1. Identify signs of dissatisfaction using historical data
Customer satisfaction is at the top of the list in every organization. Dissatisfied customers result in churn, which in turn costs time and money.
Use predictive analytics to 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 based on historical data 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 analytics techniques 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 using data analytics
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.
Predictive analytics models that assess 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 analysis 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 determines 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 analytics 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 analytics can be used to identify patients who are more at risk for certain adverse health conditions. Risk scores are generated using continuous data mining techniques 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 use predictive models or 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. Whether it's predicting customer behavior, optimizing marketing strategies, or improving operational efficiency, NiCE can significantly enhance your business operations by providing robust predictive analysis and regression analysis solutions.
