Why Predictive Analytics is Changing the Way We Use Our Data
One of the key jobs of a business manager is to make decisions. Indeed many internet sources suggest that the average adult makes 35,000 decisions on an average day. Many of these are at a basic survival level, e.g. what should I eat today, or what should I wear? However many, and certainly the majority of those made in business, are complex and need due consideration.
Each decision we make carries consequences. For instance, if we choose to eat healthy food, we are likely to give ourselves healthy bodies. If we choose to survive on junk food, our bodies react negatively, building increased fat and decreasing energy.
Business decisions may have positive consequences the firm may make a profit thanks to a series of management decisions, or they may have negative consequences too many bad decisions may cause the firm to lose money, or perhaps even go bankrupt.
Decision makers rely on available data which they use to weigh options when making decisions. The better the quality of the data they have available, and the more reliable the process followed, the better their outcomes will be.
How Big Data is Changing Business
With the increase in data quantity and the expansion in the variety of data types able to be collected, data has emerged as a driving force in decision-making for businesses. People no longer have to rely on their gut feelings. They now have previously uncollectible evidence available to help them make higher quality decisions.
New data sets are continually being made available. To a large extent, this is because we can now collect and record many different types of data than we could previously. For instance, a decade ago businesses would have had to rely on surveys to determine how call center customers feel. It would take some time for these surveys to be taken, collated and the results made available for analysis. Now machine learning algorithms such as those used in RankMiners predictive voice analytics platform converts the unstructured data of a customer/agent conversation into structured data. Furthermore, that data is automatically transforms that data into actionable information by identifying the emotions and behaviors that drive business outcomes.
This is a clear case of data now being able to be collected and turned into actionable information to help businesses optimize their decision making process and would have been impossible in the relatively recent past.
How Big Data Can Enable Predictive Analytics
Data scientists are changing the way we do business. The massive quantity of data collected today enables predictive and even prescriptive analytics by extracting insights from existing data to reveal previously unseen patterns, events, and allows predictions of potential future outcomes.
Predictive analytics can take a wide range of previously unconnected data items from disparate data sources, and mesh them together to make educated predictions about future behavior. Data scientists now use predictive analytics with a high level of confidence in many industries from improving the hiring process to retention, to quality monitoring to prioritizing interventions.
Predictive Analytics in the Call Center
Predictive analytics helps call centers better understand their customers. Emotions, or how experiences make a customer feel, have the single greatest impact on customer loyalty. But how can you know what a customer will do as a result of how they feel?
Predictive voice analytics such as RankMiner measures human emotions and behaviors on 100% of your customer phone interactions which immediately identify which customers are at-risk, which will buy more, and which of your agents are helping or hurting your business. It can help distinguish the time wasters from the genuinely interested.
Predictive voice analytics, in particular, can help you make better decision regarding your customers, your agents and your operational processes. It helps ensure that a call centers resources are allocated in the most suitable way, delivering the best possible value.