Big rewards from big data for telco operators

DatabigstockOne of the most critical challenges for telco operators today is managing customer churn. Many operators are still pushing their products instead of listening to the needs of their customers. Allocating the marketing budget at the right time with the right messaging to new and existing customers are the telco CMO’s bread and butter when decreasing the declining average revenue per customer. An oversaturated and competitive market creates big challenges for telco operators. Winning over new clients has always been a priority, but that is of no use if simultaneously you lose your current business and revenue.

Demanding customers, emerging technologies, and new competitors are transforming the environment in which operators do business. A real-time response utilising customer and enterprise data is needed to decrease churn. Knowing your customers’ needs, values, eligibility and loyalty allows you to be more relevant when offering the right price to the right person and at the right time. The power of the retention message can be amplified by integrating the available customer data and employing the response directly throughout all available channels, significantly increasing engagement among customers.

Timely and targeted advertising has played a pivotal role in facilitating the easy-switch culture of the telecommunications market by enabling rival providers to compete on price, top-of-the-range phone availability, data allowance, network quality and many other influencing factors.

Working with T-Mobile recently, it became clear that in order for the company to optimise its offering it needed to provide customers with relevant offers and a consistently good experience each time they interacted with T-Mobile. It was necessary to mine its own customer database to find out what people were most likely to respond to, based on their previously – logged preferences. The value of employing first party data in these instances is in the fact that the ecommerce team can pull heavily on users’ behavioral trends from CRM data.

“One of our biggest challenges has been to successfully harness the data within our CRM system to help us understand our customers better,” says Amanda Bouwmeester, T-Mobile’s Online Marketing Manager. “We wanted to find out what our customers’ purchasing trends were, what would propel them to buy from us and what we could offer them that would make them truly engage and stay with us as a service provider. In order to do this, we first needed to bring together two very different areas of the business: the CRM database where our data is housed and managed, and the eCommerce part of the business that deals with customer offers and digital marketing.”

Customer Lifetime Value
Along with the switch of voice calls to data, revenue streams of telecom providers shifted. For decades telco operators generated income via calls and text messages. The network usage has recently changed with IP data being the most important component of the consumers’ activity. Many operators were forced to devalue their business model and rethink their customer lifetime value.

By integrating this data into your online media strategy, the profiles and customer lifetime value will ultimately greatly increase your ROI. But how does it work?

Enabling effective online marketing based on customer value requires careful segmentation into separate audience pools. Segmentation converts a wide array of customer information into actionable profiles such as grouping by churn likeliness, current customer value, loyalty and eligibility for upgrades. By doing so it is possible to find out who to target, at what price, and with which relevant proposition.

In addition to customer data, the average revenue per user (ARPU) can be increased by giving more options in regards to rate plans. The most common algorithm applied in dynamic banners is showing the proposition that is viewed last. This algorithm matches the interest of the prospect based on parameters and variables linked to the customer, makes a suitable recommendation and thereby prompts an action. Here we can also take into account whether a client is a new acquisition for the company, or whether they are a renewal. In the case of a renewal, there is an opportunity to upsell the current client with a better, or more suitable plan or service.

This commonly used business rule is geared towards relevance and is therefore purely based on visitor preference and not on the need of the advertiser. By adjusting the algorithms towards advertisers’ needs, the ARPU can be increased.

For example, for a high-end handset the algorithms were set to offer a more relevant rate plan, the share of higher rate plans sold increased significantly from 19% to 28%, a surge of 47%.

Telecom players that use customer data to find the right types of customers and build on those relationships can turn data into revenue. By using customer analytics to develop relevant products and services, telecoms can keep and develop their best customers.

The use of RTB can be employed to rapidly gain insights on the performance of different strategies to make use of customer data. Telecom providers can profile their customers based on rate plans, choice of device, demographic data, login behavior and many more variables.

Through the application of RTB, different profile setups can be A/B tested, enabling marketers to optimize in real time to the best performing outcome. The outcomes of these tests can be applied throughout all channels. Profiles that prove to be most valuable in display can optimise the effects of telesales or direct mail.

The use of RTB can rapidly gather data on the value of certain profiles, but also other channels accumulate insights on the success of different approaches towards individual customers. The feedback of different channels on individual customers can be easily aggregated in the profiles employed by the RTB channel. This results in display exposure that’s perfectly aligned with the overarching retention approach.

Mendel Senf is CEO of YD.