Ever wondered why people tell you to trust your gut, and not some other body part or organ? It's because our gut is where tons of hormones are being nurtured and distributed throughout the body. Since they affect what we do, people often call the gut the Second Brain.
Now that's fun trivia, but what does it have to do with business? Well, everything. While many successful entrepreneurs claim to work based solely on gut feelings, it isn't something you can pitch to investors, partners or suppliers. Not in this economy.
They're looking for something more feasible and reliable: data, to be specific—and there's more of that than you might think.
Turning historical data into future insights
We can't exactly predict the future—yet, but we're getting amazingly close, thanks to concepts like predictive analytics.
Data is becoming an important part of business. Not just in long-term decisions, but even in the day-to-day. Data helps you predict what is going to happen, which in turn helps you prevent, and optimize.
Think data analytics for predictions is only for big businesses? Think again. The tools you need for predictive analytics are becoming more mainstream, accessible, and affordable.
If you want to start supporting those gut impulses with real data, now’s the time to dive into everything predictive analytics has to offer. Let’s get started.
What is predictive analytics?
Predictive analytics uses AI to analyze historical data to forecast future activity, behavior and trends. It uses algorithms that are constantly being trained. It combines that with statistical analysis techniques, analytical queries and machine learning to create predictive models that become more accurate over time.
Think of sales numbers, revenue, but also demographic data and any other stats that are relevant. Combining these in a creative and smart way, paints a pretty clear picture of what is happening in a market.
Sentiment analysis: don't listen to your gut, listen to your customers
But predictive analysis doesn't just look at numbers. More often than not, the real secret to business success is in feelings and sentiments. That's why sentiment analysis is an important part of predictive analytics.
Sentiment analysis uses AI to read any given piece of text, and extracts the most important sentiments from it. Check out Levity’s article on text analysis to find out how it works exactly!
Combining this with the other data you find, doesn't just help you see what is happening—and what might happen again—it will also help you understand why.
How accurate is predictive analysis anyway?
We’re not saying it’s a crystal ball that never fails. But due to the lack of anything better, and the fact that the AI and algorithms are getting better by the minute, predictive analytics is the second-best thing after that crystal ball.
The advancements in the technologies that are used, also mean that predictive analytics can be used in more and more industries. This also benefits its accuracy, because there are more scenarios to start learning from, making the AI better and better.
Where can you use predictive analytics?
You might not think about it, but you are the target of predictive analytics, right at this very moment.
Remember that show you bingewatched on Netflix yesterday? It was in your Recommended for You list, and said there’s a 98 percent chance you liked it. Fast-forward to this morning, and you finished a whole season.
Netflix is one of the many companies using AI-powered algorithms to make predictions based on your watch and search history, demographics, ratings, and preferences. Their predictions show with 80% accuracy what show or movie you'd like to see next. Take that, Blockbusters.
So, if there is historical data for it, and you plan to repeat processes in the future, you can use predictive analytics. From allocating supplies, to knowing how much stock to order, and even setting up effective marketing campaigns: here are some areas you could apply predictive analytics to.
Marketing campaigns: data-driven customized marketing campaigns take customer behavior into account, so you can be at the right place, at the right time.
Hiring and employee retention: take a look at the data from the past and see which employees thrived, and what they had in common, data-wise. This will enable you to make smarter hiring decisions for long-term success.
Personalization and recommendation: if you know that a certain customer historically tends to put some extra items into their cart on their way out of your web shop, you can get in front of that and leverage that knowledge with better deals.
Improving operational efficiency: we all suffer from supply chain issues, but you can still try to predict the larger waves and trends to optimize logistics, inventory and demand-supply.
Risk management: from your own financial decisions to those of your customers: if you get insight into them, you can make better decisions
Fraud detection: hacking and fraud is all about patterns, and recognizing when something is off. Use historical data to find out what ‘normal’ looks like, so you can be warned when the pattern breaks.
How to use predictive analytics
If you're already breaking a sweat thinking about all the math that will be needed for this, calm down. There are—luckily - plenty of automated machine learning (AutoML) tools out there that will help you get started with predictive analytics.
You don't need to have a Jimmy Neutron on your team, either. What matters most in predictive analytics is that it is set up right, and that the tool is running properly. For the initial set up and any changes you want to make, it probably pays off to hire an external data scientist or data architect.
What also helps is understanding more or less how predictive analysis works, so you can hire the best person for the job. We’ll walk you through some of the main challenges and the process those smart machines go through.
The main challenge is creating the right model with the right predictors. There are always multiple answers to those questions, and iteration is needed to optimize your prediction model.
The 8 steps of predictive analysis
Generally speaking, there are five steps to go through for successful predictive analysis: Defining the requirements, choosing and exploring the data, creating the model, deploying the model and validating the results. Here’s what each of them entails, in short.
Define the Problem Statement and what data sets you will use:
Defining the requirements is all about selecting the target variable. Aka: what do you want to predict? This will affect what data sets you will be using in your analysis.
Data extraction:
Get your historical data set ready. Extract as much data as you can, from as many sources you have. Feed that algorithm what it needs to perform!
Cleaning up your data
You don't want the algorithm to use or confuse unnecessary data, so when you have everything ready, start cleaning erroneous, redundant and duplicate data from your data sets.
Data Analysis: finding patterns
The exploration of data involves analyzing it thoroughly to identify important patterns and trends. But—you might also find trends or patterns that don't seem relevant now. These could be important later on.
Building the right Predictive Model:
Using various algorithms the predictive models are built. This is done based on the patterns observed.
Validation: is this true?
You can't just assume the model works. There will be a training data set. The data in here will be used to train and teach the model to find what you're looking for. Then there’s the test set. This will validate that model.
Deployment: getting to work
In the deployment phase the data is used in a real environment, ready to help you make better decisions.
Model Monitoring: getting better over time
The idea is that the models get better and more accurate over time, so it is crucial that they are monitored and fine tuned regularly.
Ready to predict your next smart step?
If you want to start fueling your gut decisions with data, find out more on how digital marketing data can be used to improve your business.