data-driven decision making and advanced analytics is now in the hands of business users, and not just technical experts.