What is predictive analytics
Predictive analytics is a type of business intelligence technology that combines current and historical data patterns to generate predictions on future outcomes. The technology consists of statistics, data mining, predictive algorithms, and machine learning.
Predictive analytics is also considered to be a key discipline of data analytics. In the data field, it means utilizing expert knowledge and quantitative methods to derive insights from metrics and answer fundamental questions. Data analysis contains 4 main stages:
- Descriptive analytics to answer: What happened?
- Diagnosis analytics to answer: Why did it happen?
- Predictive analytics to answer: What could happen in the future? – this is what we will explain in this article
- Descriptive analytics to answer: How should we answer the potential events?
For business, the prediction can be for the near future, such as next month’s inventory and maintenance, or the far end, such as next year’s cash flow and sale forecasts. The more data you collect, the more accurate and further the prediction becomes.
Benefits of predictive analytics in business and operation
An effective predictive analytics system can help enterprises optimize their business outcomes and operations.
1. For business
Predictive customer churn and the potential ones
By analyzing transaction data, historical product choices, and market trends, businesses can learn behavioral patterns among their segments and make quick adjustments where necessary. By acting promptly, a company has a chance to retain their customers and a higher advantage in attracting new ones.
More touching customer experiences
One of the best benefits of predictive analytics is offering businesses insight to personalize their products and services as well as their promotion and customer appreciation. These are the natural ways to boost the customer experience and anticipation. They feel like their needs and wants are perceived and thus trust the brand and the seller more.
Detecting fraud
This is extremely important for financial enterprises. Predictive analytics extract patterns from data; therefore, they can spot what is out of place. Companies can quickly intervene and protect their customer security by suggesting potential fraud and determining feasible solutions accordingly.
For commercial or retail businesses, flakes, countermand at short notice, or even “prank order” can also be mitigated with predictive analytics. Many e-commerce platforms score their users based on old transactions, latest orders, add to cart items to predict the chances of failed delivery in their next purchase.
2. For Operation
Strategic planning
Predictive analytics can tell you the trends and the likelihood of future events so business leaders can prepare and make vital adjustments. The technology is not for sales, marketing, or product management; it can be used for various departments, even human resources.
Timesaving and efficiency
Driving a business to success should not be based on guesswork. You might get lucky a few times, but it doesn’t always ensure accuracy. Making decisions with data and predictive tools becomes easier and more impactful for the high-risk choices.
At the same time, predictive analytics free humans from self-collecting and analyzing thousands of clusters of unstructured data with its automation. This is the beauty of technology; it helps us work smarter and get tangible results.
Preventing risks
What problems are likely to arise in the future is the question every business seeks answers to. There are no tools or techniques to help companies to answer it precisely with accuracy since it is the future. Yet, predictive analytics can pinpoint the things that need consideration, both opportunities and challenges. Even though it’s not 100% certain, the idea of knowing what might happen still benefits businesses while making strategic decisions to some extent.
How does predictive analytics work
As mentioned before, Predictive analytics combines statistical analysis, deep learning, and machine learning algorithms. But how does it work and what steps should be taken to make a seamless predictive workflow?
1. Prepare for your predictive analytics process
Understand your needs and goals
Like other products and services, businesses decide to employ predictive analytics when they face specific problems such as fraud, sales numbers do not increase, or overstock issues. Yet, you can sometimes mix the actual causes with what you inspect. For example, your sale volume is decreasing despite many active marketing campaigns. You think your advertisement does not target the right customers, but the truth is your product is outdated and not as innovative as your competitors.
Starting with a list of questions and categorizing them based on its importance to determine the leading causes and tackle the correct issues. Other methods include the SWOT model or root cause analytics (RCA).
Gather your team of experts
Predictive analytics is complex. Therefore, it requires a team to constantly monitor and manage the tools. Some key positions should be the decision-maker, the line manager, or the department that understands the problems and needs the tools; IT support to implement with existing infrastructure; a data expert to collect, read and manage the database for predictive analytics.
However, data positions might seem abundant for some SMEs. In this case, you can collaborate with data analytics vendors. They have a team of data experts who can help you scrutinize every aspect of your database and determine how to best utilize it.
2. Collect your data
Gather all the data you have, both unstructured and structured data. Afterward, screening and categorizing which is important and which is micro-important. During this step, you should work closely with your data team to determine the valuable data. A few things to do:
- Correctly label and format your data
- Ensure data integrity, which means removing inconsistent and invaluable data
- Avoid data leakage
- Constantly checking and reviewing your data to maintain accuracy
3. Develop your model
In this stage, businesses start to build, train, evaluate and complete their predictive model. Find the best way to organize the collected and refined data into an appropriate feature for your predictive model. There are 2 ways:
- You work with a data team to develop and tailor-make your model. It costs more but is more effective in terms of generating predictive results since the algorithm has been set up to best suit your organization’s resources and needs.
- You use an AutoML tool to develop one yourself. This way is more affordable but at the same time, it might not deliver the highest quality since it’s lacking in data science touch.
In the end, the best tool requires time to test and refine over and over. So don’t worry if your predictive modeling does not work well initially. Ideally, the model should automatically adjust when adding a new dataset.
4. Deploy the model
A predictive analytics model needs to produce insightful information and valuable results. It should also operate seamlessly without requiring constant checkups by humans. Most importantly, the system must comply with your existing technological infrastructure. Once the data and developer team approve the model, the whole team should complete the predictive analytics workflow before implementing it at scale. This means optimizing the process of gathering, retrieving, cleaning, and classifying the raw data required for the model.
In case you work with a data and software development vendor, you might want to put some training on how to use and how to read the data for other teammates in this step.
5. Monitor your model
External factors such as the environment, business climate, regulations, or business strategy can affect the model’s performance. Make sure you closely watch your input and output to ensure predictive analytics accuracy, transparency, and insights.
Predictive analytics techniques and models
To forecast the possibilities based on data, businesses can leverage different predictive analytics models or, in some cases, combine many simultaneously for better results.
1. Decision trees
Decision Trees is the method for classification that divides data into subsets based on the input variable’s categories. It looks like a tree with branches that each represents one choice between several alternatives, while the leaves represent a decision. Decision trees is prevalent since they are easy to interpret and can show the path of making decisions. By looking at the data, decision trees will try to find the one variable that splits the data most logically and differently. This makes the method useful for preliminary variable selection and can handle missing values very well.
2. Regression
The regression model (also called linear and logistical models) is a commonly used statistical method. It estimates the strength of the relationship between variables. This model will track how action impacts the outcome and analyze this result to suggest the future.
With the analytics of the relationship between each variable, business leaders can perform scenario analysis. Regression techniques are mainly used for marketing strategies or product management in business. You can explore how external and internal factors such as size, seasonality, price, or placement affect the likelihood of purchasing. For example, by using regression models, product managers find out that the package with red color attracts more attention and sales than the color white. In this case, the regression deeply researches the relationship between colors versus attractiveness and gives answers while the decision is in your palm.
3. Neural Networks
Neural networks were developed based on the stimulation of the human brain neurophysiological. Therefore, they are very flexible and capable of dealing with nonlinear and extraordinarily complex relationships in data. This method is often used to confirm results from decision trees and regression since it works based on pattern recognition and AI (Artificial Intelligence) processes.
Neural networks can input text, voice, image, audio, video, and so much more. However, neural networks do not comply well with mathematical formulas. They might not give you detailed explanations of the matters, instead leaning more towards predicting the outcomes of the problems. Due to its ability, this deep learning model is mainly used for voice and facial recognition software.
4. Classification
The classification used historical data to divide training datasets into categories. A training dataset means the data has been labeled and can be used to train machine learning. This helps the algorithm understand the correlation between data and its label more thoroughly and categorize new data more accurately. Classification models are popular among many industries since they can be easily retrained. In predictive analytics, the models will analyze thousands of previous datasets to learn what the future might look like and quickly alert if necessary.
5. Clustering
Clustering models identify similar attributes between data and then place them into groups. This method adopts a data matrix, which associates each item with its relevant features. First, the algorithm will cluster the data groups and spot the patterns in each group that might be hidden. The clustering model performs its best in customer segmentation and personalized strategies since it can point out hidden insights and allow organizations to target their customers better and earn retention.
6. Time-series
Time is one of the most common independent variables in predictive analytics. Time-series model is the method that collects data points over intervals by data mining and forecasting techniques. This model can be used for seasonality analysis, which predicts how demands are affected by specific periods. Or trend analysis, which indicates the movement of assets over time. Examples of time-series models include forecasting sales for the upcoming season and determining the number of potential customers during certain hours.
7. Others
Many other techniques are being used in predictive analytics, such as:
- Uplift model (or Incremental response): widely used to reduce churn and discover the effects of the marketing campaign by modeling the potential changes caused by an action.
- Ensemble model: usually used to train or combine with other models to improve accuracy and reduce bias and variance.
- Principal component analysis: used to derive independent linear combinations from a set of variables that retain the equivalent amount of information as the original variables as possible.
- Partial least squares: is a flexible statistical technique for any data shape. The method investigates the factors that explain results and predictor variations by modeling relationships between inputs and outputs.
Descriptive vs predictive analytics
Descriptive and Predictive Analytics both explain the development of an event. While predictive focuses on possibilities of future events, descriptive investigates the past and ongoing events. In data analytics terms, predictive analytics looks at historical data to determine the likelihood. Descriptive analytics looks at historical data to identify how a unit reacts to a set of variables.
They are both supporting and crucial to the process of data analytics. Without descriptive, it would be challenging to forecast the future when you don’t know what current state you are in. Data mining and data aggregation are the main techniques of descriptive analytics. Some add-ons such as querying, real-time reporting, and data visualization may also be employed to gain better insights.
Prescriptive vs predictive analytics
Prescriptive analytics heavily relies on predictive analytics since it presents hypothetical scenarios and extrapolates outcomes in the future based on variables. With predictive analytics, businesses can determine different plans and strategies, then use a prescriptive model to stimulate each choice’s context for better decision-making.
Prescriptive analytics is also considered a combination of both descriptive and predictive analytics. This method is the last step of data analytics and answers the most crucial question of what a business should do. Thus, without the above methods, prescriptive cannot exceed its ability. Its techniques include simulation, decision analysis, and game theory.
5 Examples of predictive analytics
Predictive analytics is a powerful tool for businesses in a plethora of industries. Yet, some specific industries can potentially acquire more benefits in predictive analysis than others. Here are 5 examples of predictive analytics use cases:
1. Healthcare
According to the International Data Corporation (IDC), healthcare data is primed to grow faster and produce larger than any other industry. Thousands of people come in and out of a hospital daily, and each one of them provides vital and detailed information about their persona, habits, conditions, and lifestyles. This data gives you an insight into their daily life, which can foretell a lot about their current stages or possibilities of disease. Yet, healthcare workers don’t have the time and resources to investigate each profile deeply. In this case, machine learning with predictive analytics can screen thousands of profiles and quickly identify abnormalities.
Predictive analytics in healthcare can help organizations:
- Improve patients’ outcomes by analyzing their health and medication history, lab test results, and living habits to offer the best treatment plan. This can further prevent misdiagnosis and missed diagnosis.
- Quickly intervene by identifying the nearest and high-risk patients. This is highly beneficial to at-home healthcare and elderly care services, which require many priorities.
- Enhance patient services by tailoring treatment suggestions.
- Improving healthcare operations for cost-saving and time-efficiency.
2. Retail
Predictive analytics, or data analytics in general, is essential to the retail industry. Why? The reason is retailers, or e-commerce businesses have a wide chance to leverage the information to boost their sales. Customers nowadays have thousands of places to shop. Thus, you are not the only provider within their reach. Getting ahead in the market and staying on top of the customer’s mind becomes challenging. Predictive analytics in retail gives you the benefits of mitigating risks, which is a significant advantage in this booming economy while attracting and retaining customers simultaneously.
Some detailed uses of retail predictive analytics include:
- Personalized customer experiences
- Customer demands forecasting
- Customer churn forecasting and preventing
- Inventory management
- Understand your shopper behaviors and insights
- Impactful decision-making
3. Finance
Bank and financial firms are expected to ensure and guarantee the highest level of customer data security. But potential risks are always there, from hackers trying to break the system, identity thieves, credit fraud to Ponzi schemes and fraudulent charities. Surprisingly, an incredible benefit of predictive analytics is detecting fraud and risk management.
Predictive analytics in finance not only identifies similar and familiar patterns but also spots abnormal and potential attacks. One way is that the system will process historical fraudulent cases and apply patterns to examine new data. Another way is to use machine learning algorithms to analyze spending behavior. This way, financial businesses can check creditworthiness and establish the appropriate amount to mitigate risks. As a result, the company becomes more agile when facing security issues.
4. Marketing
Marketing is one of the top industries that use vast amounts of data. Yet, the marketing team can’t rely on their self-collected data alone. They need data from sales, products, or customer services departments to make their operations the most effective.
Predictive analytics in marketing can assist the team in creating tangible campaigns with the highest level of quality and effectiveness by:
- Better customer segmentation by researching their habits, personal references, previous purchase, add-to carts, and other metrics. This allows marketers to personalize outreach, including content, voucher, or promotion for each customer group.
- Quickly react to customer churns to maintain their retention.
- Better marketing decisions
5. Manufacture
Production and supply chain are the core of many businesses. Optimizing the process with predictive analytics has been the case study of many manufacturers. Some of the best predictive analytics use cases in manufacturing can be:
- Forecast demand to allocate production (costs, inventory, and human resources) and avoid overstocking materials or finished products.
- Making agile decisions with up-to-minute and up-to-date reports.
- Predictive maintenance helps identify potential machine breakdowns beforehand. The goal of this adoption is to help businesses save costs and time, as well as avoid lost production time as much as possible.
Leveraging Predictive Analytics from scratch might be a daunting start, so it would be better to seek advices from data scientists and data analysis before execution.
Synodus provides Predictive Analytics services, including advisory, BI reporting components, tailor-made your analytics platform and managed services to help businesses benefit from a the most tangible insights.
Wrapping up
Utilizing predictive analytics can help business leaders make winning decisions and minimize the risks of losing customers and costs. Yet, predictive modeling should not be quickly implemented. For it to perform the best, businesses need considerable preparation and suitable tools. With its flexibility and ability to solve multiple problems, predictive analytics can be used in every industry.
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