In an ever-evolving digital world, customers are ready to switch to a new brand in the blink of an eye. In this context, firms must provide their customers with an outstanding customer experience every time.
In the past, it was easier to compete in a cutthroat industry because data was harder to come by. Companies are under tremendous pressure to streamline their operations to avoid failures with so much competition.
What is Predictive Analytics?
Predictive analytics is a specific type of data analysis that forecasts an event, either past or future, based on the patterns established in historical data. The process often involves statistical modelling and machine learning techniques to develop these trends.
Any organisation can accurately predict past and current trends to generate insights up to years into the future using predictive analytics tools and models.
Zion Market Research predicted a global market for predictive analytics would grow to about $10.95 billion by 2022. The report anticipates the industry will see a compound annual growth rate of around 21 per cent between 2016 and 2022. (Source)
There are many predictive analytics techniques that companies use to predict the future. It is rather simple, and these can be broken into three main types of prediction.
Forecasting a Trend
The first type is used for forecasting a trend in traffic or sales given ongoing data. This means taking data from the current period and working out trends and averages to predict future changes. This can be used to predict the change in sales revenue within a business or to forecast the distribution of traffic on a website.
Determining how likely an event will occur
The second type is used to determine how likely an event, say purchasing something, will occur. This is done by using historical data and working out probabilities, such as predicting the likelihood of someone visiting your website and buying something on their first visit.
Prediction by extrapolation
Another approach is the prediction by extrapolation. Here, we’re looking at a time frame beyond our current data and trying to predict what might happen—for example, taking monthly sales data and predicting how they’ll look next year.
Techniques, Tools, and Technology Behind Predictive Analytics
Predictive analytics is an advanced science with several techniques, tools, and technology to make it work. It is a mix of math, statistics, computer programming, and machine learning. It would be best if you had advanced skill sets to use predictive analytics properly.
The starting point for predictive analytics is data collection. It would be an arduous task to manage and use all the data manually, so we need to gather it from multiple sources like mobile devices, websites, social media platforms such as Facebook and Twitter, e-commerce sites, etc. With more information about your customers, you can offer products and services accordingly.
The first thing you can do to prepare yourself for predictive analytics is data mining. This is a technique that extracts valuable information from huge volumes of data through analysis. This is usually done by looking at previous events and finding patterns and trends within them.
Many software solutions have been designed to do this, and most of them are made to work on databases. They can use things like statistics, probability, data modelling, machine learning, analysis, and artificial intelligence to extract information from large amounts of data.
The third and final approach we will look at is clustering. Here, the data points are split into groups that contain similar characteristics. The aim is to find these groups by looking at historical examples to then be used to predict the future. For instance – analysing a set of customers’ spending habits to see how they can be grouped, such as people with a high amount of spending or those who don’t spend much.
You can use the data and the techniques we’ve looked at to predict the future. You may want to build a simple model that can give predictions based on historical data. Or, you may want to go with something more complex that uses other decision-making algorithms such as regression analysis or neural networks.
Decision trees are helpful predictive analytics techniques. The process plots the data and then tries to predict future outcomes using statistics, probability, real-life examples, and other factors such as machine learning.
You use this technique by first figuring out what decisions you want to make based on your data. Then, you look for patterns and trends in the data by analysing it from different angles.
Once you’ve found something, you can build a model to predict your future events, such as how likely someone will buy something on their first visit. Then, you use ‘regression analysis’ to predict these future outcomes with the data you have on your customers’ activity and behaviour.
There are many ways you can use predictive analytics. You can find out what customers are likely to buy next and discover their purchase intent. This is known as ‘sentiment analysis’. It uses Natural Language Processing technique to determine the general tone of text written online or on social media. For example, you could find out whether a customer is happy with the product they bought or not.
Sentiment analysis can be done by analysing text that has been posted on social media such as Twitter or Facebook, along with reviews and comments on e-commerce websites. It’s also commonly used to understand what journalists are saying about a company or brand by looking at what they’ve written online.
You can also use sentiment analysis to help you understand how customers feel about the information you are providing. For example, if a customer is leaving a negative review, it may be because of bad service, price change, etc. Or, if you’re getting positive reviews and comments, then it’s likely because of good service, product changes, etc.
Here are predictive analytics techniques to try out for yourself.
These include forecasting, which predicts future events by using time series analysis on historical data such as stock market data or weather patterns.
You can also use predictive modelling, which is the process of using a model to make predictions about things that have not yet occurred. For example, you can create models of customer behaviour to predict what they are likely to buy or when someone will be at their house by looking at historical data on past events.
This type of probabilistic approach uses algorithms to predict something about the future based on what has already happened. In a nutshell, you use historical data to build and train a model to predict future events. For example, you can do this with customer behaviour or financial markets.
Simple Statistical Modeling:
Predictive analytical techniques are designed to help make future predictions. For instance- predicting when someone is going to buy something or precisely what they will want to buy, for example. The best predictive analytics software uses five main types of models.
Neural networks are one of the most powerful predictive analytics techniques because they can be used to predict almost anything from customer spending to predicting stock market behaviour.
They let you use machine learning so that your computer can find patterns that humans would have trouble spotting. These are very flexible when handling large amounts of data and finding correlations, which is ideal for predictive modelling. However, they are very computationally expensive, making them challenging to run on large datasets.
This is another popular type of predictive analytics tool: logistic regression. This method lets you find patterns in your data by predicting customer behaviour based on past events. It uses a special algorithm to determine which of your predictive analytics strategies works best. This tool is ideal for binary outcomes (things that will either happen or not) and can be used for everything from predicting whether a house buyer will purchase to finding out what customers are likely to buy next.
Predictive Analytics: Deep Learning Application
We already know that self-driving cars are being tested on the road. These vehicles use cameras and sensors to collect data, including information about the environment around them. Then, they interpret this data to predict how they should react in different conditions.
For example, if there’s a cyclist ahead of the car, it will slow down, stop or steer out of the way. This is all thanks to predictive analytics, which put together an algorithm based on historical data to predict what’s likely to happen in different situations.
The Future: Smart Cities & Other Applications
Although they might not be widespread now, self-driving cars are just one example of what predictive analytics can do. Predictive analytics will be used in many other areas, such as healthcare and business intelligence, in the future.
As we move towards smarter cities, these techniques could help improve things like public transport systems by making them more efficient. This could involve planning bus routes based on past data or preventing traffic jams by predicting where they may occur.
Predictive analytics is expected to grow even more in the next decade, showing how important this technology can be.
Until recently, most businesses would use customer surveys or phone calls to collect data about their customers’ behaviour. Thanks to big data and predictive analytics software, this is now changing, which makes it easier to collect data and use it for other purposes.
Predictive analytics is a rapidly growing field that provides clear benefits to companies that practice it. Do you have any other Predictive Analytics techniques that you would like to share with us?
Happy to hear from you.