Use Cases

Data driven strategic decision making by developing a better understanding of customers and their choices

Objective

Identify business problems that can be solved through analytics depending on the availability of data.
Optimize the strategic decision making by developing a better understanding of customers and their choices.

Outcome

Problem space that needs to be addressed using data driven analytics in the niche domain has been identified.
Customer segmentation, churn prediction, propensity modelling and recommender engines etc. have helped in data driven decision making.

What are the most common problems faced by e-commerce companies?

Problems faced
Solutions proposed

How to identify the customers segments to be targeted through various marketing campaigns?

How to identify the likelihood of a customer to buy online?

When can a customer churn and when should the efforts to retain them start?

Which products should be suggested to existing customers?

How to measure loyalty of the customers?

How should pricing strategy & promotions be decided?

How should inventory be managed & demand forecasted?

How should the process be tracked & improved?

How should the best vendor out of all be selected?

Customer segmentation

1) Demographics
2) Buying history
3) Visiting history

Propensity modelling

1) Competitors
2) Browsing behaviour
3) User experience

Churn modelling

1) Product attributes
2) Service
3) Competitors

Recommendation engine

1) Purchase history
2) Viewing history
3) Demographics

NPS survey analysis

1) Text mining customer feedback
2) Identifying pain points
3) Linking satisfaction scores to pain points

Pricing strategy

1) Competitor pricing
2) Dynamic Pricing
3) In-house brands

Inventory management

1) Demand capture process improvement
2) Forecasting process improvement

Process tracking and improvement

1) Tracking the supply chain
2) Identifying the bottlenecks in the process and its causes
3) Proposed solutions for the same

Vendor development

1) Tracking all metrics to evaluate the vendors
2) Selecting the best possible option
3) Dashboard that maintains the record month over month

Segmentation of customers using their demographic and buying / visit behavior

For each month, the average growth for both the groups is compared against each other for all the 5 segments.

Demographics
Visits on websites and apps
Buying on websites and apps
Visiting and buying from stores

Customer	Age	Location	Product	Views
A	25	North	Mobile	4
B	33	West	Book	6

K-Means Clustering:

It is an iterative unsupervised machine learning algorithm to cluster the data.

Age: 21 – 25 yrs

North: India
Gender: Male
Product: Mobile

Age: 30 – 35 yrs

West: India
Gender: Female
Product: Book

Customer churn prediction

How to identify customers who are likely to churn due to inactivity and when should they be sent promotional offers to retain them?

Preliminary factors that affect churn of the customers

Product Attributes

Unavailability of products on the website

Variety of products available

Quality of products

Competitors pricing

Promotion of products

Service Attributes

Complaint resolution time

Loyalty programs

Post purchase service

Delivery delays

Staff behaviour at stores

Some of the Hypotheses are...

Instances of unavailable products
Variety of products that are better priced
Promotions for the products on the website
Resolution time for the complaints registered at the call centre
Delays in delivery of the products
No of hours of soft skills training to the staff at the store

Approach to predict when the existing customers can churn

Customer omni-channel behaviour

How to identify the customers who are likely to view products on the website and then make purchases in stores?

Preliminary factors affecting propensity to buy online

Company Attributes

Unavailability of product reviews on website

Activity on social media

Competitors pricing

Promotions of the products

Service at the showroom

Customer Attributes

Demographics

Visits on website

Product views

Visits to store

Purchase history

Website attributes

Shipping charges

Return policies

Product description

User experience on website

Options for fast-track delivery

Some of the Hypotheses are…

Number of reviews for the products
Activity on social media for the company
Number of customers in the same area
The user experience for any website visitor
The familiarity of the brand ambassador
No of visits on the website for a product
The store visits for the website visitor

Predict the Omni-channel behavior of customer – viewing products online yet buying in stores

Personalized marketing

What are the products that should be recommended to the customers based on their past behaviour?

Preliminary factors that affect personalized marketing.

Company Attributes

Products that are viewed together

Products bought together

Categories of products

Range of prices

Promotions of the products

Service at the showroom

Customer Attributes

Demographics

Visits on website

Product views

Device used

Purchase history

Session Behavior

Referring Website

Website attributes

Views of product

Visitors’ frequency

Recency of visits

Some of the Hypotheses are…

Customers are recommended complementary products while buying the primary product
Highly viewed product by a particular segment is recommended to the customer
Higher the recency of the visit to product page by the customer
A particular category of Products has been viewed earlier by a visitor
Products having highest number of views are shown as suggestions
Promotional festive offers are displayed

…then customers:

end up buying the complementary products
end up viewing that product
will view the same product while navigating other products
will view similar products in the same price range if suggested
will view that product
will buy the promoted products

Personalized marketing to the customers using a recommender engine

Data Preparation

The data preparation phase covers all activities needed to construct the final dataset [data that will be fed into the modeling tool(s)] from the initial raw data. Data preparation tasks are likely to be performed multiple times and not in any prescribed order. Tasks include table, record, and attribute selection, as well as transformation and cleaning of data for modeling tools.

Select Data
Clean Data
Construct Data
Integrate Data
Format Data
Data Set Description

Sources Of Relevant Data And Gaining Access

Obtain Data Set For Modeling And Establish Dependency

Decide on the data to be used for analysis. Criteria include relevance to the data mining goals, quality, and technical constraints such as limits on data volume or data types.
Raise the data quality to the level required by the selected analysis techniques. This may involve selection of clean subsets of the data, the insertion of suitable defaults, or more ambitious techniques such as the estimation of missing data by modeling.
Constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes.
These are methods whereby information is combined from multiple tables or records to create new records or values.
Formatting transformations refer to primarily syntactic modifications made to the data that do not change its meaning, but might be required by the modeling tool.
Create the final Data Set required for modeling and give business definition for proper consumption.

Data Integration and Develop Understanding

Exploratory Data Analysis

The data understanding phase starts with initial data collection and proceeds with activities that enable you to become familiar with the data, identify data quality problems, discover first insights into the data, and/or detect interesting subsets to form hypotheses regarding hidden information.

Select Modeling Techniques
Generate Test Design
Build Model
Assess Model

Sources Of Relevant Data And Gaining Access

Conduct Exploratory Data Analysis, including

Univariate non-graphical: to help identify any outliers and better understand the distribution of the sample by primarily utilizing descriptive statistics

Frequency reports
Measures of spread
Central tendency measures

Univariate graphical: visualization of the descriptive statistics from the non-graphical techniques, including but not limited to:

Histograms
Box-plots
Quantile normal plot (looks at the observed and expected values)
Stem and leaf plots

Multivariate non-graphical: to understand the relationship between two or more of the variables contained in the databases through statistical techniques, including but not limited to:

Cross-tabs
Correlation
Covariance
ANOVA

Multivariate graphical:

Scatterplots
Side-by-side box-plots

Data Modeling

Select Modeling Techniques
Generate Test Design
Build Model
Assess Model

Select And Fine Tune The Model For Optimum Results

Segmentation Modeling

Multinomial logistic regression
Segmentation techniques
Traditional cluster analysis
Regression guided segmentation
Classification guided segmentation
Bayesian hierarchical models

Persistency/Churn Modeling:

Proportional hazards model
Kaplan-Meier
Other Survival modeling techniques

Model Evaluation and Maturity Iteration

At this stage in the project, you have built a model (or models) that appears to have high quality from a data analysis perspective. Before proceeding to final deployment of the model, it is important to thoroughly evaluate it and review the steps executed to create it, to be certain the model properly achieves the business objectives.

Evaluate Results
Review Process
Determine Next Steps

Measure And Improve Performance

Variable 1

Modal Performance%

Variable 1

Variable 2

Modal Performance%

Variable 1

Variable 2

Variable n

Modal Performance%

Deployment and Continuous Improvement

Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that the customer can use it. It will involve applying “live” models within an organization’s decision making processes and improve by applying machine learning techniques

Deployment
Track and Monitor
Machine Learning (On-Going)

Put Insights Into Practice

Deployment

Establish the drivers for the customer persistency
Identify the strategy for using the score during the life cycle of the customer
Integrate the score in applicable processes
Learn from persistent customers and applying techniques to reduce churn

Track, Monitor and Learn

Monitor the prediction to the actual results for applicability
Find out discrepancy where exist and apply learning to model for improvement of prediction

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Ecommerce

Use Cases

Data driven strategic decision making by developing a better understanding of customers and their choices

Objective

Outcome

What are the most common problems faced by e-commerce companies?

Segmentation of customers using their demographic and buying / visit behavior

K-Means Clustering:

Customer churn prediction

Preliminary factors that affect churn of the customers

Product Attributes

Service Attributes

Some of the Hypotheses are...

Approach to predict when the existing customers can churn

Customer omni-channel behaviour

Preliminary factors affecting propensity to buy online

Some of the Hypotheses are…

Predict the Omni-channel behavior of customer – viewing products online yet buying in stores

Personalized marketing

Preliminary factors that affect personalized marketing.

Company Attributes

Customer Attributes

Website attributes

Some of the Hypotheses are…

…then customers:

Personalized marketing to the customers using a recommender engine

Data Preparation

Sources Of Relevant Data And Gaining Access

Data Integration and Develop Understanding

Exploratory Data Analysis

Sources Of Relevant Data And Gaining Access

Data Modeling

Select And Fine Tune The Model For Optimum Results

Model Evaluation and Maturity Iteration

Measure And Improve Performance

Deployment and Continuous Improvement

Put Insights Into Practice