Embed Size (px)
Citation preview
“Big Data & Artificial Intelligence”
----How to Achieve Accurate Sales
Chongqing University of Posts and Telecommunications, Chongqing, China
1/30
Prof. Guangxia Xu
Outline
1. Background
2. How to Achieve Accurate Sales
3. Applications in Other Industries
4. Future outlook
2/30
Outline
1. Background
2. How to Achieve Accurate Sales
3. Applications in Other Industries
4. Future outlook
3/30
Sales to brand strategy
Internet technology and sales integration
“AI & Big Data “and sales Fusion
1.Background
4/30
1.Background
5/30
Significance and
Function
Update Sales Strategy In Time
Reduce Enterprise Sales Cost
Higher Performance Price Ratio
Enhancing Customer Satisfaction
6
Data Processing Takes A Long Time
Error In Analysis Result
Miss the Best Time of Sales Data
Quality Is Too Low
Data Filtering Is Difficult
Data Processing Error
Data Value Is Not Represented
1.Background
The Plight of Accurate
Sales
6/30
显示
7/30
Bank Insurance Online Shopping
AI
1.Background
8/30
Data Collection AI
Analysis Result
Accurate Sales
……Data Preprocessing
1.Background
9/30
1.Background
he
User Data Filtering
01
User Data Analysis
02 User Feature
Extraction
03
Match the Product
04
User Match
05
Marketing Evaluation
Effect
06
Marketing Closed Loop
Process
Outline
1. Background
2. How to Achieve Accurate Sales
3. Applications in Other Industries
4.Future outlook
10/30
2. How to Achieve Accurate Sales
Mail Recommendation
SMS Recommendation
Social Software Recommendation
Recommendation System
User Recommendation
UserManagement
Accurate Sales
Insurance Industry
Financial Industry
FourismIndustry
Industry Users
Insurance Industry
Financial Industry
FourismIndustry
Other Industry
Industry Customers
Sales Demand
Big Data Platform
Sales Users Group
Own Channel Sales
Business
Market Report
Ability Output
Ability Output
Accurate Sales Platform
Data Cleaning
User portrait extraction technology
pre-trained deep neural network
11/30
2. How to Achieve Accurate Sales
Potential User Value MiningMarketing
Model building
Feature Variable Selection
Basic Attributes
Financial Credit Hobby APP
PreferenceLocation
PreferenceUser
Portrait
Application Layer
Data Collection
Information Exchange
Terminal Information
Financial Credit
APP Application
Loss of Customer Retention
Value Customer Conversion
Personalized Recommendation
Stock User Interaction
Combined Marketing
Electricity Consumption
Business Layer
Data Layer
Correlation Analysis
Identify Smart
Variables
Building A Mining
Model
Output Target Group
12/30
Step1： Data cleansing
13/30
2. How to Achieve Accurate Sales
Data cleansing
14/30
Data cleansing
Target Data Set
Graph Data
V1
V2 V3 V2
V4
V3
V5 V4 V5 V5 V4
Graph Data Processing Storage
15/30
16/30
2. How to Achieve Accurate Sales
Step2： Personas
Personas
17/30
User Demographics
Text Data
Network Data
Cleaning and FilteringCrawling
Step1:Data Preprocessing
……
PersonasStep2：Features Extraction
Features One
Features Two
Features Three
Features ...
Personas
18/30
High-level Features
Low-level Features
……
Multi-source data
Personas A
Personas B
Personas C
Personas D
Personas
Personas
User Initial Data
KeywordVector
Latent Dirichlet Allocation
DataProcessing
Data Cleansing
PMICharacteristic
Distribution
User Feature Database
19/30
Personas
User Feature
Database
Feature Sequence
Feature Sampling
Feature Selection
User FeatureGroup
Residual CHaracteristics
PMI
User Portrait
20/30
21/30
2. How to Achieve Accurate Sales
Step3： Recommendation model
22/30
Recommendation model
{W1,b1}
Input x
{W2,b2}
h1 h2
Hidden layers
Output y
23/30
...
...
...
...
...
...
...
...
...
...
...
...
...
Autoencoder 1 Autoencoder 2 Autoencoder 3
UnsupervisedPre-training
SupervisedFine-tuning
Recommendation model
24/30
Recommendation model
g DNN
X1,X2…XN(random)
Y1,Y2…YN
Personas
（w,b）
25/30
Recommendation model
Wage earner
SAE
Stratum
Outline
1. Background
2. How to Achieve Accurate Sales
3. Applications in Other Industries
4.Future outlook
26/30
3.Applications in Other Industries
Intelligent Diagnosis
Drug Selection
Equipment Purchase
Personnel Employed
Doctors Distribution
“Big Data + Artificial Intelligence”
Target UsersMedical Industry
27/30
3.Applications in Other Industries
Equity Market
Insurance Industry
VC Industry
Financial Industry “Big Data + Artificial Intelligence”
Target Users
28/30
Outline
1. Background
2. How to Achieve Accurate Sales
3. Applications in Other Industries
4.Future outlook
29/30
Data Mining
Database
Statistics
Machine Learning
How to solve？
Timeliness
Inefficient
Sample Deviation
…Application
Single
Useless Data
4.Future outlook
1. In the Personas construction, the method of Machine Learning is introduced to adjust data parameters. 2. Preprocessing the data to avoid the curse of dimensionality. 3. Integrated use of cross domain data to break data dependencies.
30/30
Thank You Q & A
![How to achieve [big] goals](https://img.dokumen.tips/doc/110x75/53ffff838d7f72ba408b458c/how-to-achieve-big-goals.jpg)
![How to Achieve Blogging Success [Infographic]](https://img.dokumen.tips/doc/110x75/554d2069b4c905ab268b48af/how-to-achieve-blogging-success-infographic.jpg)
