Predicting trends and patterns is a mainstay for managers operating at breakneck speed in today’s service world, which means there are tons of use cases for predictive analytics in the contact center. 

Who doesn’t want to look into the future and know exactly what actions could keep your organization ahead of the curve? While you can never be one hundred percent certain what the future might hold, some practices come close to giving forward-looking plans 20/20 vision. Predictive analysis is one of them. 

Predictive analytics uses statistical techniques like data mining, predictive modeling, and machine learning to estimate the likelihood of future outcomes so you can receive alerts about events before they happen and make informed choices about how to move forward. 

Use Cases for Predictive Analysis

Past customer behavior data and historical interaction information can be used to generate forecast reports that increase sales, revenue, and boost agent productivity. 

Predictive analytics is great for customer retention initiatives and strategizing how to maintain existing customer relationships, but there are many more creative uses for it in call centers

Predictive analysis lets you identify potential events and either avoid or leverage them. Being one step ahead has a significant impact in customer service organizations, no matter what industry you work in. 

  • Healthcare: support patient health management and improve outcomes across populations, demographics, or avoid medical equipment downtime. 
  • BPO: use behavior patterns to predict conversions. See what behaviors increase conversions and apply that behavior 
  • Financial services: risk scores in small business loans help determine the right price in the quickest amount of time
  • Telecommunications: insight into where marketing campaigns will have the most impact
  • Customer Care: predict customer behavior, recognize preference patterns, and automate campaign follow-ups. 

1. Identify signs of dissatisfaction

Customer satisfaction is at the top of the list in every organization. Dissatisfied customers result in churn, which in turn costs time and money. 

Predictive analysis can identify signs of dissatisfaction and customers that are most at risk for leaving. Your organization can use this information to proactively approach them and try to right the ship. 

2. Customer segmentation

Customer segmentation allows you to group customers by shared traits. You can make predictions about how each segment’s preferences might change, what actions they may take, and their future needs. You can make data-driven decisions about how to best serve each segment. 

3. Quality assurance

Good predictive analytics can provide insight into potential quality issues before they become a problem. Your approach to quality assurance goes from reactive to proactive. 

Financial services use predictive analysis to detect fraud and stop it before it becomes a problem for customers. Machine learning can identify patterns in your customer’s account behavior. Activity that falls outside of the learned normal can trigger a fraudulent activity alert. Conversely, you can use historical payment data to predict delinquencies or identify at-risk accounts.

4. Up and cross-selling 

Data from purchase history can be used to determine which goods and services might benefit from being offered together. This is helpful to your organization’s bottom line and your customer. You increase your sales and your customer walks away with items that work together. 

5. Brand & reputation management

Your reputation plays a huge role in your organization’s success. Keeping a keen eye on customer sentiment and how it changes is a smart business preservation strategy.

Data analytics that assesses feedback scraped from across your website and the wider internet gives you a holistic picture of customer sentiment. Speech analytics can perform call recording and transcription and boil sentiment down to keywords so you can make changes to create the reputation you desire. 

6. Campaign management

Analytics tools like channel performance dashboards and word clouds generated from call recording data help you determine outreach efforts that are working and actions you can take to improve those that aren’t. 

A campaign launched using email may work better as a suggestion at check out. Or, maybe more of your customers are turning to your website for information. Predictive analytics help you determine where your campaign is best focused. 

7. Proactive maintenance

Maintenance is a necessary cost. The more you can minimize its impact, the better. 

Predict upcoming maintenance expenses by analyzing lifecycle metrics of technical equipment. You can streamline your maintenance costs by performing work that will increase the lifespan of your equipment. 

Most systems become inoperable during maintenance. Predictive analysis helps you determine the best time to perform maintenance to avoid lost revenue and dissatisfied customers. 

8. Calculate messaging approach

Historical campaign reporting takes the guesswork out of determining the right way, and time, to approach your customers. Data from social media, CSAT surveys, and customer communication patterns can power forecast and trend reporting while helping you plan your best messaging approach. 

9. Risk assessment 

Prediction and prevention are two sides of the same coin. Health organizations can leverage prediction to make sure patients get the care they need. 

Predictive analysis can be used to identify patients who are more at risk for certain adverse health conditions. Risk scores are generated using data from lab testing, biometrics, and patients themselves. These scores help health organizations determine patients that might benefit from preventative care, enhanced services, or wellness consultations.

10. Volume prediction 

Fluctuations in volume can have a severe impact on how well you can serve your customers. Being able to predict when increases in inbound volume might happen eases that impact. If you know when spikes will occur, you can make sure your facilities are adequately staffed

For instance, an insurance company might use predictive analysis to predict patterns in plan use for certain providers or demographics. They can then model use patterns to make adjustments to claim workflows or adjust processes for seasonality.

Bottom line: Move beyond reacting

Predictive analytics gives managers and their teams the ability to move beyond a reactive approach. It provides the insight needed to make informed decisions about how an organization moves forward while becoming more anticipatory as a business.