Mature telecom markets have become extremely competitive. But aiming to take customers from competitors is costly: It typically involves giving new customers special discount rates, which will eat into revenues.
A better way to pursue growth is to take a radical customer-centric approach that seeks to increase the value to the company of each customer. Doing this requires deep knowledge of the customer that can be used to create propositions they are likely to accept.
The knowledge starts with basic data, such as socio-demographic profiles and consumption history. But these only go so far. To figure out the best offer to present to a customer – and whether or not to approach them proactively – telcos need to know more about customer behavior. In particular, they need to know whether customers are likely to upgrade to a more expensive package, downgrade to a cheaper one, or quit for another operator. They can do this through three steps: Use big data and analytics; create deeply personalized value propositions; and offer these via the multiple channels of the company’s commercial operations.
Focus on the customer
A radical customer-centric approach means that the unit of analysis – and of key performance indicators – is the customer: the household or enterprise being served and not product lines or revenue generating units (RGU). Marketing and commercial operations should not primarily aim to sell a product. Instead, their goals should be to satisfy a customer need. That implies a shift from product-based campaigns to customer-base management centered on the best proposition to make to an individual customer.
First, telecom operators need basic customer lifecycle information. That starts with each customer’s socio-demographic profile, credit scores, and paying history. In addition, telcos need to know the value of the products customers have subscribed to over time, their consumption related to these products, and the resulting economic value. They should also record the different types of service a customer has subscribed to and their interactions with the telco’s various sales channels. The resulting data is an excellent base from which to know customers and their experiences and to support customer-centric marketing, as well as commercial and care operations.
A step further: Customer behavior prediction
To take customer focus a step further, telcos can try to predict customers’ imminent behavior. For example, when a customer calls to ask for the remaining months in her commitment-to-stay clauses, that can be considered as a sign that she might be about to switch to another operator. Other relevant predictions of individual customer behavior are:
- How likely is a customer to spontaneously downgrade her TV subscription?
- How likely would this same customer be to accept a heavily enriched TV offer – for example, including premium sports channels – at a personalized price, instead of downgrading her current subscription?
- How likely is another customer to accept a specific, personalized cross-sell offer, such as high-speed fixed broadband bundled with a mobile plan?
- Is that customer more likely to accept the offer when contacted through the telco app than by a phone call?
Answering these questions needs another layer of information about customers. This can be generated by machine learning based on big data, and the resulting predictions provide a deep, 360-degree view of an operator’s customers.
How to use the new knowledge
Many operators already use business intelligence and big data to derive such information. However, they have not all integrated the data and models into their daily customer interactions. To get the best out of their knowledge, operations should organize and update it, and make it accessible for business purposes. That means making it easily available for planning, reporting and operational purposes.