February 15, 2022

How To Improve Business Processes with Predictive Modeling

How operations and CX teams can use predictive modeling to improve processes and productivity

What is predictive modeling?  

Predictive modeling is a process of taking historical and current data and creating a model to help predict future outcomes. It’s a form of data mining, as the historical and current best data is taken from computer logs, not the opinions of CX team members and managers.  In predictive modeling, data is collected by a piece of computer software, the statistical model is formulated, and then the predictions can be made. Later, the model is validated (or revised) as additional data becomes available. Predictive modeling can be used for almost anything, from TV ratings and a customer's health insurance premiums to credit risks and corporate earnings. 

The point of Predictive modeling is that it allows companies to make smart decisions that will work for themselves, their customers, and their employees. It enables companies to invest time, people, and financial resources into projects and changes that will actually make a difference. 

Different models can be used for different situations because companies and businesses will want to know different things to make different decisions. Retail for example will have very different priorities than hospitals, so a different model is needed. 

Common predictive analytics models and algorithms are: 

Classification Model 

In some ways, the simplest predictive model, and the best for answering yes or no questions. The classification model works by putting data in categories based on what it learns from historical data. Although the analysis is broad and simple, it is helpful for guiding decisive action and can answer questions such as: 

  • Within retail, “Is this customer about to make a purchase?”
  • Within financial planning, “Will a loan for person A be approved?” or “Is person B likely to default?”
  • Within online banking, “Is this a fraudulent transaction?”

Because there are so many possible questions that this model can answer, it can be applied to many different industries and therefore is commonly used for future planning purposes. 

Clustering Model

A clustering model is a form of predictive modeling that separates data into different groups, based on similar attributes. For retail companies, for example, this could be things like the age of their customers. From this information, they make decisions on how best to sell to them in the future by considering what is working now and what worked in the past. This is much easier, quicker, and cheaper than building ad campaigns for each individual customer. 

Outside of retail, financial institutions often use clustering modeling to group loan applicants based on loan attributes. It can also be used for benchmarking SaaS customer data into groups to identify global patterns of use. The clustering model more often than not is about generalizing information so decisions can be made more quickly, saving time and money. 

Time Series Model

A time series model takes the same data from evenly spaced points of time, such as once a year, once a month, weekly, daily, or even hourly. For example, this could be the monthly temperature in a specific state, the annual profit for a company, or the monthly price of fuel.

Time series data has four aspects of behavior: 

  • Trend: the overall long-term direction of the series. Some series have no discernible trend, meaning that each data set taken is random with no correlation. 
  • Seasonality: repeated behavior in the data which occurs at regular intervals. For example, in retail, sales may rise during the holiday season every year. 
  • Cycles: when a series follows an up and down pattern that is not seasonal. Cycles can be of burying length which can make them harder to detect. 
  • Variation: in all data is there is a random variation. Some time series will be very regular, with little random variation, while others may consist of little else. 
  • Irregularities: these can be due to a one-off event such as weather disrupting air travel or an anticipated change in the rate of sales tax. 

Like with all other forms of predictive modeling, companies can make an estimated guess on what the future weather, profit, or fuel prices might be based on the historical data. 

How operations and CX teams can use predictive modeling to improve processes and productivity 

When operations and CX teams are looking to make changes to their workflow and processes, using predictive modeling can enable them to test the changes, before rolling them out across the entire organization. For example, a CX manager may want to introduce a new SaaS application into the workflow. They hope that the new application will speed up their agent's process and help to keep average handling times (AHT) low without affecting the high customer experience quality. Without using predictive modeling, the manager will have to introduce the new SaaS application across their team. This will take valuable recourses, such as the cost of implementing the new software, and the time of training agents on how to use it effectively. 

After two months, through monitoring agent productivity and average handling times, it’s clear that the new SaaS application has not had the desired effect the CX manager hoped it would. Because the company did not use predictive modeling, large amounts of time and money were wasted, customer experience dropped and the average handling time of the team rose. 

If they had used predictive modeling and made their decisions based on historical data, the company could have avoided this. That is exactly what Fin Experiments offers. Experiments enable companies to test out changes to their workflow, without affecting the current workflow of their CX agents. The historical data used has been collected from the company itself, so the outcomes are tailored to that specific operations team. Using Experiments lowers to the risk to almost zero, and means every change implemented is backed by data that proves it works. With Experiments, operations teams define a hypothesis they want to test, select a group to include in the Experiment, and specify a duration. Based on the parameters of the Experiment, Fin generates a customized dashboard comparing the impact of that Experiment.  

Updates, changes, and improvements are crucial to maintaining a high customer experience, but without using predictive modeling to improve processes and productivity, companies could be risking a lot to gain very little. If you’re interested in what Fin can do for you, get in touch with our team today or book a free demo.

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