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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.
Equipment failure can cause all sorts of issues for businesses, from production line stoppages to safety hazards.
Predictive maintenance is a process of using machine learning to predict when equipment is likely to fail, so that it can be repaired or replaced before that happens. This helps businesses avoid the cost and inconvenience of unplanned downtime.
To do this, you need a dataset of previous equipment failures, such as with information on the equipment, the environment it was in, and what happened leading up to the failure. With this data, you can train a predictive model to identify patterns in failures and use that pattern recognition to predict future failures.
You can then use Akkio to make predictions on new data and identify which pieces of equipment are at risk of failure.
Appointments are often booked and then cancelled, which can be costly for businesses.
Predictive models can be used to reduce the number of cancellations by predicting which appointments are likely to be cancelled. This information can then be used to take action to prevent the cancellation, such as sending a reminder email or text message.
To do this, you need a dataset of past appointments, which may include information on the date and time of the appointment, the location, the type of appointment, and whether or not it was cancelled. With this data, you can train a predictive model to identify patterns in cancellations and use that pattern recognition to predict future cancellations.
You can then use Akkio to make predictions on new data and identify which appointments are at risk of being cancelled.
A business’s ability to increase customer lifetime value (LTV) is essential to its success. Historical customer data can be used to train a predictive model that will predict how much a new customer is likely to spend over their lifetime.
This model can then be deployed on new customers, giving you a real-time prediction of their LTV. With this information, you can make strategic decisions on where to allocate your resources to most effectively increase LTV. For example, you may want to focus your marketing efforts on high-LTV customers or give additional support to low-LTV customers who have a high chance of becoming high-LTV customers.
In any business, it costs more money to acquire a new customer than it does to sell additional products or services to an existing customer. Thus, it’s important to understand which products or services your customers are most likely to buy so you can focus your upsell and cross-sell efforts accordingly.
A predictive model can be used here as well, learning from historical purchase data to predict what items a new customer is most likely to buy. This prediction can then be served in real-time, allowing you to make targeted recommendations at the moment when a customer is most likelyto buy them.
We often think of intuition as that "gut feeling" we get about something. But when it comes to making decisions, relying on our gut can often lead to suboptimal results. This is where machine learning can help.
Machine learning is a form of artificial intelligence that allows computers to learn from data, identify patterns, and make predictions. This ability to glean insights from data can be immensely helpful in making better decisions - especially when there's a lot of data to consider.
For example, imagine you're trying to decide which inbound leads to prioritize. You could look at each lead's individual characteristics and try to make a decision based on your gut feeling. Or, you could use machine learning to analyze all of the data and identify which leads are most likely to convert.
In this way, machine learning can create better intuition by identifying patterns that we might not be able to see ourselves. And as more data is collected, the insights gleaned from machine learning will only become more accurate. So if you're looking to make better decisions, don't rely on your gut - let machine learning give you the insights you need to succeed.
Businesses lose $1.6 trillion a year to churn. That's over $50,000 a second.
And a lot of that customer churn is due to poor decision-making. In fact, 85% of consumers say they've churned because of a bad customer service experience.
These are just a couple examples of how critical moments can easily become disasters when intuition fails us. We often think we know what customers want, but we're really just guessing. And when it comes to making decisions that could make or break your business, relying on guesswork is simply not good enough.
Machine learning doesn't rely on guessing – it relies on data. By analyzing huge data sets, machine learning can identify patterns that humans simply couldn't see on our own. This means that machine learning can help us make better decisions, based on actual evidence rather than gut feeling.
For example, imagine you're trying to decide which products to stock in your store. You could base your decision on your personal preferences, or you could use machine learning to analyze past sales data and identify which products are most popular with your target audience. stocking the items that are most likely to sell will help you avoid costly inventory issues down the road.
In this way, machine learning can create better intuition by identifying patterns and relationships that we wouldn't be able to see ourselves. And as more data is collected, the insights gleaned from machine learning will only become more accurate. So if you're looking to make better decisions, don't rely on your gut - let machine learning give you the insights you need to succeed.
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.