If you’re looking for ways to use your valuable business insights to shape the future of your business, you might use predictive models. Predictive models take data from the past to make insights about the future. These insights can shape business decisions, predict trends, and forecast events that could affect your business.
What are predictive models?
There are several types of predictive models available today and each area designed to interpret different data. Predictive analytic models take historical data and use it to discover patterns, identify trends, and predict the nature of future data. This information can be valuable to business looking to prepare for the future and make use of their data. Predictive models make use of machine learning and data mining to predict the statistic probability of a future event happening. The outcomes are scientifically based, making them highly accurate and readily available.
How do predictive models work?
The basis of predictive modelling is statistics. The models take an outcome that might occur in the future and test the statistical chance of it happening based on the historical chances of that event occurring. For example, retail stores around the world have been collecting data on the purchasing habits and response rate of their customers for years. They can now take this data and use it to calculate the success rate of future campaigns and promotions. Using predictive models, they can calculate the statistical chance their customers will respond positively to a promotion based on its success in the past.
Uses of predictive analytic models
Businesses can use predictive analytic models in every industry to make more business decisions related to:
Promotions
Predictive models can predict the success of business promotions by looking at historical data on promotions and reflecting on consumer perception of them. If customers historically responded well in in-store events and displays, predictive models can show that this may continue in the future. These insights can help businesses allocate funds and design promotions in line with the purchasing behaviour of their customers.
Employee turnover
Predictive models can help predict employee turnover and retention rates. By looking at historical data related to retirement age averages and comparing it to graduation rates of the industry, predictive analytic models can inform you of potential gaps in the workforce. These insights can make informed decisions on your promotion and hiring strategy.
Financial downturn
Scientists have recorded economic cycles such as recessions for decades, making them easy to predict. Predictive analytic models can narrow these predictions down to which industries they might affect, which can impact your business decisions. For example, if models are predicting a recession in the next year, you may reconsider investment or expansion plans as you hold on to extra capital.
E-commerce trends
Businesses around the world are using their data on their online customers to tailor the online shopping experience to the needs of their consumers. Predictive analytic models can improve targeted advertising and messaging. By studying what makes consumers make a purchase, businesses can adjust their online platforms and gain more sales.
Top predictive analytic models
Of the predictive models commonly used, the top 5 predictive analytic models include:
Classification model
One of the most commonly used models is the classification model. These models are designed by inputting a series of scenarios and classifying the outputs. Eventually, the models will take inputs and classify outputs on their own. Businesses use these models in almost every industry, as someone can easily retrain them to use different data. This model categorizes data based on set parameters and is best suited to answer “yes” or “no” questions.
For example, banking systems can use classification models to detect fraudulent transactions. By setting the parameters of an outcome to the common fraud triggers, such as unrecognized email, lack of transaction history, and unregistered name, the models can predict which transactions might be fraudulent. When they scan an input for these triggers, if they appear, the model will flag them as fraudulent and a bank advisor can investigate.
Clustering model
Clustering models take data and sort it into groups based on common parameters. These models can be beneficial to businesses that handle large amounts of data or have access to years of historical data. Clustering models make sorting through data timely and efficient, and we businesses can use them in most industries.
For example, marketing professionals can input their data on consumers and the model can cluster the data sorting it into customer demographics such as age, location, purchasing behaviour, and more. Marketers can then use this information to better target these groups and tailor their marketing efforts to their buying patterns.
Forecast model
Forecast models are the most widely used predictive analytic models, as we can customize them to fit any type of parameters. These models are numerical based, so as long as historical numerical data is available, they can accurately predict the chances of an outcome. They take historical data and estimate the value of new data by identifying patterns in the number sequence. These models can also identify aspects that may affect the data, such as events or disruptions.
For example, a call centre can use forecast models to predict how many calls they might receive in a week. They can then take this information and use it to schedule the right amount of call centre agents to handle the call volume. Forecast models can identify how the launch of a new product may increase the call volume for the week, so call centre managers can prepare for a higher call volume and staff their centers accordingly.
Outliers model
While most predictive models use historical data to generate their findings, outlier models search for data that deviates from the average. They collect data and pull any data points that fall outside of a set parameter. This information can be valuable for businesses that cannot have anomalies in their data, such as retail and finance.
For example, businesses commonly implement outlier models alongside classification models to identify fraud. Information that is flagged strays outside the set parameters for further investigation. Since banks typically link fraud cases to suspicious information, pulling these cases can identify fraudulent behaviour before money is withdrawn. This information can save billions of dollars around the world annually.
Time series model
Time series models compare data points against time to identify patterns and make predictions about future timelines. These models are useful for tracking the progression of a single data case over time to see if it’s growing. Since growth is not linear, time series models provide a more accurate prediction than averaging methods.
For example, a hospital wanting to predict ICU capacity in the next six weeks can use time series models to predict it. If they were to average the capacity rates of the previous three months to see what the next six weeks will be, the reading won’t be accurate because hospitalizations are not static. By using a time series model to track the top causes of hospitalizations and factoring in events such as flu seasons and extreme sport seasons, hospitals can get a more accurate prediction of what they can expect.
Limitations of predictive analytic models
While predictive analytic models have provided mass economic benefits and allowed businesses to gain valuable insights about their futures, there are limitations to these models.
Data Sets
These models required large amounts of historical data to accurately make predictions. This requires businesses to have been in business for many years and to have access to their previous years of data. This limitation makes these models inaccessible too many new businesses or businesses with limited data.
Customization
Predictive analytic models often have a difficult time applying their model to different data. This means businesses may have to spend large amounts of time reprogramming these models if they wish to study different outcomes. This can be a significant upfront investment for many businesses, especially new or small ones.
Categorizing data
Since most of the models require the categorization of data in order to make future predictions, the mis-categorization of data can have huge effects on business. One small categorization mistake can lead to larger miscalculations down the line, which can be costly for businesses that rely on these insights to make investment decisions. Data labeling and categorization is a meticulous task often too large to be done by humans. With humans unable to check and review the categorization, there is trust left in these models to not make mistakes.