With customers being bombarded by marketing messages at all hours of the day, delivering relevant and personalised customer experiences (CX) is essential to ensuring brand satisfaction and loyalty in 2022. Today, a subpar interaction with a customer has the potential to seriously tarnish a brand’s reputation, especially in an age where bad experiences can go ‘viral’ quickly at any given time.
The best way to personalise customer experiences is by implementing data-driven changes, and as such, data analytics can lead to substantial benefits for both agents and customers alike. McKinsey & Co. reports that personalisation can boost company sales by 10% and improve returns on marketing investment eightfold.
Which metrics should you be tracking@f1 How should you utilise this data to deliver personalized CX@f2 We’ll answer these questions and more in this article.
What types of data analytics are useful@f3
Advances in artificial intelligence (AI) and robotic process automation (RPA) have led to increased fervor to gather as much useful data as possible. There are numerous ways raw data can be collected for analysis and action:
Speech analytics are derived from the contents of recorded calls. Through natural language processing (NLP) and voice recognition features, customer dialogue and tone can be recorded and analysed. This helps improve processes, find the best solutions to frequently recurring problems, and enhance training materials.
Text analytics are drawn from different textual means of communications such as email, chat, or social media comments. Once conversational text is extracted and filtered, the resulting data can be mined for useful analytics pertaining to customer sentiment, intention, and behavior.
Desktop analytics focus on capturing and understanding agent interaction and workflows with their desktop or laptop computer. By analysing keystrokes and monitoring how applications are being accessed, managers are able to measure compliance with client protocols and determine if there are any workflow bottlenecks, process inefficiencies, or issues that can be resolved through training.
Cross-channel analytics are especially important for omnichannel customer support providers such as Scicom. Through a robust customer relationship management (CRM) platform, a combination of speech, text, and desktop analytics can be collected simultaneously for a more comprehensive analysis of how customers interact with a client’s brand.
Self-service analytics are particularly useful for brands that have self-service portals or knowledge bases which allow customers to resolve issues on their own. These analytics help companies determine which products or services customers frequently face issues with, while metrics like page views, number of sessions and session duration help to enhance the overall self-service experience.
Predictive analytics are the most advanced analytics from a technological standpoint. By harnessing sophisticated artificial intelligence systems, predictive analytics can anticipate customer needs and determine the most efficient course of action. This is particularly useful when creating a personalised customer experience roadmap and can also help to identify bottlenecks during peak times that could lead to potential service failures.
How does personalised CX benefit from data analytics@f4
Data analytics play a significant role in improving the quality of personalised CX and make it easier for agents to equip themselves with the contextual knowledge they need to consistently deliver exceptional experiences to customers. Scicom leverages data analytics to continuously improve customer experiences in the following ways:
Do you want to know more about how data analytics can help your organisation achieve its customer support goals@f5 Reach out to us to find out more!