While the term ‘predictive analytics’ was born in 2003, the process itself is a lot older than we realize. From our day-to-day life to business proceedings, we’ve been using predictive analytics for a long time.
But in today’s competitive landscape, the ability to predict events and trends in the future is almost necessary for survival. With so many companies fighting for the spot at the top of the ladder, predictive analytics is now more important than ever.
From buildinghow competitive advantage to maintaining company health, predictive analytics is a vital part of any business’s decision making.
Predictive analysis is the process of using historical data to forecast future outcomes. This is something we’ve been doing since forever, and we do it on a daily basis!
If your company supplies energy and you notice your customers using more energy during winters, you’d keep that in mind for the next winter season. This is predictive analysis.
Predictive analytics nowadays, however, is often used in a technological context. With the help of statistical models, data mining techniques and machine learning, you can find patterns and trends in the data you hold, and use these to identify the risks and opportunities available to you.
In a business context, this means everything.
For a business to move ahead of its competitors, it needs to make all sorts of decisions. Not only do these need to be informed, but they also have to be at the right time. Wait just a bit too long and you lose your competitive advantage!
Nowadays, AI and machine learning are the main source of information to support these decisions. Dealing with small amounts of multidimensional data is difficult if you’re doing it without a spreadsheet or BI software, but as the data size grows larger, it becomes near impossible.
Without predictive analytics, you wouldn’t get that deeper look into the trends present. You’d be operating on a surface-level analysis and guesswork.
Predictive analytics isn’t actually that easy. In the past, Excel sheets and relational databases were enough to hold all the data we needed, but as technology grew more complex, so did the data generated.
As of 2023, Facebook alone generates 4 petabytes of data per day, and there’s no limit to what kind of data it is either. From text to images and video, all of it is equally important when it comes to making predictions and business decisions.
But there are different forms of data used.
Structured data is what we often think of when we imagine ‘data’. Rows and columns of numbers and more numbers. Past sales, financial records, inventory logging – all of these are structured forms of data. They’re often stored in relational databases (that is, in tables) and the structure does make them easy to analyze.
Unstructured data is free-form and tends to be of multiple types. It’s also significantly harder to work with because it’s so unstructured, but is also the most abundant type of data generated. In fact, the estimate is that about 80% of all data being generated is unstructured.
Unstructured data includes text data, such as reviews, social media posts, company emails, etc. To use these for predictive analytics, you have to first prepare it using NLP models and Text Analytics.
Images, audio and video files also fall under unstructured data. These require processing through deep learning models before they can be used for analytics. It takes quite a bit of time, but audio-visual data can also hold a whole bunch of insights that you’d be missing out on.
Dark data is the name given to the data collected by businesses that don’t have any meaning or value assigned to it. In fact, about 50% of the data owned by businesses is dark, but that doesn’t mean that there are no insights to gain from it – just that the process hasn’t been carried out yet.
For example, CCTV footage is generally used for security purposes, but think about how much information you can get on consumer behavior by watching them as they shop.
The main challenge with dark data is knowing how to use it for analytics. There are plenty of ways to dig into it and make sense of it, but to do this, you need to know how and why you’re digging into it in the first place.
As predictive analytics becomes more important, AI tools for prediction also become more commonplace.
AI is rather vaguely defined, but generally used to describe machines that carry out actions which normally require human intelligence. For the most part, predictive models use machine learning or deep learning.
Classification models are quite simple, and can be done with machine learning techniques. Classification models put data in categories based on what it learns from historical data. For example, “Is this a fraudulent transaction?” is a classification problem. The model ‘classifies’ the data instance to fall within one of two classes – yes or no.
These types of models calculate probabilities for each class, and make predictions accordingly. Classification problems can be binary (with two classes) or multiclass.
Clustering models sorts data into groups based on similar attributes. This is commonly used for customer segmentation, so businesses can tailor their strategy for each group.
While clustering isn’t a predictive model in itself, it is an important preprocessing step upon which predictive models can be applied. When you have similar types of data instances combined into groups, it becomes much easier to predict how each group will behave.
Forecasting models are most widely used in predictive analytics. These provide a metric value prediction – that is, estimating a value for each new data instance based on new data.
In business, forecasting will use historical data – that is, future behavior is predicted based on how something has behaved in the past. For example, if a business wants to predict sales for product A, it’d make sense to look at the previous sales value for the same product.
If sales tend to rise during a specific season every year, it’d be reasonable to assume that they’d rise during the same season next year as well.
Outlier models are centered around picking out anomalies in data, rather than on the majority like most other models do. Outlier models are necessary in determining unusual instances that could be potentially harmful, such as fraud in credit card transactions, or the instance of heart disease in a group of patients.
These models help because they learn the attributes of what constitutes an anomaly, and pick these attributes out when new, unseen data comes in.
Predictive analytics isn’t limited to any single industry – its use is widespread across all types of businesses, in any sector. From E-commerce to telecommunications, it’s necessary for any business to incorporate analytics into their strategy.
The use of predictive analytics in E-commerce has come to redefine the way the sector works. In fact, with COVID-19, online sales shot up, leading to a 76% YoY increase in sales. With so much business lying in online sales, using tools to improve these services becomes even more important.
Predictive analytics helps businesses personalize their approach towards customers, turning the experience into a proactive one.
Analytical models, for example, can help you predict your customers’ buying behavior. These models look at data on what products get added to carts, common search terms, etc. As a result, they determine what you can expect these customers to do once they get to your store.
By doing this, you can make them return, by catering to their needs. You can also assess customer satisfaction rates by looking at reviews, repeat purchases, etc. With satisfaction data, you can also determine the likelihood of churn, and take steps towards retention.
Going a step further, you can use clustering methods to group your customers into segments and predict the patterns in their behavior, which you can then use to determine their needs.
By providing value to your customers before they know themselves that they need it, you can make them stay, keep them happy, and boost your business results.
Customer behaviour predictions are also crucial from a marketing standpoint. Just like with E-commerce, clustering methods can help with customer segmentation.
Using these segments, you can determine whether a new lead is likely to become a customer or not – this is called lead scoring, and a Forrester study in 2015 determined this as one of the top three uses of predictions in marketing.
Taking a step further, you can use predictive analytics to determine what kinds of ads to show specific types of customers, to increase the likelihood of conversion, and personalize the customer experience even more.
From a logistics standpoint, predictive analytics helps with challenges like determining the optimal inventory level considering expected demand and storage cost. By predicting future sales, you can minimize supply chain and inventory costs, as well as the space taken up by stock, while keeping your customers satisfied.
Going further, you can also determine the shipping info – from the likelihood of having to transport products to a specific location, to predictive route planning to determine the best possible way to ship your product while minimizing transport costs.
Outlier models in particular are highly useful for fintech. Identifying fraud helps companies and their customers keep from falling into losses.
Predictive analytics is also used in fintech for determining the credit scoring of customers, and whether or not to provide loans to applicants.
It also helps with risk management when it comes to investments, and streamline compliance by detecting patterns that lead to non-compliance and send out alerts.
The telecom industry is one of the most data-intensive, so it makes sense that the use of predictive analytics would be essential here.
From determining network capacity planning and using real-time data to optimize networks, predictive analytics is pretty much a key aspect in this industry. Speed and quality of connection is everything in the telecom industry, so these two aspects are crucial.
There are even further uses when you consider the supportive functions within the industry, such as marketing, improving customer experience, credit scoring and fraud detection.
Large corporations use predictive analytics almost religiously, and it’s easy to understand why.
For example, Netflix uses predictive analytics models to personalize recommendations to each individual, with auto-generated thumbnails and finding content similar to what the user has watched in the past. As a result, the retention rate is enormous in comparison to other streaming services, with its market cap reaching $83.27 billion in May 2022.
Similarly, Amazon analyzes your browsing and buying history (this becomes historical data) to predict future buying behavior. Therefore, the products you get recommended are those that you are actually likely to buy. It’s no surprise that the company is the largest e-commerce platform in the world. Standing at a $856.95 billion market cap at the end of 2022, Amazon’s success is attributed to the organization’s focus on data and numbers.
There are six steps to effective predictive analytics.
The first thing you need to do is define your goal. What are you trying to do? What are you trying to predict and why? Unless you know why you are doing what you’re doing, you won’t actually get anywhere. Are you trying to predict sales for budget allocation, or to determine demand?
You need to be clear about your goals before you take steps.
Now, having identified your goals, you have to collect the relevant data. If you’re trying to predict sales for product A, you don’t really need the data on product B. But if product A’s sales are proportional to that of product B, then you do need product B’s data as well! It’s important to understand what type of data is needed, and how other types can affect it.
Remember, in predictive analytics, as long as your data is relevant, more is better. The more you have, the greater your predictive accuracy.
Now you need to prepare your data. This includes things like cleaning and preprocessing. Removing duplicate values and outliers, scaling different dimensions of data to bring them within the same range, and other such aspects of data preparation so you can move to model building.
Now, given your goal, your desired output and data type, you build a model. If you’re looking for a yes or no answer, for example, a classification model would work. For forecasting problems, you want a forecasting model, etc. Once you’ve created your model, you have to divide your training data into training and validation, to make sure it’s working properly.
Based on your validation results, you may want to go back and tweak it.
Now, it’s time to implement your model. At this stage, you’d be using new, unseen data that you can’t cross-validate. These are the results you’d be using to make your business decisions, which is why it’s crucial to make sure the model has a high rate of accuracy without overfitting or underfitting.
Your predictive model is likely not going to be of one-time use. As you get more data, you have to feed this into the model and retrain it to increase its accuracy even further. Keep monitoring your model and make improvements as the need arises.
To summarize, predictive analytics is the use of different types of data to make predictions about the future. Based on these predictions, you can make various business decisions that will help your business improve and progress.
Data science experts at Algolytics bring the Automatic Business Modeler as a solution: a single tool that carries out various tasks to help with business decision-making.
From churn prediction to maximizing ROI and minimizing loss, the ABM allows you to build accurate predictive models without having to worry about all the programming behind it. But it doesn’t stop there.
With all the different factors affecting how your customers behave, it’s difficult to identify which of them play the most important role. The ABM also highlights these factors, and uses only them to ensure high perfomance of your predictions.
Schedule a demo or sign up for a free trial and check how our solution can help your business grow.
