Predictive modeling is a way of conducting data analysis, often with machine learning, to understand and forecast the probability of future events. It is important in a variety of fields, including finance, marketing, sales, healthcare, and more.
For example, predictive modeling can help with everything from determining the likelihood of a hospital patient experiencing a heart attack to predicting whether a customer will repurchase an item. The idea is to use data points from the past to train a predictive model so you can predict what might happen in the future.
Predictive modeling as a part of data analytics can take many different forms, depending on what type of data is available and what type of prediction is being made. It can be applied to classifying text as positive or negative, forecasting revenue, or even predicting employee retention.
Until recently predictive modeling was a pretty complicated undertaking relegated to data scientists. You would start with a historical data set, do a linear regression to identify weightings of input variables, and then develop forecasting algorithms (or build decision trees) based on key predictors of outcomes.
Fortunately, the advent of artificial intelligence has made predictive modeling more accessible than ever. Deep learning neural networks can now easily parse complex sets of big data, identify patterns and outliers, and quickly predict future outcomes from new data. No-code platforms are eliminating the need for advanced technical degrees or data science teams to do this data mining - now every business user and subject matter expert can take advantage of the power of predictive analytics.
Because of the wide range of predictive modeling use-cases, it’s an important tool for businesses to empower employees in any business function. Let’s go through a few practical examples.
Employee turnover used to be a black box. Who will quit next? When? Why? These questions can now all be answered with predictive modeling, which learns patterns in historical data to understand which employees will be likely to quit, and which will stay.
Using Akkio, you can upload a historical dataset of employees, including a column on whether or not they quit, to train a predictive attrition model. You can use numerical data, like ratings, categorical data, like their department, and even time series data, capturing their employment journey over time. You can then deploy this model to predict whether new employees will be likely to quit and reach out to those with a concerning probability of quitting for a 1:1 discussion.
You can read our full guide on predictive modeling for attrition prediction here.
Every company is different and has different causes of churn.
A series of ConversionXL case studies, for instance, shows that some companies experience churn due to poor customer onboarding, others due to a lack of customization, some due to infrequent communication with customers, incompleted setup processes, and more.
There’s no telling exactly what the causes of churn are for your product or service, but we can use predictive modeling to accurately pinpoint why users drop off, given a historical customer dataset.
Using Akkio, you can upload a historical dataset of customers, including a column on whether or not they churned, to train a predictive analysis model for churn. You could then deploy this model to predict whether new customers will be likely to churn, and reach out to those with a concerning probability of churning with a special discount.
You can read our full guide on this here.
The sales funnel is a process with three stages (or four, depending on who you ask): prospecting, funnelling, and closing. It is designed to turn prospects into customers.
The first stage of the sales funnel is prospecting. This step is about discovering and reaching out to potential customers. The goal is to get these potential customers to enter the funnel. This means the person will take some sort of action, such as filling out a form, watching a video, or downloading a product.
The second stage is funnelling. Once someone has entered the sales funnel, they may become anxious or disengaged due to not knowing what to do next. This stage of the sales funnel takes the prospect's information and moves them into a different phase of the sales process. They may be asked to fill out a survey or sign up for a demo.
The third and final stage is closing. This stage of the sales funnel leads the prospect to take an action that will lead to a conversion, such as signing up for an account or paying for products.
Predictive modeling techniques can optimize many aspects of the sales process. One way it can help optimize the sales process is by anticipating the needs of customers before they do. AI can analyze data about previous customers and predict what they will do in future situations. This means that if, for example, many customers abandon their shopping carts when they are asked for their billing information, AI could suggest that it be hidden from view and only shown when it's necessary.
You can use Akkio to make predictions on historical sales data and deploy a machine learning model to focus your sales efforts on the leads most likely to convert. Read our full guide on sales funnel optimization to learn more.
To improve in any area of business, you need to first decide upon and track metrics. In customer support, common metrics include First Response Time, Customer Retention Rate, Customer Satisfaction Score, Average Resolution Time, Net Promoter Score, and others.
While data can be helpful, make sure to take action on these metrics and set short and long-term goals to improve them.
For example, a long First Response Time may be a sign that it’s time to add more resources or provide a supplementary automated response system to get an instant first response.
More complex metrics can be optimized with machine learning. By uploading a historical customer support dataset to Akkio, you can select the KPI you want to optimize and AI models will automatically allow you to predict the KPI and see what its main drivers are.
As we’ve highlighted, there are around 40 billion annual credit card transactions in the US. Detecting fraud in these transactions is like finding the veritable needle in the haystack, but it’s an absolutely vital task, as payment card transaction fraud costs nearly $30 billion a year, with more than a third of those losses attributable to the United States.
Fraud is on the rise during the pandemic and new types of fraud are constantly emerging, such as synthetic account fraud, in which false credit accounts are made and abandoned after withdrawing a large amount of credit.
Detecting fraud manually, across billions of transactions, would be an essentially pointless task. This is where using machine learning for data mining comes in, as it can quickly scan massive amounts of historical data to uncover patterns in fraud and use that pattern recognition to uncover fraud in new financial transactions.
With Akkio’s proprietary AI training process, you can build and deploy models to predict fraud in minutes. To get started, you can sign up to Akkio for free. Every machine learning task, including fraud detection, requires a historical dataset to teach the model how to recognize patterns for that task.
In this case, we’ll use a Kaggle dataset of credit card transactions, with around 285,000 rows - relatively few of which are fraudulent. The true financial information is anonymized to protect the privacy of the users who created this real-world data, but we can still use the data to fuel a machine learning model.
We’ve already included this specific dataset on Akkio as a sample for demonstration purposes, which you’ll find on the homepage, titled “Credit Card Fraud Demo.” You can use it to create your first prediction model.
Predictive modeling empowers any team to improve their KPIs by taking a data-driven approach to decision-making.
As we’ve explored, predictive analytics models can help HR teams reduce attrition, help sales teams close more deals, help finance teams reduce fraud, and more. It’s now easier than ever to put machine learning to work for you.