Organisations today are challenged by the need to understand their customers’ current and future requirements in a fast-paced and highly competitive environment, while still delivering goods and services in a profitable manner. Added to this is the need to continue to grow their business, increase sales, and improve profitability over time, says Gavin Wienand, senior consultant at Cortell Corporate Performance Management.
The ability to analyse data quickly and accurately lies at the heart of obtaining insights that will enable organisations to effectively address this challenge, but the sheer volume of data generated by organisations on a daily basis makes this a difficult task.
Harnessing this mountain of information to predict customer behaviour and buying trends, anticipate customer needs and preferences, and even address operational and manufacturing issues is key. Predictive analytics effectively enables organisations to leverage the insights contained within vast volumes of information, driving competitive advantage and giving them an edge.
Organisations today are repositories of large volumes of data about their customers, their brand and their operations, which can deliver important insights if useful information can be gained from this data. From databases to emails, call centre information to feedback generated on social media platforms, there is a wealth of value that can be unlocked, and the key to leveraging this value is predictive analytics.
Using this tool, organisations gain the ability to make more informed, fact-based decisions, improving business agility. Predictive analytics enables organisations to proactively establish the outcomes of ‘what if’ scenarios, delivering a snapshot of likely future trends and patterns based on current and historical data.
However, while predictive analytics is a highly sophisticated tool, it must be applied in the right areas of the organisation in order to deliver maximum value. Predictive analytics is designed to solve challenges at a transactional level, making it very useful for operational business decisions, including understanding customer behaviour.
It allows companies to identify market and buying preferences, customer churn probability, and other trends and patterns within transactional data. Given the pace of today’s business environment and its competitive nature, this information is invaluable, as it allows organisations to identify problems quickly, retain customers, and proactively maintain products and services before they fail.
For example, a major car manufacturer may use predictive analytics to analyse data such as current information from dealers, data on vehicle computers, repair information and call centre data, and use these insights to continually improve vehicles, customer service and sales.
Information such as defects that appear during warranty periods, allow the manufacturer to continuously evaluate and improve the manufacturing and service process, resulting in more reliable vehicles and enhanced customer satisfaction.
Another example of the power of predictive analytics lies in the insurance industry, where claims assessors have to process hundreds of claims a day and decide whether they are legitimate or fraudulent.
Predictive analytics speeds this process, using historical data from similar transactions as well as customer data and other information, to accurately predict the likelihood of a claim being fraudulent, in near realtime. This can save insurance companies large amounts of money when paying out claims.
Call centre sales agents can use predictive analytics to identify cross selling opportunities, based on historical client data, client profiling, products purchased by similar customers and so on. This means that products and services can be tailored for maximum uptake.
Similarly, predictive analytics can be used by telecoms companies, using customer data to predict the likelihood of churn, so that actions can be taken to retain the customer before they move to another provider.
In the past, predictive analytical capabilities have been limited by data volumes, as the tools available were not able to cope. This lead companies to reduce the amount of information used for analysis, mainly as a result of time constraints, which in turn limited the accuracy of predictions.
With the sophisticated tools available today, which are able to handle large volumes of both structured and unstructured data, including social media data, the insights gained are far more accurate.
If the outcomes desired of predictive analytics are based on business goals, and work within the strengths of the tool, in other words harnessing the power of transactional data, predictive analytics has enormous power to drive real insights, change customer interactions and gain more value from customers. This gives organisations an important competitive edge by putting them one step ahead of their competition.
