Reduce operating costs: Reduce operating costs, reduce the workload of manual operation and maintenance personnel through automation, and improve efficiency

Author: Zen and the Art of Computer Programming

1 Introduction

With the rapid development of the Internet, the vigorous development of mobile Internet, e-commerce, and the transformation and upgrading of traditional enterprise businesses, more and more enterprises are facing higher and higher operating costs. How to reduce operating costs has always been a difficult problem for business operators, and in recent years, automation has become an effective method to reduce operating costs. Currently, according to IBM Cloud computing data, the number of global mobile application users is expected to reach 100 million per month, and these applications used by enterprises have a great impact on the operation of their businesses. Therefore, automation is important to reduce operating costs. One of the directions. In the past decade, in terms of operating costs, the rapid development of the mobile Internet and Internet financial industries has promoted the need for data collection, processing, and analysis. In this context, cloud computing and machine learning technologies offer new possibilities for reducing operating costs. This article will explore ways to use AI technology to improve operational efficiency based on the latest research in related fields.

2. Explanation of basic concepts and terms

(1) Automated operation

What is automated operations? Automated operations refer to using some automated tools or processes to help enterprises manage various processes to achieve more efficient operations. Automated operations include the automation of daily workflows, automated data analysis, equipment resource management, etc.

(2) Operating expenses

Operating expenses are the total amount of money a business spends to pay employees, purchase goods or provide services. Among them, daily basic expenses, business expenses and other expenses constitute the main part of operating expenses. Operating expenses are expenses incurred in the daily operation of an enterprise, and also include expenses incurred by the enterprise in participating in business activities, such as office fees, conference fees, agency accounting fees, etc.

(3) Product/service sales

The products sold or services provided by an enterprise in the market are products/services. Each product/service requires a certain price before it can be sold to customers. When the price of the product/service is higher, the operating cost of the company will increase.

(4) Customer satisfaction

Customer satisfaction with an enterprise is a measure of its success, reflecting its loyalty to the brand, confidence in product quality and service attitude. Customer satisfaction with a company not only directly affects the company's profitability, but also affects the company's treatment, care and training of employees.

(5) Target customer group

Target customer groups usually include company internal employees, external customers, partners, suppliers, stakeholders, etc. The target customer group determines the target audience of the enterprise. Different target customer groups have different needs and expectations, which is of great significance for reducing operating costs.

(6) External channels

External channels generally include competitors, partners, third-party collaborators, etc. External channels play a vital role in the promotion, marketing, marketing, and after-sales support of a company's products or services.

3. Explanation of core algorithm principles, specific operating steps and mathematical formulas

The methodology for reducing operating costs mainly has the following three aspects: (1) Identify revenue sources: By looking at the revenue of each channel, understand the mainstream consumption behavior among the customer base and classify it. (2) Extract core income: eliminate non-core business income that has nothing to do with mainstream consumer behavior. (3) Improve core efficiency: Use precision marketing strategies to increase the revenue retention rate of core business and improve the efficiency of core business.

(1) Algorithm model—Growth hacking

Growth hacking believes that successful companies often invest huge resources in the early stages to establish an effective growth mechanism, then start to solve core problems, and then expand operations. Therefore, when an enterprise faces the challenge of rapid expansion, it should consider adopting growth hacking principles to improve core efficiency. The CKER law of growth believes that companies improve core efficiency through the following methods:

Accurately locate the core customer group: The first task of the growth hacking rule is to accurately locate the core customer group so that they can get the best service experience. To this end, it is necessary to design targeted advertising, corporate brochures and other publicity methods to focus on the core customer groups.
Collect effective information: Enterprises collect user feedback by collecting user feedback, collecting usage behavior data, collecting user information, etc. Through these data analysis, key information such as user preferences, preferences, and satisfaction can be obtained, based on which precise positioning can be achieved. At the same time, the information collected can reflect the real demand and market status of products and services, making product development more accurate.
Improve core efficiency: Growth hacking principles believe that companies can improve the efficiency of core business by improving product functions, optimizing product architecture, updating marketing strategies, adjusting marketing channels, etc. Gradually optimize products and services through innovation, iteration, user participation, etc., and ultimately improve the revenue retention rate of core business.
Use artificial intelligence: Growth hacking principles also recognize that when implementing growth strategies, it is necessary to combine emerging technologies such as artificial intelligence, machine learning, and data analysis to improve product effectiveness. For example, image recognition technology can be used to analyze users’ behavior patterns when watching videos and recommend products or services with similar content. Through the above methods, enterprises can reduce operating costs and improve the efficiency of core business.
(2) Algorithm model—AARRR model
The AARRR model is a marketing strategy that divides the marketing cycle into four stages, namely Acquisition, Activation, Retention, Revenue, and Repeat. Each stage has corresponding activities, and each stage has clear outcome goals. Therefore, the AARRR model can be used to identify, extract, release or stimulate marketing factors to improve the efficiency of core business.
(2.1) Acquisition stage: This stage is mainly to attract, guide and acquire target customer groups, including recruitment, recommendation, free trial, free gifts, etc. The goal of this stage is to expand the core customer base.
Activities: Try to attract users’ recognition and loyalty to the product through personal connections, traffic, word-of-mouth, etc.
Result: Expand core customer base and form market loyalists.
(2.2) Activation stage: This stage refers to attracting, activating, and retaining target customer groups, and is achieved by improving the performance of core business. The goal of this stage is to achieve sustained attention from the core customer base.
Activities: Improve customer satisfaction by providing coupons, discounts, rebates, product upgrades, download trials, reviews, etc.
Result: Promote the core customer base to continue to pay attention to and purchase products or services, and enhance the popularity and love of the core business.
(2.3) Retention stage: This stage refers to maintaining the loyalty and satisfaction of the core customer base and continuing to provide high-quality products or services. The goal of this stage is to continuously expand the core customer base.
Activities: Provide continuous marketing support by providing promotional activities, brand promotion, VIP customer rights, shopping guide services, etc.
Results: Maintain the loyalty and satisfaction of the core customer base and enhance the core competitiveness of the core business.
(2.4) Revenue stage: This stage refers to the income from core business, increasing the proportion of core business income to more than 80% to achieve profitability. The goal of this stage is to improve the profitability of the core business.
Activities: Increase the revenue of core business by using various means, such as providing additional incentives, reducing fees, providing free resources, etc.
Result: Improve the revenue retention rate of core business and generate sustainable revenue.
(2.5) Repeat stage: This stage refers to the continuous optimization of marketing strategies to increase the final revenue of core business. The goal of this stage is to achieve sustained growth to increase the lifetime value of the core business.
Activities: Continuously improve marketing strategies, improve products or services, and enhance user experience.
Result: Achieve sustained core business development and drive the development of the entire industry.
(3) Methodology to reduce operating costs:
Based on the above algorithm models and marketing strategies, methodologies for reducing operating costs can be summarized.
First of all, we must pay attention to the establishment of target customer groups and focus on the definition and contribution of core customer groups. If you cannot accurately locate your core customer base, you will easily miss opportunities and affect the effective growth of revenue. Therefore, you can formulate a good activity strategy in the Acquisition stage to attract users; set up timely and effective promotional activities in the Activation stage to attract users; set up continuous brand promotion in the Retention stage to provide quality services.
When improving the core competitiveness of the core customer base, close attention should be paid to retaining reasonable cost levels, and efforts should be made to improve the efficiency of the core business through innovative methods. The AARRR model's iterative development of products and services can drive revenue growth and can track the operating status of core business in a timely manner. In addition, you can also increase the loyalty and transparency of the core customer base by giving permission to a "small team" for the product or service and paying in advance.
When achieving sustained growth, you can consider introducing new tools, services or concepts, and adopt comprehensive marketing planning and execution to better meet user needs and enhance the life cycle value of your core business.
4. Specific code examples and explanations
Combining the above algorithm models and marketing strategies, code examples in Python language are used to demonstrate how to improve operational efficiency.
(1) Python code example - deploy text classification model
Using text classification models, a large number of documents can be classified and automatically classified into different topics. In this way, business needs can be discovered and responded to more accurately. As shown below, a text classification model is deployed through the Python language to classify articles.
```
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
```

train = pd.read_csv('train.csv') test = pd.read_csv('test.csv')

X_train = train['text'] y_train = train['category'] X_test = test['text'] y_test = test['category']

pipe = Pipeline([ ('tfidf', TfidfVectorizer()), ('clf', MultinomialNB())])

pipe.fit(X_train, y_train) print("Test accuracy:", pipe.score(X_test, y_test))

## （2）Python代码示例——部署LSTM模型
LSTM模型是一种常用的序列模型，可以用于时间序列预测、时间序列分类、以及命名实体识别等任务。如下所示，通过Python语言部署LSTM模型来预测股票的收益率。
```python
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout

np.random.seed(0)
tf.random.set_seed(0)

# 数据准备
time_step = 7 # 一周股票交易天数
data_dim = 5 # 每个股票特征数量
output_dim = 1 # 输出维度（股票收益率）
batch_size = 32

def get_data():
  """
  获取训练数据
  """
  data = np.loadtxt('./stock_price.txt')
  X, Y = [], []
  for i in range(len(data)-time_step):
      a = data[i:(i+time_step), :data_dim]
      X.append(a)
      Y.append(data[i + time_step, -output_dim:])
  return np.array(X), np.array(Y).reshape((-1, output_dim))

class lstm_model:

  def __init__(self):
    self.model = None

  def build_model(self, input_shape):
    model = Sequential()
    model.add(LSTM(units=128, input_shape=(input_shape[1], input_shape[2])))
    model.add(Dropout(rate=0.2))
    model.add(Dense(units=1))

    optimizer = tf.keras.optimizers.Adam(lr=0.001)
    loss ='mse'
    metrics=['accuracy']

    model.compile(optimizer=optimizer,loss=loss,metrics=metrics)

    self.model = model


  def train(self, x_train, y_train, batch_size, epochs):
    if not self.model:
        print("Please build the model first.")
        return
    history = self.model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
    return history

  def predict(self, x_test):
    result = self.model.predict(x_test)
    return result

if __name__ == '__main__':
    # 获取数据
    X_train, Y_train = get_data()
    print(X_train.shape, Y_train.shape)

    # 构建LSTM模型
    lstm = lstm_model()
    lstm.build_model((None, time_step, data_dim))

    # 训练模型
    lstm.train(X_train, Y_train, batch_size, 100)

    # 测试模型
    X_test = np.loadtxt('./test_data.txt').reshape(-1, time_step, data_dim)
    predictions = lstm.predict(X_test)

    # 可视化结果
    plt.plot(predictions[:, :, 0], label='prediction')
    plt.plot(Y_test, label='true value')
    plt.legend()
    plt.show()