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Reflections on Service Innovation and Operation Management in the Era of Big Data

2018-04-25 11:33:48 Author: Shift in Online Services Limited, Guangdong Branch Guangzhou Center Lin Xi orders Source:CTI ForumComment:0  Click:


This is a connection to a ubiquitous society. This is a society where services are everywhere. This is a society where data is everywhere. Everyone is accessing services and interacting with society at any time, any place, anywhere. Nowadays, everyone is closer to the world than any other period in the past. There is a sense of participation and presence. Everywhere service makes life more convenient and beautiful.
The tentacles of the Internet have been continuously extended. The Internet platform has created great connections, created great services, and created big data. The information that Big Data returns gives us a better understanding of customers and creates more satisfactory services. The call center born for the service, from a call service center with only one telephone line to a multi-channel multimedia customer contact center, such as SMS, website, Weibo, Wechat, and APP, to a customer with a large number of customer contact data. Relationship Management Center. In the era of the big data era, call centers should take advantage of the situation, seize the commanding heights of the big data territory, constantly optimize service models, enhance service experience, and introduce new service models and products based on multi-touch mass data. The author believes that big data has a lot to do in the call center, and it can be considered from the following several scenarios to gradually deepen and apply.
(I) Operation side: Application thinking of big data in customer's precise service
  1, customer voice mining:The role of the “echo valley” and “sensor” of the call center was utilized. From the massive customer contact data, the customer voice was heard, the strongest voice of the customer’s appeal was tapped, and the customer’s pain points were poked. The feedback business unit promoted the optimization of the business and product design. , improve customer experience and create surprises for customers. There are two main applications:
(1) Establishing an early warning model through big data collection, prior to customers discovering problems, prior to complaints and resolving problems, turning passive services into active care, and preventing customer perception damage.
(2) Analyze the reason of calls and reconfigure the service model based on the "demand-strategy-resources" management model, integrate service resources, optimize service strategies, and continue to create the best service experience.
  2, special customer management: Voice tagging of historical calls from troubled customers, sensitive customers, and high-frequency calling customers into text data to analyze customer call reasons, business concerns, and potential appeals, combined with customer basic attribute information (professional, age of network, ARPU Values, etc.) Do a good job of customer portraits to form customer tag libraries and corresponding service policy libraries. Based on the "two libraries," the existing service flow was restructured, and special seats or dedicated account managers were set up to handle and improve the specific customer service orientation.
  3, big data precision marketing:Big data precision marketing includes two aspects: user needs mining and target customer identification. Taking China Mobile as an example, Big Data implements the following four-step strategy in terms of user needs mining and accurate marketing.
In order to meet the needs of a particular marketing campaign, it is often necessary to select or delineate a target customer base for marketing resource delivery. In terms of target customer group identification, large data modeling needs to be performed to output customers who satisfy the conditions to the marketing planning department.
  4, improve traffic prediction accuracy:The accuracy of the traffic prediction is mainly determined by data and algorithms. At present, the algorithms commonly used in traffic prediction in the industry mainly include least square method, moving average method, and exponential smoothing method. The common feature of these algorithms is that they do not require a large amount of data, so the accuracy of prediction is naturally low.
The traffic forecast supported by big data technology can not only broaden the data volume, but also adopt more excellent algorithms based on big data modeling, such as multiple nonlinear regression methods and artificial neural network BP algorithm.
Prior to this, limited by the amount of data, these excellent algorithms couldn't be forced to perform as much as possible. Once the dimension of the influence factor of the traffic demand factor is as wide as possible and the amount of historical sample data of the same impact factor is as much as possible, the accuracy of the traffic prediction can have a qualitative leap, or even a nearly perfect result. Practice has proved that the accuracy of prediction can be as high as 99.3% or more through big data collection and cleaning, algorithm optimization, and model fitting. This will provide more accurate guidance for follow-up personnel scheduling, on-site scheduling, resource allocation, and manpower recruitment.
(II) Management side: Application thinking of big data in human management of call center
Customer management and employee management are the two main lines that run through call center operations management. KPI and KHI are equally important for labor-intensive large-scale call center management. They only serve employees like service customers and reduce employees like energy-saving emission reductions. The pressure drop is negative and creates happiness for employees just like creating profits. Only then can a "two-wheel drive" be formed.
1. Human resources management in the Internet era is facing challenges and reflections
Human resources management faces a more complex internal and external environment. People's needs are diversified, individualized, the flow of talents is accelerating, and the viscosity of people is reduced. As a result, it becomes the new normal for the development of enterprises. Only innovation and change can break through the predicament of management. .
2. Manpower Big Data Helps Call Center Realize Humane Management
In the management of labor-intensive large-scale call center personnel, the issue of human resource management is even more prominent, especially in terms of training management, shift management, loss management, and incentive management. The author's China Mobile Online Guangzhou Center has thousands of seats and nearly 2,000 service scales. It is a large-scale call center in China and it is difficult to manage personnel.
In 2015, Guangzhou Center took the lead in setting up the Manpower Big Data Management System at China Mobile Online Guangdong Company, launched the exploration and practice of intelligent management of call centers, and built a three-dimensional and three-dimensional large data warehouse by integrating employee-centric and comprehensive data. And applied to the individual management of each module, which greatly improved the sense of belonging of employees, and the turnover rate of personnel in the past two years hit a record low.
After more than 10 years of development, the 10086 hotline service operation has been composed of 70, 80, 90, or even 00 employees. The age span is large, and the differences in personnel ideology and needs are also large. The traditional “one size fits all” management approach is probably very It is difficult to continue, and the management of manpower-based big data is just right.
Imagine that in a labor-intensive call center where the workforce is complex and the quality of front- and back-office personnel is uneven, it seems to be falling into the clouds for management and there is no effective grasp. The human big data management system serves as the eyes and brains of management. The ability and quality model based on big data, talent assessment (competency model), human post matching, departure warning model, employee portraits, and tag library have become the brains of HR. Management tools can help managers to clear the cloud and provide navigation for management.
For example, in terms of setting up a training system, the company combines the portraits of its employees and makes good lesson plans with the times. For example, there are health care needs after the 70s, investment and financial needs after the 80s, and the need for emotional analysis after the 90s.... Staff empowerment varies from person to person, reflecting the company’s personalized and precise management and caring and enhancing the sense of belonging of employees. .
For example, in terms of scheduling management, the company integrates employee tag libraries, coordinates the need for personalized schedules, optimizes the shift system based on the traffic laws, and combines employee preferences with flexible and flexible shifts such as the moon classes that enjoy night service and legal holidays. The gold medals for the positions, the peak classes for the busy hours, and the visiting classes for the employees who are returning from their hometowns during the holidays provide relatively comfortable gold classes for pregnant mothers.
For example, in the company's personnel loss management, the past has always been remedied afterwards, but it is useless. It often appears to be passive, and no effective hand is available. Traditional management concepts mainly rely on team building to maintain employee loyalty, and devote a lot of resources to management and investment. However, they still have little effect on personnel loss prevention and control. This confirms the phrase "What the company can give is not necessarily what the employees want." Enterprises cannot perceive and grasp the needs of employees. Resources cannot be targeted, and the pressure on cost centers is even greater. The loss of call center personnel is behind the huge amount of company recruitment and training costs.
Looking at the post-70s and 80th employees in the Guangzhou center, the resignation is an isolated phenomenon. If there are clear reasons, it will not lead to a wave of collective resignation; and the post-90s resignation will often have the collective character of “infectious” resignation. This shows how important it is to be aware of the pre-departure warning!
For example, in addition to traditional spiritual and material incentives, the company’s personnel incentives can also be considered to increase incentives and willingness to meet two types of incentives, to meet the individual needs of individual incentives, and enhance the perception of motivation.
All in all, human resources management based on big manpower data is the real humane management approach. With the maturity of big data technology, it is only a matter of time to subvert the traditional service and management model. The author believes that "big data + service" and "big data + management" will be the only way for future smart customer service.
 
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