Telecom Marketing Auto-Modeling

TELECOM MARKETING
AUTO-MODELING SOLUTION

Opportunities & Challenges Pain Points Introduction Advantages Values

Opportunities & Challenges

With the rapid layout of new business and the development of business scale, both the internal digital and intelligent operation of operators and the ability base of creating digital and intelligent transformation for external customers have put forward higher requirements for operators' relevant technical capabilities in the fields of marketing, service, management, operation, and maintenance.

In the process of digital intelligence application, operators will face a variety of scenario modeling work. They have put forward urgent requirements for "efficient" modeling and "fast" forecasting and need more automated and business closer solutions to reduce the modeling threshold, reduce modeling costs, and shorten the modeling cycle.

Pain Points

The surging demand for modeling

The current demand for machine learning modeling in various business lines is increasing, especially in the refined operation of existing and incremental customers, such as improving the efficiency of human-goods-field matching, and fine modeling for different package levels and customer groups, all of which have led to a surge in modeling demand.
Low efficiency of traditional modeling approach

It is difficult for data scientists to model with coding to meet the surge in marketing modeling needs, and at the same time, due to the constant changes in the marketing environment and data situation, the demand for model iteration becomes higher, which is difficult to be supported by the traditional inefficient modeling approach.
Limited tool function and high usage threshold

The existing modeling tools have relatively limited functionality and high threshold, and are only suitable for professional data scientists to develop algorithms. In the modeling process, the business specialist needs to go through repeated communication after presenting modeling requirements to the IT department, resulting in high time costs.
Difficulty in reusing modeling scenarios

The group-province-municipality organizational structure of carriers leads to frequent demands for reuse of the same scenario across different municipalities. The traditional modeling approach is difficult to meet the need to improve the efficiency of modeling and model application through scene reuse.

Introduction

DataCanvas telecom marketing auto-modeling solution can greatly reduce the threshold of artificial intelligence machine learning modeling, enable business elites to easily start modeling, reduce modeling costs, and shorten the modeling cycle. The solution ensures the accuracy of the model, improves the efficiency of business intelligence support, rapidly applies data intelligence to the production and operation process, and greatly improves the business value and application efficiency.

The solution is applicable to the following operator marketing scenarios: user churn warning, online user clustering, terminal replacement prediction, user online behavior analysis, customer dual card identification, DPI mining, potential customer mining, different network family width identification, marketing response analysis, 5G user marketing, package forecasting, package migration, customer value improvement, etc.

Advantages

Guided one-click modeling

One-click model training can be completed by simply selecting a scene template, significantly lowering the threshold for machine learning.
One-click prediction to assist decision-making

After the automatic training of the model is completed, you only need to select a specific customer group to make predictions with one click. The results are displayed in a friendly visual way, quickly transforming machine learning results into decision-making basis.
Mutual verification of machine learning and human experience

Compare the tags used in the customer group rules with important features of the model training results to obtain the customer group matching degree, and seamlessly integrate it with the original marketing rules, so that human experience and machine learning can mutually confirm each other and improve marketing accuracy.
Knowledge accumulation

AI assets such as features, scenarios, and models can be quickly deposited and shared on the platform, facilitating the accumulation and reuse of knowledge.