In a crowded market, communicating with customers in a personal and relevant manner is more important than ever. This means not just addressing a customer by their name, but providing them with tailored information that they are likely to act upon – based on customer history, known preferences, and an understanding of what might bring better, or additional, value to them.
Data has become a key competitive advantage as the arrival of powerful analytics solutions and machine-learning algorithms helps service providers move from reactive to predictive analytics. This allows them to not only respond to their customers’ needs as soon as they arise, but be a step ahead of their customers’ expectations – perhaps pre-empting a complaint by pinpointing an issue quickly, or even persuading a customer whose most recent usage patterns indicate a lack of interest to stay by offering him new services or a more suitable price plan.
However, whilst the value of personalization and bringing together data from disparate systems is becoming well understood, the practicalities of achieving it are less apparent. What’s needed are clear, strategic approaches that closely tie investments in data handling and new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to improve customer satisfaction, likeliness to promote a service provider to friends and family and increase the number of new customer wins.
In theory, service providers already have all the data they need to do this but it’s the how to do it that’s missing, often because the data sits in separate, siloed systems. The billing system for example is not integrated with the call center logs, the CRM system or the product catalog. Important intelligence, such as what the customer said during a phone call, is not passed on or shared, making it impossible to establish an ongoing conversation, or to manage and maintain the customer journey.
To achieve true customer-centricity, service providers need to connect and correlate the data they hold on each customer. They must be aware of everything that has been said and done between them and the customer, no matter whether it was online, on the phone, or in-store. What’s more, this information must be shared across all customer interactions and channels. Only through this level of awareness and single customer view can a foundational strategy be put into place to access or extract data and consolidate it, potentially in a data warehouse. Key customer insights will allow service providers to create personalized communications and campaigns. The data can also be fed into AI and ML engines for processing to enable new targets and propositions to be identified.
This level of continuity is essential in making the customer feel like they really matter – bringing the personal touch back to customer communications.