Data analytics have seemingly endless applications for business owners. No matter the size of your operations business analytics integrations have tons of highly advantageous use cases. Business intelligence software is all about breaking the mold to make new business rules that better serve the needs of your operations. With the right business intelligence integrations and data analytics software, you can make better decisions that lead to innovation within your company. The following are some popular use cases for a specific type of analytics called prescriptive analytics software.
Forecasting Customer Needs
One of the most popular use cases for prescriptive analytics is determining the best possible outcomes and utilizing actionable insights for customer interactions. Prescriptive analytics technology extracts the best solution from the decision options of future outcomes created by predictive analytics models. Descriptive analytics organizes and describes historical data, predictive analytics interprets this data and creates simulations of possible scenarios, and prescriptive analysis ties these two models together by determining the implications of each decision option.
This type of analytics technique finds the best way to improve customer service. Prescriptive analytics software works to breathe innovation into your current business processes by re-writing business rules. It interprets the predictive models and produces its own optimization model with the best decision options. Whether it’s streamlining your sales cycles, optimizing customer interactions, or building highly encrypted databases of customer information, the prescriptive model of business analytics will help you determine the best course of action.
Creating Fair and Standardized Pricing
Financial services are considered another popular use case for prescriptive analysis. Prescriptive analysis is like a prediction optimizer, and when you know the right choice to make for future scenarios, you can streamline your operations. One area that can easily be optimized with these analytics and statistical models is your sales channels. Utilizing operations research, machine learning, and artificial intelligence gives you supply chain updates in real-time.
The next step is to optimize your eCommerce channels by ensuring your prices are fair and regulated. Analytics will show you and your sales team what works for your customers and what needs to change. Innovation comes when you follow the insights that analytics give you. You can also employ responsive algorithms that track customer activity and product viability. Using this information you can keep your product prices updated and be sure that all parties are benefiting. You can easily maintain high-profit margins while still offering reasonable prices to your customers when you have access to the right data insights.
Improving Customer Service and Engagement
The final use case we’ll discuss is improving customer interactions. When you know what your customers need and your pricing scale is optimized, it’s time to focus on your customer service team. The way your salespeople interact with your customers is foundational to the success of your company. When your team has immediate access to crucial customer data, they can better serve people on an individual level. Your analytics software integration can also serve your team by optimizing their workflows and interfaces.
Accelerating your sales cycle is about more than just increasing profits. This increase in efficiency plays a big role in maintaining customer loyalty and relationships. When customers know they’ll be taken care of when they interact with your team, they are more likely to keep coming back. They will also be comforted to know that they can get in contact with your support team whenever it’s needed. For instance, if your online sales channels offer live chats with your support team, you’ll want your employees to have immediate access to the correct data sets. Without this accessibility, the entire process slows down and customers are less likely to use the feature in the future.