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MACHINE LEARNING

as a tool to improve the effectiveness of marketing campaigns in the banking sector.
Project duration
5 months
Team
  • 1 product manager
  • 1 designer
  • 1 data analytic
Сlient
Lithuanian Bank

TRENDS FOR THE USE OF DATA

SCIENCE TOOLS IN THE BANKING SECTOR

Marketing
  • Personalized marketing
  • Real-time marketing
  • Customer segmentation
  • Building social graphs, etc.
Sales
  • Predictive analytics
  • LTV forecast
  • Forecast churn, etc.
Security Service
  • Identification of clients making fraudulent transactions
  • Detecting hidden connections and "dark" schemes, etc.

Customer Service

  • Chatbots to respond to appeals, etc.
Risks
  • Short credit questionnaire
  • Credit scoring automation, etc.

THE EXPECTED EFFECT OF USING

MACHINE LEARNING ALGORITHMS


Improving the effectiveness of marketing campaigns
MACHINE LEARNING:
Classification
+
Clustering
1. Growth of conversions from mailing lists
2. Reducing ineffective contacts
3. A deeper understanding of customers

THE STAGES OF SOLVING THE TASK

OF IMPROVEMENT IN THE EFFICIENCY

OF SENDING TERMS WITH

MACHINE LEARNING ALGORITHMS

THE GENERAL DIAGRAM

CHARACTERISTIC OF THE STAGES.

DEVELOPMENT OF THE PREDICTIVE MODEL

The task
  • Using machine learning algorithms determine for all bank customers the probability of applying for a product after receiving an advertising message.
  • Form a mailing list of clients with a high probability of applying for the product.

STAGES OF SOLVING THE CLASSIFICATION PROBLEMS

Data collection, data loading into the analytical tool

1
Data preparation for analysis
2
Converting data to numeric format
3
New attributes formation
4
Trait selection
5
Data normalization
6
Development of models classification
7
Quality assessment of models
8
Result output
9

DESCRIPTION OF THE STAGES.

DEVELOPMENT OF THE CLUSTERING MODEL

The task
  • Identify homogeneous clusters using machine learning algorithms
  • Interpret the results - describe the portraits of the selected clusters
Clients used to build the model
  • Clients were used for the cluster analysis,
  • probability of response for which >=0,7
Number of dependent variables
  • 24 - segmentation was conducted based on data on gender, age, place of residence, as well as on data on spending for three months by spending category
Number of clusters and clustering algorithm
  • The mailing list was divided into 5 clusters by the algorithms
  • K-means method was used for clustering

THE ACTUAL EFFECT OF USING

MACHINE LEARNING ALGORITHMS

Would you like to improve the effectiveness of your marketing campaigns?

Accept
E-Mail: team@castlemarketing.lt
Phone: +370 6 835 68 31