Use predictive analytics to perfect the customer experience
Staying in business is increasingly a challenge for companies, let alone expanding and attracting new customers. But in today’s competitive landscape, growth and diversification is a necessity. Wisdom tells us that companies that don’t grow eventually shrink. When companies stagnate they don’t improve, while the competition does. To stay on top of their game, companies have to innovate, understand and meet customers’ needs and deliver a superior customer experience. This is where predictive analytics can help, writes Jonathan Wax, the vice president for EMEA of NICE Nexidia.
Put simply, predictive analytics is a combination of artificial intelligence (AI) and machine learning technology. AI looks at human behavioural or narrative data and matches it with factual information to draw an intelligent conclusion. Machine learning uses statistical techniques to give computers the ability to ‘learn’ and adapt.
Predictive analytics enables organisations to anticipate a customer’s needs based on historical, post-interaction or real-time analysis of big data, and quickly deliver what the customer wants. This is a huge boon in an era where customer experience is king.
Measure customer satisfaction and loyalty
Keeping customers happy and ultimately retaining them is the holy grail for businesses in today’s customer-centric world. Knowing when they’re unhappy is equally important. Analysing speech patterns and teaching machine learning engines to recognise and act on customer cues is already bringing huge benefits to companies. For example, if the AI engine recognises that a customer is angry, it can then pair it with factual information patterns – such as long call times or words associated with that emotion to accurately assess customer satisfaction levels.
Perhaps the conclusion is that angry customers facing long call times are getting incorrect information? Predictive analytics can greatly reduce the human effort needed to trawl through data and draw the same conclusions.
Taking it a step further, predictive analytics has huge value for assessing customer loyalty to your service or products – and for identifying a customer’s propensity to leave you. Customers identified as high risk can be sent to call centre agents to be proactively contacted and offered competitive offers to hopefully help retain them.
Boost sales effectiveness
As well as improving the customer experience, predictive analytics can help organisations better identify cross-sell or up-sell opportunities. The most commonly used predictive analysis is for measuring customer sentiment – predicting the likelihood of a customer to purchase a product or service.
Gain a competitive edge
Predictive analytics brings real benefits in identifying challenges or opportunities with individual customers. But where it really comes into its own is crunching lots of historical and real-time data to identify broader issues and trends that have a much bigger impact on your operation. Imagine being able to look across your business and predict when and why batches of customers may be considering leaving you.
Businesses can act on this information, and put processes and procedures in place to minimise the risk. Building predictions using past behavioural patterns allows companies to identify different categories of customers and then anticipate their future actions and attitudes.
Used effectively, predictive analytics has the potential to transform businesses and industries. As you gain insights, and this actionable intelligence is used to make improvements, companies will get a better understanding of their customers, proactively resolve issues before they spiral, and ultimately improve the customer experience.