Explore what points to focus on in data analysis

In conjunction with customer center management practices, the following key points are typically focused on in analysis.

(1) The distribution of data is more important than the mean of the data. Due to the weak representation of the individual on the whole, the customer center, many data with the average value of expression, such as the connection rate is the number of a period of time connected to the number of overall demand than on the number of a team's quality control results is the average of all members of the team, the average value can represent the whole, but ignored the uniqueness of the individual. Take the connection rate as an example, the whole day's connection rate is 85%, it seems very high, but this 85% is likely to be 90%, 80%, 95%, 50%, even including the average of 0 from each time slot, if subdivided into different skills and more hourly segments (such as 5 minutes, 15 minutes) the difference is even greater, which is like me and the "richest man! It's like me and the "richest man" averaging out an average of wealth that doesn't make any sense. Therefore, it is necessary to analyze the distribution state of data frequently and pay attention to the data that deviates from the average value. In the operation and management of the customer center, if some of the data that deviates from the larger data has been improved, the overall average value will also be improved accordingly, which is also an important way to improve performance.

(2) Their own progress is more important than comparing with others. Often my peers ask me for certain data to understand their level of operation. This is necessary in the initial stage of operation of the customer center or when a new field is opened up to help establish a clear reference system, but for a center that has been in operation for many years, these data are of little significance. Not to mention the fact that customer center data varies greatly from one industry to another, even in the same industry, and even in the same center, the data can fluctuate drastically due to its own operational strategy. Such a single point of data values and their own comparisons do not make any sense, often adding to the trouble.

For example, customer satisfaction is often different for different industries, and even for the same industry, there are huge differences between customer satisfaction in Guangdong and Shandong, and similar differences between Shantou and Guangzhou. The operation and management methods and strategies of different customer centers are worth learning and borrowing from each other, but the specific data of the operation is relatively less significant. In the operation, it is important to constantly compare with their own past, you can make ring comparison and year-on-year, and even put the same type of data in the past few years to compare together, at the same time, there should be a clear explanation for the deviation of the data.

(3) Fluctuations and trends in data are more important than the data itself. There are generally two directions in the operation and management of the customer center, namely smooth and continuous improvement. Reflecting these two requirements from the data is a smooth curve that continues upward, with fluctuations as small as possible, while the trend should be good. For some data with target values, it is important to try to maintain a smooth curve above the target value. Indeed, while small occasional deviations are not important, it is important to be concerned about how often these deviations occur and whether the range of deviations is within acceptable limits.

Even an operational metric that has no empirical value can eventually achieve a very high level of operational excellence if you keep the data on a consistently upward smoothing curve.

(4) Inferiority is more important than yield. In the production field is mostly concerned about the rate of finished product, the rate of finished product is calculated as 1 minus the rate of scrap, it seems that the two indicators are the same, just expressed in a different way, but when an indicator involves the human factor, this calculation method is no longer applicable.

Take the connection rate as an example, many customer centers are very confused, why our daily connection rate is very high, but customers always say we are difficult to connect? There are two reasons for this:

First, there is the problem of how it is calculated, one is the data from the system and the other is the data perceived by the customer. For example, if the connection rate is 85% on a given day, that means 15 out of 100 calls are not connected. Assuming that 10 of the 15 customers who did not connect call again (these re-call volume has been counted in the total call volume), the result of the connection, then the system statistics of the connection rate is 85%. But according to a single customer to calculate is not the same, the number of non-repeat customers is 90 instead of 100 (assuming that all connected customers are not repeatedly dialed), the 10 re-calls to get through the customer will think that the hotline's connection rate is problematic, the survey will be that the hotline is "difficult to get through", if the full survey of all customers on that day If the full survey of all customers that day, the connection rate will not be 85%, but (90-15)/90=83%.

Secondly, it is a human trait to be more sensitive to and remember negative information. Negative unanswered experiences decay more slowly and are more memorable than positive connected experiences, and a single unanswered call requires multiple calls to correct. When asking customers about their connection perceptions, negative memories are evoked and positive memories are weakened.

(5) Value is more important than revenue. When it comes to value, people usually think of the measure is often money, is the income, but the value should not be measured only with money, it is like evaluating a child is not a good child can not just look at the results, should be from multiple angles, more comprehensive evaluation. If only academic performance is used to evaluate a child who specializes in piano or painting, then the unfairness is obvious. For most customer centers, especially inbound centers, revenue is by no means a strength, the real value of the customer center should be reflected mainly in the maintenance of customers, which is also the purpose of the organization to establish a customer center, to improve customer loyalty through every contact with customers, to tap the possible needs of customers, the purpose of marketing in the service should also be to maintain the customer.

When the customer center managers think they can show their own value through the income, is embarked on a "road of no return", is the use of their own least ability to go with the marketing department, marketing department, sales office of the strong PK. the result is that the staff is more and more bitter, the center of the operation began to be unstable, performance is getting worse and worse.

The data itself is a very important part of the process.

The data itself has no meaning, the data is analyzed to guide the operation is meaningful, the operation is to be carried out around the goal.

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Table of Contents

Preface

Introduction

Part I: Emotions and Stress Management

Unit 1: Potential and Self-Efficacy

Unit 2: Understanding Emotions and Stress

Unit 3: Ways to Manage Emotions and Stress

Part II: Practical Management Psychology for Customer Centers

Unit 4: Motivating Employees