Bi mktg presentation - Using Business Intelligence for Marketing and Sales Analysis

1Proprietary and Confidential © ERP India – Vispi Munshi – erp-india.org

Using Business Intelligence for Marketing and Sales Analysis

Vispi MunshiFounder - ERP [email protected]://www.erp-india.com



Today’s Agenda

- Introduction

- Marketing and Sales Functions

- What is Business Intelligence

- Data Analytics, Dashboards

- Data Mining, Predictive Analytics

- Examples of using BI in Marketing and Sales

- Blueprint of a Marketing Intelligence System

- Measuring Marketing and Sales performance, Scorecard

- Analytics Demo

- Conclusion, Q&A



Ground Rules

- Please turn mobile ringer to SILENT MODE

- If you have to take a call, please leave the room and then start talking

- Questions can be asked any time

- Before asking question, raise your hand

- No talking with each other

- Disagreements allowed, but no disrespect

- Timeframe awareness

- Lets learn from each other and increase our knowledge



Introductions

- Full Name

- Where do you work (Company/Consultant/Student etc)

- Your job designation and responsibilities

- Any BI tool you have already used?

- Expectation from this workshop



What is Customer Relationship Management

CRM is about identifying and acquiring new customers, and maintaining and enhancing relations with valued customers.

Customer

DemandCreation

LeadGeneration

Lead Mgnt

OrderMgnt

Quoting

Contracts

ServiceMgmt

Opportunity Mgnt



Key Marketing and Sales functions

- Demand Creation (Marketing Campaigns, Promotions)

- Identify and Acquire customers (Sales Lead management)

- Proposal/Quotation and Order processing

- Order and Contract Management

- Sales Channel Management

- Product and Territory Management

- Pricing

- Loyalty Programs

- Surveys (for feedback)



Identifying and Acquiring Customers

Call Center

Field Staff

Your Web Site

Partners

External Sources

Suspects

Prospects

Leads

SALE

Referral

Repeat



What is business intelligence?

Business Intelligence is a term generally used to identify a class of Information System applications useful for supporting operational, tactical and strategic decision making of a organization.

BI deals with producing (and presenting) Accurate, Relevant and Timely (ART) INFORMATION from integrated data.

Not Accurate: Users loose faith in system

Not Relevant: Users ignore the system

Not Timely: Users find alternatives to the system



Benefits- Sales Optimization

- Cost Reduction, Planning and Budgeting

- Inventory Optimization

- Purchase Optimization

- HR Optimization

- Production / Manufacturing Optimization

- Demand Forecasting

- Market Competitive Analysis and Customer Relationship Optimization

- Supply Chain (Sales and Distribution) Optimization

- Most Important is ART Information to support all levels of Decision Making

- Aim is to replace/reduce Excel based Analysis



BI System Anatomy

BACK-ENDData Collection and

Integration Tools

ERP

Ecomm

CRM

Files

….

Data Mart

Forecasting, Mining

PRESENTATION

FRONT-END

Analytics, Visualization, Dashboards, …

DISTRIBUTION

Alerts, Email, Mobile, Intranet ….



Backend BI Tools: Used for collection, integration and consolidation of data from various sources.

Intermediate BI Tools: Allows users to create forecasts and identify trends (data mining) from the data.

Frontend BI Tools: Provides users with a interactive and intuitive interface to view the information generated.



- Based on multi-dimensional view of data (cubes)

- Pivot Table demo

- Features for drilling in and across data

- Slicing and dicing of data

- Graphics capabilities

- Similar to Excel

- Exception reporting

- Data Analyst would use such tools and create Reports/Analysis/Graphs (objects)

- These objects would be supplied to Executives using Dashboards, Alerts etc.

Data Analytics (New Age Decision Support Systems)



Multi-dimensional Data



High-end: Business Objects, OBIEE, Microstrategy, …

Open Source: http://en.wikipedia.org/wiki/Business_intelligence_tools

Data Analytics Tools in Market



Dashboards and Alerts

- Dashboards are collection of reports/analysis/graphs

- Alerts are specific events that the system finds and sends a email/sms etc to the executive

- Key Performance Indicators (KPI’s) can be set and linked to alerts

- Linking across objects provides executive with ability to go through the generated information in a very intuitive manner



Data Mining

- Also known as KDD (Knowledge Discovery in Databases)

- Involves use of large data sets

- Involves uses of Statistical Methods, Database Systems and Artificial Intelligence

- The objective is to discover patterns or knowledge from existing data

- Involves four step process: Data Preparation (classification), Hypothesis (user provided guidance), Discovery (of knowledge) and Validation (of discovered knowledge against hypothesis)

- Techniques: Classification, Clustering, Association Rules, Regression

- Predictive Analytics

- High end tools: SPSS, SAS, …

- Open Source: http://en.wikipedia.org/wiki/Data_mining



Data Visualization- Presents a intuitive graphic representation of the data and generated

knowledge

- The visualization is a embedded feature in most Analytics and Mining tools



Predictive Analytics - Combines Data Analytics and Statistical techniques to provide a interactive

tool

- encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events

- Recency, Frequency, Transaction Value, Demographics

- Classification, Clustering, Association Rules, Regression

- http://en.wikipedia.org/wiki/Predictive_analytics



Marketing and Sales Metrics

- Cost per lead generation

- Proposal turnover time

- Lead closure rate

- Lead closure time

- Sales Channel utilization

- Customer Retention/Churn rate

- Campaign Effectiveness



Using BI for Marketing and Sales Examples

- Example 1: Predicting which Customers are likely to switch to competitors

- Example 2: Predicting which Customers can be cross sold

- Example 3: Predicting lead success probability & providing recommendations

- Example 4: Focusing Marketing Campaigns



Predicting which Customers are likely to switch to competitors

Past Customer Records- Transactions- Demographics- Buy Behavior

Past Switch Records

Create Model (Association Rules, Clustering

Switch Model (Decision Tree, Neural Network)

Current Customer Records- Transactions- Demographics- Buy Behavior- Service Records

Prediction Algorithm (Classification)

Switch Predictions

Analytics

Service Records



Predicting which Customers can be cross sold

Past Customer Records- Transactions- Demographics- Buy Behavior

Past Cross Selling Records

Create Model (Association Rules, Clustering

Cross Sell Model (Decision Tree, Neural Network)

Current Customer Records- Transactions- Demographics- Buy Behavior- Service Records


Cross SellPredictions

Analytics

Service Records

Product Cross Sell Possibilities



Predicting lead success probability & providing recommendations

Past Leads-Attributes- Campaign- Sales Channel

Conversion Results- Success or Failure- Reasons- Process followed

Create Model ( Clustering)

Conversion Model (Decision Tree, Neural Network)

Current Leads


Conversion ProbabilityPredictions& Recommendations

Analytics



Focusing Marketing Campaigns

Past Prospects/Leads Conversions and Failures

Demographic Data

Create Model (Association)

Model (Decision Tree)

Shortlisting

Prospect List Shortlisted

Prospects

High

Medium

Low



More Ways to use BI

- Customer Profitability Analysis: which customers to value?

- Ecommerce Site User behavior analysis: Make special offers to convert suspects to prospects and leads, depending on which pages of the site the suspect has visited

- Call Center Log Analysis: identifying leads using data mining or predictive analysis

- Customer Retention: identify potential leads, renewal offers

- Customer Relation Extension: identify potential leads based on service records



Marketing and Sales Intelligence System

BACK-ENDData Collection and

Integration Tools

ERP, CRM

Sales

Industry data

….

Marketing Data Mart

Forecasting, Mining

PRESENTATION

FRONT-END

Analytics, Visualization, Dashboards, …

EXTRANET ACCESSTo

PARTNERS (Marketing Portal)

ALERTS by email and mobile to Sales Force and Partners



Measuring Performance: For each Campaign or Sales Channel

New Customers: 5678, 7%

Acquisition Cost: 2500/-

Lead Closing %: 34%

Renewal %: 77%

Extended Relations: 345, 1.3M

Recovered Relations: 45

http://www.google.co.in/url?url=http://www.targetdashboard.com/blog/47/Why-Experts-say-Don-t-Use-Gauges-on-KPI-Dashboards.aspx&rct=j&frm=1&q=&esrc=s&sa=U&ei=ZeGuU5P4K8GWuATU4ICgCQ&ved=0CBUQ9QEwAA&sig2=uWlAjS3VtbD5hjxZrUjrGg&usg=AFQjCNGp1FeAHE1QOmrev55gnLxvNxe07Q



Analytics Demo



Q&AThank You

[email protected]

98250 11489

Bi mktg presentation - Using Business Intelligence for Marketing and Sales Analysis

Data & Analytics

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