5/28/2015

Data Intelligence and

Predictive Analytics

From ADG Online Solutions

A Case Study for a Globally functional Mega

store showing efficient decision making

through Data Intelligence & Predictive

Analytics

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Objective To analyze a mix of structured, semi-structured and

unstructured data in search of valuable business information

and insights.

Scenario: Mega Store is a globally operated enterprise that

manufactures and supplies its products through multiple channels

and suppliers.

In order to operate efficiently and process strategically, the senior

management required a solutions that can fill in the missing factors

crucial to better decision making.

The Mega Store’s management was facing problems in getting the

actual picture of ongoing activities across various countries, states,

cities and regions.

Also, information on customer segment distribution across various

categories was an essential requisite.

Company Operating Environment:1. 100+ countries 2. Three Departments (Each with

categories)a. Furnitureb. Office Suppliesc. Technologies

3. Four Customer Segmentsa. Home Officeb. Corporatec. Consumerd. Small Business

4. Three Shipping Modes5. 12+ Global Enterprise Suppliers

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Challenge/Data Intelligence

Existing Data Analytics of

Management was lacking :

1. The ability to view existing

information from different

dimension .

2. “At a Glance”

understanding of data

3. Centralization and

Standardization of data

4. Mobile Friendliness

5. Ad-hoc Ability

Key problematic areas where Senior Management

was loosing cohesion

Because they were not getting precise answers to the following questions:

-> What are my top profit making categories and departments in my business?

-> What factors are contributing to the bottom line?

->How to get insights from my business stats effectively?

-> How to get big picture of my business ?

-> How to combat Information Overload?

-> How to expand my purview of Data Analysis?

-> Ho do I keep eye on key metrics along with sub key metrics of my Business?

How to Achieve Intuitive Data Analytics?

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Solution/Data IntelligenceIdentifying TopPerformer categories and departments

With the combination of Geography Analytics and Data Analytics , thesenior management was able to identify Performers and Laggards in their value chain,

Exploring Big Picture

Also , Geo Analytics made them accessible to region specific information.

Fig. 1

Answering Questions:

->Who are my top profit making categories and departments in my business

->How to get Big picture of my business ?

At Country Level

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Getting Insights from Business StatsWith the combination of Data Analytics and Geo Analytics users were able deep dive into data with most Granular view( i.e. State or city level profitability split in Fig. 2)

Expanding Purview of Data AnalysisDeep diving into key metrics made easy to expand the purview of Data Analysis.

Solution/Data Intelligence

Fig. 2

Answering Questions:

How to get insights from my business stats effectively?

How to expand my purview of Data Analysis? At State Level

Now, the management was able to analyze progress of each subcategory for a particular state of a country in a

specific year along with customer segment contributions.All this without any technical expertise

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Top Performers /Laggards in ….

Now users were facilitated to know about Top performers and Laggards for each category ad in fig.3. • User is availing information on different customer segment profitability corresponding particular category e.g. Furniture

Keeping Eye on Key Sub Metrics• Now a Regional Manager was able to look into his sub metrics (Product Base Margin and Unit price ) for his particular country/state in any year .

• He is also able to know profitability from each customer segment from any category and subcategory too.

Solution/Data Intelligence

Fig. 3

Overview of Top performers along with sub key-metrics such as Product Base

Margin and Unit Price at any Year

Answering Questions:

Who are my top Performing categories in my business/category/subcategory?

How to get the Big picture of my business ?

Ho do I keep an eye on the key metrics along with sub key metrics of my business at any

time ?

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Bottom line ContributionCombination of Key Metrics and Sub key Metrics helps a price decision maker to closely understand how each item was floating around Unit Price and Product base margin .

Expanding purview of InformationA Category head was able to look over sub metrics of Category(Furniture)/ Subcategory /Items .

Controlling Information OverloadThe management can control data analysis information without having much expertise on Querying.

Solution/Data Intelligence

Fig. 4

Each bar chart visualization is equipped with a (+) button which expands purview of reporting

Answering Questions:

What factors are contributing to the bottom line?How to expand my purview of Data Analysis?How to combat Information Overload?

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Existing management was not equipped enough to predict the feasibility of existing or proposed business plans /suggestions by sales team

This proved to be an area of major concern and presented a dire need for Predictive Analytics.

Challenge/Predictive Analytics

How to bring power of Prediction to the existing Business Model?

Key areas where Senior Management was facing obstacle

Following questions were going to be answered after predictive analytics

implementation?

-> To what extent my business plans will succeed ?

How my past experience can help me succeed in future?

Which Geographies to be Targeted on?

Identifying Stakeholders/Performers in Business

How I can project the direction of my Business Units are heading towards?

Which SBU’s are going to offer me the maximum ROI?

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These model logics are the basis of Business Intelligence Tools that actually predict feasibility of Business plans and Proposed Business ambitions

Solution/Predictive Analytics

Utilization of Past Data to Create Efficient Business Models

Fig. 5 Big Data Predictive Model( Tree Map Visualization)

With the help of Machine Learning

practices, the team utilized past

historical data of transactions by

the Mega Store (comprising Unit

Price, Avg. Base Margin, Category,

Container etc.)

This resulted in a set of logic or

formula which were utilized for

Predictive Analytics Practices.

In fig. 5 Predictive Model’s Tree

map representation depicts path

of the logic

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How we predicted this??

Answer = Predictive Analytics Practices

In simple, management did the following :

(Historical sales, Transaction data )+ (Data model (Fig.5))= Predictions

Solution/Predictive Analytics

Utilization of Past data to create Efficient Business Models

Home Office Corporate Consumer Small Business

$10 K

$20 K

$30 K

Scenario : A Stakeholder from company and propose a plan and says “ Hey! Let’s bet on my Sales plan” which comes with modified quotation for all categories with all different discount % ,unit price , Avg. base margin % for each product for a specific region in country.

In this situation now, the management with Predictive analytics solutions was able to answer specifically that the proposed plan will help in ROI only in categories Home office, Corporate, Consumer but will be loss making in Small Business category.

Profit making proposal quote

Loss making proposal quote

5/28/2015

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5/28/2015

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