Customer Segmentation Model

This Bot enables segmentation of target with similar traits and assigns them into clusters. Output can be used in a workflow to automate your business processes

Top Benefits

  • Increase Productivity: Save ~30% of an analysts time to prepare Customer Segmentation for business processes.
  • Target Right Segment: Get relative viable clusters for targeting right customers i.e. behavioral segmentation.
  • Zero Maintenance: Model is provided as SaaS using serverless function architecture of Azure.
  • Ease of use: Model is easy to setup, highly scalable, fault tolerant (Each request is independent of another request).
  • Runs in Cloud: Model runs in cloud and provides output to your workflow and automate your business processes.

Tasks

  • Prepare rapid unsupervised customer clusters/segments and personas.
  • Input CSV file is provided to the model and processed MS Excel output is retrieved.
  • Help in understanding behavioral customer segments to a marketeer to make data driven business decisions.

Inputs:
1. Path: Path of the CSV file. Ex.: C:~Car.csv
2. ColumnCount: Count of columns available in CSV file being provided as input data.
3. InputColumnNames: Name of columns being provided in CSV file. For example: 'Choice','Hsg2','Coml5'......
4. InputColumnDataType: Data Type of columns being provided in CSV file in the same order as column names. For example: 'String','Numeric','Numeric'....
5. MissingNumberValueReplacement: A value that can be used by Model in case CSV file has blank field/s or missing numerical value/s.
6. MissingStringValueReplacement: A value that can be used by Model in case CSV file has blank field/s or missing string/character value/s.
7. ClusterCount: Count of clusters to be made by the user. For example: 3/4/5 to any user-defined number
8. IgnoreColumns: Column name that model shall ignore while calculating customer segmentation. For example 'SNo','Range6' to any user-defined input

Actions:
The Bot takes in the CSV file and performs the customer segmentation and outputs in the MS Excel.

Outputs: Customer segments,
1. Output: the result in the form of the MS Excel.
2. Viable customer segments/personas (5 clusters in the given example) that are cluster output and segmental counts (for unique cluster identification).

Use Cases:
1. Identify the most profitable customers and target them in your marketing campaigns to increase sales per customer.
2. Segment your customers by purchase history and their interest to target the right product/ service.
3. Segment patients based on their medical history and demographic to recruit the right patients for the clinical trial.

Get Bot

Free

Bot Security Program
Level 2
Applications
Business Process
Category
Vendor
Automation Type
Bot
Last Updated
April 15, 2020
First Published
April 9, 2020
Enterprise Version
11.3
Community Version
11.3.1
ReadMe
ReadMe
Support

See the Bot in Action

Code
Input Sample
Output Sample
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Code
Input Sample
Output Sample

Setup Process

Install

Download the Bot and follow the instructions to install it in your AAE Control Room.

Configure

Open the Bot to configure your username and other settings the Bot will need (see the Installation Guide or ReadMe for details.)

Run

That's it - now the Bot is ready to get going!

Requirements and Inputs

  • 1. Path: Path of the CSV file.
  • 2. ColumnCount: Count of columns available in CSV file being provided with input data.
  • 3. InputColumnNames: Name of columns being provided in CSV file.
  • 4. InputColumnDataType: Data Type of columns being provided in CSV file in the same order as column names.
  • 5. MissingNumberValueReplacement: A value that can be used by Model in case CSV file has blank field/s.
  • 6. MissingStringValueReplacement: A value that can be used by Model in case CSV file has blank field/s.
  • 7. ClusterCount: Count of clusters to be made by the user.
  • 8. IgnoreColumns: Names of the column that model shall ignore while calculating customer segmentation.