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What is Predictive Analytics?


Predictive Analytics.



Who would have thought that the best way to predict the future would have nothing to do with magic but science?

Predictive analytics are artificial intelligence tools that manage to "guess" the behaviour of your users or the market by combining historical data with statistical modelling, data mining techniques and machine learning to identify future risks and opportunities.

According to reports from the Business Intelligence Market Study 2021, its effectiveness is such that technology, business services, consumer services, and manufacturing industries reported the highest increases in planned adoption of business intelligence tools. Facts & Factors informs us that the global market for Predictive Analytics is increasing at a CAGR of 24.5% and is expected to reach a value of 22.1 billion by 2026. Statista informed us that by this year (2022), this market would get 11 billion dollars in annual revenue as more companies start incorporating it into their strategies.

Meanwhile, in Finances Online reports, we found that 63% of companies alleged that data analytics improved their efficiency while 57% say they achieved more effective decision making. 

Predictive Analytics is the branch of analytics that can help you make predictions about future events. It is done through data, statistical algorithms and machine learning techniques. In this article, we'll show you how it works, its benefits and all the trends for this AI tool, so keep reading and find out everything about it.

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How does Predictive Analytics work?


Before using PA, you must build a mathematical structure called the predictive model. This tool helps you determine the characteristics you'll evaluate based on historical data, target outcome and information about the situation. In a few words, the predictive model replicates your chosen position and scales it down to the most meaningful data. This process consists of 3 steps:


  1. Define what you'll predict: beyond risks and opportunities, you can preview any other factors within your company with PA like customer churn, what products customers will buy, the following trends within your niche and many more. Nevertheless, let's go into more specific industries. We could find out that you could forecast future cash flow for finance companies, determine staffing needs if you work for entertainment or hospitality and even prevent malfunction for manufacturing.
  2. Collect the data: In this step, the trick is to make the most mining you can; the more you do it more precise your prediction will be. For this task, we recommend using management software to automate this process.
  3. Train and train again: test your model and evaluate its results to make them more accurate for future analysis. PA applications can self-improve as you keep using them.


Source: Youtube.


How to build a predictive model?


Models are the foundations for Predictive Analytics because they help you turn historical and real-time data into strategies to improve your business. The most common predictive analytics models are:


  • Customer Segmentation: It groups customers into similar buying behaviours and other aspects.
  • Customer Lifetime Value Model: It identifies customers with more potential to invest in your products and services.


To train your AI, you must set a "ground-truth definition," which is a dataset for an expected result in the machine based on previous experiences. You have to set clear parameters to define what you want to predict. For example, if you discover when buyers tend to purchase more, you could specify hours or the days they do it the most.

Then it would help if you had a  train and test model to evaluate how well it's predicting. In the end, when you have mastered your model, it will be able to precisely predict the best days and hours for your audience, similar to the insights you can see on social media platforms like Instagram or Facebook.

One of the most significant benefits of modelling is its scalability. It can extract predictions from millions of data points, something that would be exhausting and almost impossible for us humans.


Predictive Model.


Top Predictive Analytics models


Depending on your business, you can choose a different model. These are powered by different algorithms you can use for other cases. You can take advantage of this technological tool by selecting a suitable model. In this part, we'll show you the top predictive models you can use for your business.


Classification Model


This model may be the simplest one among all the others, but we say it positively. The model puts data in categories based on what it learns from all the historical data.

When you use this model, it is better to put yes or no questions so it can get you valuable insights to make an action. For example:

  • Is this customer satisfied with their order?
  • Is this customer likely to abandon our brand?
  • Is the product selling well?

With so many options for questions, this model can be used for a wide range of industries. Besides, it has the potential to be re-trained with new data.


Clustering Model


The clustering model consists of creating subgroups within your audience with similar characteristics and then redirecting more personalized campaigns to all of them.

For example, if you have a clothing store selling pop culture items like rock band shirts or anime character shirts (Japanese cartoons), you could regroup them into different groups to make your strategy more precise. Even within your subgroups, there may be smaller subgroups because, within rock shirts, there may be people who like The Beatles while others like Guns' N Roses.

Whatever the case, this model saves you the daunting task of sub-dividing each sub-group into your audience.

Another excellent use for this modelling technique is grouping loan applicants on "smart buckets," bassing on characteristics like areas with a high volume of crime or benchmarking SaaS.


Forecast Model


According to Insights Software, Forecast is one of the most widely used and helps businesses estimate new data values based on your company's data history. It could help your business predict:

  • How many customers could convert within the next week?
  • How much inventory should your e-commerce have to keep demand on a specific holiday?

The model can get the answers to this prediction by studying specific factors like a pandemic going around, if there are significant events in your country, or even the weather.


Outliers Model


An Outliers Model can help your company find irregular data entries in your system. The model can find estrange figures by itself or with other characteristics. For example, it could help you identify fraud or a product failure in your business.

According to Insights Software, this model fixes well for predictive analytics in retail and finance. For example, if you have a fraudulent transaction, the system could identify it, giving you the location, date and purchase history.


Time Series Model


The time series model predicts future scenarios based on past events.

History tends to repeat itself, and what happened last year could be repeated this year under normal conditions.

In this way, based on the history of your company, this model could predict how many users would visit your page in the next three weeks.

This model is widely used to predict how the market will behave during the holiday seasons since these dates sales tend to increase the most.


Source: YouTube.


Predictive Algorithms


We can separate the predictive analytic algorithms into machine learning and deep learning.

The first one - machine learning - consists of structured data that we can check in a table. This type of algorithms group linear and nonlinear varieties. The linear combinations can be easier to train, while nonlinear is better for problems you could face.

Meanwhile, deep learning is a subset of machine learning that allows machines to have the ability to recognize objects, images or videos through their characteristics, which we have already downloaded through hundreds of examples for them to detect.

Although machine learning needs some manual steps to be set up, its benefit is that it can be trained over time. While deep understanding needs too much information and examples to finally detect the objects, needing high-performance computing and a few thousand images to get reliable results.


Predictive Algorithms.


Benefits of Predictive Analytics for business


Now that we covered the basics of predictive analytics, it is a good time to mention the benefits it will bring once you include it in your company.

Let's take a look at some of these.


Predict customer churn


The predictive models can use data to learn what attitudes customers tend to take when they're unhappy and want to churn. To avoid this, the AI can flag these customers to help you take prompt actions to retain these customers.


Personalize your marketing campaigns


Nowadays, using a strategy for your entire demographic is illogical and harmful. You must understand that although everyone follows the same company, not everyone is looking for the same products.

Returning to the shirt store example. If a customer who doesn't like Anime or Comics starts receiving emails about new t-shirt offers of Marvel Heroes or Dragon Ball, this would not only be wrong but could also detect it as spam and start to move away from your brand.

With all the personalized data that predictive models give you, you should take advantage of it to segment and create strategies that satisfy each of them, generating better customer service, a point we will touch on below.


Improve customer experience


As a business owner, you should want more customers, which is fine, but you should also know that the most important thing for the stability of your business is to keep customers who are already loyal.

For this, predictive analytics help you adapt to their preferences by giving you all the information you need about what they consume when they consume it and even how they do it.

That is why by customizing the campaigns, you will be able to keep them happy with what they are used to, and in case they want to change their preferences, the technology will also warn you.

But, perhaps the most exciting thing is that predictive models can also help you deal with them directly. This technology can communicate with your users through text prediction or well-known chatbots, even detecting feelings or other inflections. This provides an assistant who can be attentive to your audience 24/7.


Risk Reduction


Have you paid your credit? Predictive analytics can track a new customer's credit score and let you know how trustworthy they are. Another risk-related use is for insurance claims and collections.


Fraud Detection


One of the true strengths of PA is that it can recognize patterns, but what could this do for your company? Let us explain.

By detecting strange patterns, you can discover any fraud or violation of your business. For example, if a bank user buys in other continents in a short period, the institution could hold the account to ensure it is legal.  


Benefits of Predictive Analytics.


Start Using Predictive Analytics


Step One:


Predictive Analytics can help you solve problems; you have to stipulate which one you want to solve. Then ask yourself these critical questions:

  • What do you want to know about the future based on past experiences?
  • What do you want to understand about your customers?
  • What actions can you do to solve the problem?


Step Two:


You need to complete a lot of data from your customers, get track of transactional systems, data collected by sensors, call center notes, weblogs, leads and every place to know how your customers behave. Find somebody who knows about data and understands the problem of your company. This may be the most time-consuming part, but it is crucial to train the model and let it get precise predictions about it. 


Step Three:

Your predictive model has begun!

Although there is more easy-to-use software for analytics nowadays, you will still need a professional who knows data analysis and can keep refining your model to come up with the best performer.

Information Technology (IT) is another aspect you have to cover. A specialist in this area will help you deploy your models; this means putting the models to work on your chosen date.

Now you will get your results!


Final Step:


Build a team that can help you improve your model.

It would help if you had someone who understands the business problem, and this person must know how to prepare data for analysis.

As we said before, an Information Technology expert is needed for model building and deployment and an executive who can sponsor your analytic studies.


Predictive Analytics Process


Predictive Analytic Techniques


Predictive Analytics uses old results to train a new technique to predict values from new data. If you want to calculate revenue, these models will provide results in predictions based on a set of input variables.

Predictive Analytics differs from descriptive analytics because the explanatory model can help you understand what happened with critical relationships in your business and why this happened.

There are two predictive techniques:


Classification: 


This one is used for predicting class membership. This model is helpful if you want to classify whether a customer is likely to leave or has terrible credit risk. These models are in the form of 0 or 1. If the result is one, this is the result you are targeting.


Source: Youtube.


Regression:


This one is used for predicting a number – for example, if you want to know how much revenue a specific customer will generate over the next year.


Source: Youtube.


What industries use Predictive Analytics?


Looking at this technology's features and functions, it isn't surprising that all types of industries adopt it. So let's examine how predictive analytics empowers different types of companies.


Banking


In the banking industry, technological advances have allowed predictive analytics to reduce lending risk using customer data. This has to do with the credit score, which evaluates the possibility of making a loan depending on the client's history and payment of accounts throughout their stay in banking institutions.

 On the other hand, we know that cashless transactions are more efficient than payments. Still, they also come with a problem: increased fraudulent activities like phishing, application fraud, identity fraud, and card skimming. However, as we mentioned earlier, this technology can detect "suspicious" operations and thus prevent possible fraud.

On the other hand, predictive analysis allows banks to segregate between risk-free customers and those who do not evaluate how their debt payment is, optimizing the collection process for those who are delinquent.


Retail


As a retailer, you must keep track of many products, and predictive analytics can help you with this. Having control over your stock is crucial for having a great month of sales or a sales deficit.

For this, Carl Hildebrandt, Epicor vice president of Product Management, thinks that predictive analytics can help retailers by ranking a product's performance based on factors like sales margins and the number of units sold. In the article, he explains:

"While forecasts are predictions based on actual sales history, using these two capabilities together help retailers stock items more optimally based on overall store profitability and revenue."

This is great for the business and customers because forecasting tools can improve your customer satisfaction by predicting balance and inventory.

In the same way, this software technology will allow you to be one step ahead of events in society, such as a change of season or festive dates, to enable you to make the appropriate decisions to take advantage of them. Speaking of predicting, this technology is beneficial for retailers to be one step ahead of the trends for their niche.


Industries that use Predictive Analytics.


Government and Public Authorities


Nowadays, governments use predictive analytics to improve service and performance. They can detect fraud and understand the behaviour of their citizens.

One of the actual uses of this technology is to enhance cybersecurity.


Healthcare


Maybe the healthcare industry is one of the most benefits from predictive analytics because it can help them understand the history and illnesses of their patients. With the models, they can provide an accurate diagnosis based on past data from the patient.

Besides, with the help of health factors, technology can help professionals detect the root cause of a disease, providing in-time analytics to provide treatments in an early stage.

On the other hand, the equipment needs to be updated to avoid any risk in the operations. With the manufacturing functions of this technology, the professionals will be able to carry out the indicated repairs in due time.


Streaming and Music


To better illustrate it, we will talk about Netflix and Spotify and how they use predictive analytics to give us a more personalized experience.

On the one hand, the streaming titan Netflix collects information about its users with its AI-powered Algorithms. Once they analyze data based on the user's watch history, search history, demographics, ratings, and preferences. Thanks to this process, 80% of users say that the recommendations are adjusted to their preferences, says an Engati article.

In the same way, Spotify sends you playlists or recommendations based on the genres or artists you usually listen to, making these personal to your preferences.

That's why labels are so helpful since the algorithms analyze what you see as "horror" when we talk about Netflix or "pop-rock" when we talk about Spotify.


Manufacturing:

Manufacturers need to identify factors that lead to reduced quality and production failures. Companies like Lenovo have used predictive analytics to understand warranty claims, which has reduced warranty costs to 15 percent.


Source: Youtube.


Predictive Analytics trends for 2022


IDC analysts explain that the analytics market has increased 10% compared to 2020, and businesses spent approximately $215 billion in 2021 on big data and business analytics. On the other hand, the demand for data analysts experienced a substantial increase of 31% in data science through 2030, as found in a Forbes article.

If you want to keep updated with the trends for this market, we'll share some of the most interesting ones for 2022.


Artificial Intelligence


AI and Automation are big players for organizations everywhere, and with the substantial progress in this field, it can help businesses generate value. The rapid developments in this field can be a product of the pandemic because the remote work forced companies to focus on tracking and measuring data, generating more investment.

With AI, companies can increase sales by forecasting demand improving customer satisfaction reducing the delivery time and automating repetitive processes to make everything more agile.


Data Fabric as a new industry standard


Forrester tells us that nearly 60 to 73% of data is not used for analytics in business. However, thanks to predictive analytics, this disparate data could be of great help to businesses. Artificial intelligence will allow the creation of a unified data architecture that can reach hybrid and multi-cloud environments in the company. This makes it easy for all users to access this information from any domain.


Predictive Analytics 2022.


Data Democratization


As everyone has seen the importance of analytics for good results, today, companies are looking for all their workers to have access to it to maintain constant improvement. The only tricky thing about this is the extra work it will take to accomplish it. Gartner analysts expect that by 2025, nearly 80% of data analytics initiatives will be business-critical.


Analytics Everywhere


Very soon, customers will get personalized insights that help them obtain more value from their data and even reach their goals quicker. Companies taking advantage of this trend could experience a significant advantage over their competitors. Companies can do this by combining AI/ML, business intelligence and Automation to let customers benefit from personalized services powered by analytics.


Conclusion


Good marketing is done through 50 percent strategy and 50 percent knowledge. You can't make decisions randomly that won't help your business grow.

Technology is constantly evolving to help us take the best steps through our entrepreneurship journey and business; it has grown so much that today predictive analytics can help you figure out what the future might bring for your business.

Having people on your team who know how to handle this kind of analytics and knowledge to manage the data and give you exact solutions for your problems is crucial.

Technology is here to stay, and the best thing you can do is know how to take advantage of it to get you to a better place.

Did you know about predictive analytics?

How do you use it in your business?

Let us know in the comments, and we'll see you soon on another article!


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