Customers, products, channels, and opportunities: a brief analysis of CPCT's refined operational strategy

With the popularization of mobile phones and the saturation of the telecom operator market, the increase in operator users mainly comes from re-joining the network after abandoning the card and tapping the potential of other networks. In recent years, operators have been gradually reducing KPI assessments for new development and paying more attention to the management of existing customers. On the other hand, as operators' tariffs and traffic fees have continued to decline in the past decade, their marketing resources have continued to shrink, and the traditional model of casting a wide net and operating in an extensive manner is no longer applicable. Refined operations, scientific allocation of resources, and continuous improvement of resource allocation efficiency are the operating strategies that operators have been adhering to in recent years.

How to tap into user value among the hundreds of millions of existing users and conduct refined operations is a difficult problem faced by many industries. For telecom operators who have structured, multi-dimensional big data, relying on data for refined operations is inevitable. What we are mainly talking about today is the CPCT strategy (Customer, Product, Channel, Time) using data to recommend the right products to the right customers at the right time through the right channels . We will explore how to do this from four dimensions: Customer, Product, Channel, and Time.

1. Customer

1) Customer Segmentation

For telecom operators with hundreds of millions of customers, the first step in operating customers is to segment them. Recommending different products based on different customer attributes, behaviors, and preferences is the basis of refined operations.

Different industries have different customer segmentation dimensions, and customer value and customer stability are commonly used segmentation dimensions.

The most commonly used customer segmentation dimension is customer value. Based on customer value, we segment customers into high-value customers, medium-value customers, and low-value customers. Sometimes it is necessary to divide them into four or five levels based on thematic analysis, such as high, medium-high, medium-low, and low-value users. For example, when we were doing customer segmentation for a certain province's mobile phone business, we divided customers into high-value users, medium-low-value users, and low-value users due to the need for matching strategies.

In the early years when telecom operators’ charges were higher, customer value followed the 80/20 rule, that is, 20% of customers could contribute 80% of the value. However, when telecommunications charges become cheaper and cheaper and become mature products, the distribution of customer value changes significantly. High-value customers can only contribute 40-50% of the value. Telecom operators must also pay attention to medium- and low-value users and low-value users. This means that the users that should be focused on in different product life cycles are different.

In addition to the value dimension, customer stability is also a commonly used customer segmentation dimension. According to their stability, users can be divided into highly stable users, medium stable users and low stable users. Their stability can be divided according to weights based on indicators such as the length of time the user is online, the size of their social circle, whether it is a high-quality number, sticky services, and whether they have a contract. The larger the user's social circle, the longer the time spent online, and the more sticky services they have, the lower the probability of leaving the network.

The second method of customer segmentation is to label customers and establish a unified customer label library. The above customer value and customer stability are types of customer labels. A user can often be labeled with dozens of labels. When customer tags are detailed enough, marketing activities can be directly targeted at users with certain customer tags.

Customer tags are divided into static tags and dynamic tags. Dynamic tags must be updated in a timely manner to ensure marketing effectiveness.

2) Operational customer priority

When marketing resources are relatively tight, the customer base is too large, and the task is too heavy, customers can be segmented according to value and stability, and different customer segments can be prioritized.

Figure 1 Customer segmentation determines operation

Camp Priority

3) Customer segmentation operations

The operational focus of different customer segments is different. High-value users need to use "rebar bundling", paying certain marketing costs and marketing services in exchange for customer loyalty. Medium-value users need to use "sticky bundling", using various sticky factor services, such as family network, campus V network, family packages and other layers of bundling. Low-value users need to be maintained at low cost, and small favors can often retain users.

2. Product

Even products from the same company often have different positioning and selling points. Some products are used to attract new users, some products are for high-end users, and some products are to prevent user churn.

The basic products of telecom operators are numbers, packages (voice, data), and terminals. Sticky products include virtual networks, points, credit, star services, broadband, etc. Value-added services include reading, games , music, video, animation, etc. With the popularity of smart phones, terminal-based contract plans are one of the main means for operators to bind users. The marketing strategies of the three operators have different focuses. China Mobile relies on 4G network layout to make up for the network speed disadvantage in the 3G era. China Unicom relies on cheap traffic and terminals to vigorously snatch users. The integration strategy that China Telecom has always implemented is accurate in every aspect.

Divide products according to their different positioning and selling points and establish a product library, manage products in a unified manner to achieve targeted results. Product segmentation can be based on dimensions such as price, stickiness, and customer preference.

Positioning users according to the product’s own advantages and selling points is also one of the commonly used operational strategies. Especially when new products are launched or product promotions are in progress. Telecom operators often take advantage of their data to conduct data modeling and tap into potential target users. This is also a commonly used method for refined operations. With the increasing exposure of users to data and the rise of big data, this method has gradually been adopted by various industries with customer data.

Common methods of data mining include decision trees, linear regression, neural networks, etc. The marketing effect of using data modeling is often 3-5 times that of natural marketing. For telecom operators whose marketing resources are constantly tightening, big data modeling marketing has become a common means of precision marketing. It should be noted that data mining should not be the driving force of business analysis. The direction should be determined from business insights and then verified using data models.

3. Channel

With the continuous enrichment of online and offline channels and the increase in customer touchpoints, omni-channel marketing has become a trend. Telecom operators mostly adopt an online and offline collaborative approach, with text messaging as the front line, the group and provincial companies deploying online, and provincial and municipal branches doing offline publicity and promotion.

Understand the advantages and disadvantages of each channel, the users that can be reached and their characteristics, and adapt marketing activities according to the characteristics of different channels. The SMS channel can reach all mobile phone users of the website, but the marketing effect is not good. Sales staff at business halls and outlets have face-to-face sales opportunities, but their contact with users is limited. Online and mobile business halls can connect to all logged-in users, but passive marketing displays are difficult to attract user attention.

4. Time

Timing is also a key factor in refined operations. In the management of the customer's entire life cycle, marketing and service at critical moments can often achieve twice the result with half the effort. Customer behavior often changes at critical moments and can be identified from data, so precise service systems at critical moments often become one of the research topics of telecom operators.

The key moments of telecom operators include the first moment, abnormal moment, special moment and do not disturb moment. The first moment refers to the first time a certain type of business is activated or a certain type of service is experienced. At this time, customers are more likely to accept marketing-complementary businesses. The abnormal moment refers to the moment when an abnormal situation occurs during the customer's use, affecting the customer's normal use or customer interests, and may lead to customer loss. At this time, corresponding services should be provided according to the customer's abnormal situation and reasons. Special moments refer to customers’ birthdays, holidays, etc. Using holiday marketing and birthday care can improve customer satisfaction and reduce their aversion to marketing. Do not disturb time refers to the time when customers are inconvenient or do not want to receive service. Marketing activities generally exclude do not disturb users.

The key to seizing opportunities is to sort out key moments, identify users who meet the characteristics of key moments, develop corresponding service processes and standards, improve customer satisfaction, and reduce customer churn.

This is the CPCT strategy of refined operations. Any of the following elements, including customers, products, channels and timing, can become the driving force of operations. This strategy is not only applicable to telecom operators, but also to other industries with large user base, rich data and fierce competition.

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This article was compiled and published by @蔷薇石 (Qinggua Media). Please indicate the author information and source when reprinting!