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Matrix Analysis:The Ultimate Diagnostic Tool

TIA 2016

Capital Ideas Conference & Exhibition

April 6 9, 2016

San Antonio, TX

Seminar Outline:The Evolution of your Business!

• Introduction

• Business Metrics

• Business Analytics

• Business Matrix

• “Big Data”

• How to attack the problem

Introduction

Sources:

• Marketing Metrics: The Definitive Guide to Measuring Marketing Performance, 2nd Edition (2012), Ferris, Bendle, Pfiefer, Reibstein, Prentice Hall (used $19.95)

• Measuring Marketing: 110+ Key Metrics Every Marketer Needs(2013), Davis, Wiley (new $68.25)

• SAS Institute (www.sas.com)

• Gartner Research

http://www.sas.com/

Theory

Practice

Tautology

The world has changed. There are far more choices, but there is less and less time to sort them out.

Amount of Data

Time

Choices

Inertia

Critical Insights on Strategic Planning

• By 2018, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis.

• By 2018, most stand-alone self-service data preparation offerings will either have expanded into end-to-end analytical platforms or been integrated as features of existing analytics platforms.

Business Metrics

Metrics:

The Opportunity

• 1.5M data savvy managers and 140,000 - 190,000 more deep analytical talent positions are needed for the US to take full advantage of the big data opportunity. (McKinsey, 2011)

• Retailers can gain a 60% potential increase in operating margins by understanding big data. (McKinsey, 2011)

• 90% of the data in the world today has been created in the last two years. (IBM 2014)

Metrics are continually broken down into more discrete units of analysisExamples: $ / % - Margin Average (Total Load #)

$ / % - Margin per service (TL / LTL / IM),type of equipment (Van/Reefer/Flat/OS/OD)

$ / % - Margin per broker

$ / % - Margin per lane

$ / % - Margin per customer

$ / % - Margin per customer per lane

Business Analytics

What is a Business Analytics?

•Business Analytics is the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques and the communication of these results to customers, business partners and colleague executives. It is the intersection of business and data science.

New York University – Stern School of Business

Set the variables for analysis

* Vp = Volume(plan) * Va = Volume(actual)

* Pp = Price(plan) * Pa = Price(actual)

* MDp = Market Demand(plan)

* MDa = Market Demand(actual)

* Cap =Variable Cost(plan) * Ca = Variable Cost(actual)

* MSp = Market Share(plan)

* MDa = Market Share(actual)

* Mp = Margin(plan) * Ma = Margin(actual)

Net Marketing Contribution

NMC(actual)-NMC(plan)

(Va X Ma) – MEa – (Vp X Mp) - MEp

(52.9 k X 8) – 90 k – (50 k X 10) – 80 k$333.2k - $420 k

Volume Variance

Mp (Va – Vp)10(52.9 k – 50 k)

=+$29,000

Marketing Exp.

Variance(MEa – MEp)

=-($90k - $80k)=-$10,000

Margin Variance

Va(Ma-Mp)52.9 k (8-10)

=-$105,800

Demand Variance

Mp X MSp(MDa – MDp)10 X 0.25(230 k – 200 k)

=+$75,000

Share Variance

Mp X MDa(MSa – MSp)10 X 230 K(0.23 – 0.25)

=-$46,000

Price Variance

Va(Pa – Pp)52.9 k(15-16)

Cost Variance

Va(Cp – Ca)52.9 k(6-7)

=-$52,900

Business Matrix

What is a Business Matrix?

• A Business Matrix is essentially a plot on a two-dimensional plane according to how well they meet customers' key requirements. You can do this by drawing two lines in the form of a cross.

The Matrix: The Most Common Forms

•Strategic Issues

•Organizational Issues

•Products & Services

•Customer

•Sales

•Pricing

Ansoff’s Opportunity Matrix

MarketPenetration

MarketDevelopment

Product Development

Diversification

Increase market share among

existing customers

Attract new customers to

existing products

Introduce new products

into new markets

Create new products for

present markets

Present Market

New Market

Present Product New Product

Product DevelopmentStarbucks develops powdered instant coffee called Via.

Market PenetrationStarbucks sells more coffee to customers who register their reloadable Starbucks cards.

Market DevelopmentStarbucks opens stores in Brazil and Chile

DiversificationStarbucks launches Hear Music and buys Ethos Water.

Ansoff’s Opportunity Matrix

Innovation Matrix

23

Boston Consulting Group (BCG) Portfolio Matrix

Dog

Question Mark

Star

Cash CowPortfolio

Matrix

BCG Portfolio MatrixMARKET SHARE DOMINANCE

HIGH LOWM

AR

KE

T G

RO

WT

H R

AT

E

LO

W

H

IGH High growth

Market leaders

Require cash

Low growth

High market share

High cash flow

Low growth

Low market

Poor cash flow

High growth

Low market share

Need cash

Poor profit margins

$

Portfolio Matrix Example

Chapter 2 Version 6e 25

Mobile

Computing

Device (STAR)

Laptop Computer

(PROBLEM CHILD)

Tablet (CASH COW) Personal

Computer

(DOG)

MA

RK

ET

GR

OW

TH

RA

TE

LO

W

HIG

H

MARKET SHARE DOMINANCE

HIGH LOW

© 2016 Cengage Learning. All Rights Reserved.

26

Portfolio Matrix Strategies

Divest

Harvest

Build

Hold

© 2016 Cengage Learning. All Rights Reserved. 27

Cautiously Invest Invest/Grow Invest/Grow

Harvest/Divest Cautiously Invest Invest/Grow

Harvest/Divest Harvest/Divestt Cautiously Invest

Low

Me

diu

mH

ig

h

Low Medium HighMar

ket

attr

acti

ven

ess

Business Position

General Electric Model

“Big Data”

What is a Big Data?

•Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Big Data is NOT that old!

Source: SAS Institute

SAS Institute’s Outline of Big Data

• Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data.

• Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.

• Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions. (Doug Laney, 2000)


How do I get over the fact that I can’t “trust the data” and how to “do the math?”

“How do I attack the problem?”

Step One:Organize your Business

Analytics Team

Business Analytics Team

• CEO

• COO

• CIO

• CFO

• Sales Manager

• HR

• Outside Members (Design, Statistics, Software, etc.)

Step Two:Categorize and Define

your Questions?

Business activities

Focus for success

Short term Medium term Long term

Objectives Short-term profit Medium-term profit Innovation

Management focus Productivity Beat competition New product/markets

Target market Existing customers Competitor's customers New customers

Energy directed at Your Team Competition The unknown future

Differential advantage Cost control Segmentation (Niche) (Niche) Differentiation

Key component of mix Price Promotion/place Product (service) offering

Organizational culture Financial Marketing (Sales) Entrepreneurial

Business activities

Focus for success

Short term Medium term Long term

Objectives Short-term profit Medium-term profit Innovation

Management focus Productivity Beat competition New product/markets

Target market Existing customers Competitor's customers New customers

Energy directed at Your team Competition The unknown future

Differential advantage Cost control Segmentation (Niche) Differentiation

Key component of mix Price Promotion/place Product (Services)

Organizational culture Financial Marketing Entrepreneurial

Examples: Business Intelligence Questions

• Who are the top fifteen customers shipping out of Texas?

• How much has lane volume increased from Texas to California by quarter for each of the last five years?

• What are the top twenty lanes by volume (#), percentage (%), and dollars ($) out of Texas? New or organic growth?

• How many salespeople do we need to hire next year to achieve a 27% growth rate? And what would it cost?

• Based on sales turnover (HR) and sales ready metrics (margin $, loads, time) how quickly does a sales rep become profitable?

Examples: Business Intelligence Questions

• How much does a single percentage point decrease in “one and done” carrier percentage increase our net?

• How much does a single percentage point increase in carrier reloads with carriers doing more than 20 loads a year with us increase our net?

• What three product commodities have show the latest growth in the last quarter? Half year? Year? Three years?

• Which accounts have the highest/lowest transaction costs?

• What commodities are showing greater growth than three years ago?

Step Three:Build a set of sample

Matrices

Product (Service) x Market Analysis

Case Study: A simple starting point

Step Four:Determine if your Data is

Structured or Unstructured

Structure of Data

•Structured: Is the data collected daily, weekly, monthly in a computerized data base?

•Unstructured: Email, video, social media.

•Unstructured: Is the data available, in a computer data base, but not in the format we need?

•Unstructured: Is the data available, but NOT in a computer data base?

•Unstructured: Is the data not available currently, but could be collected?

Step Five:Determine which software

you are going to use to analyze the data?

Software Options• Excel

• Minitab (www.minitab.com)

• SAS (www.sas.com) SAS Institute

• SPSS (www.ibm.com)

• Business Intelligence Tools• SAS• Birst• Qlik View• Cognos - IBM

http://www.sas.com/

www.sisense.com/whitepapers/qlikview-bi-evaluation

4 STEPS TO SUCCESSFULLY EVALUATING BI SOFTWARE

• Inside the Whitepaper:

1. The differences between business intelligence software stacks

2. Project-specific vs solution-oriented approaches

3. How to choose an alternative that fits your business model

4. Tips for an effective proof of concept (POC)

Gartner White Papers (www.gartner.com)

Critical Capabilities for Business Intelligence and Analytics Platforms (10 March 2016 | ID:G00278437)

Magic Quadrant for Business Intelligence and Analytics Platforms (04 February 2016 | ID:G00275847

Source: Gartner Research

Business Intelligence

• Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions

Summary of Business Benefits:

• Increased revenue

• Better, faster decisions

• Business benefits — customer satisfaction

• Reduce IT head count

• Reduce line-of-business head count

• Reduce external IT costs

Summary of Business Benefits:

• Reduce other non-IT costs

• Expand types of analysis

• Make better insights available to more people

• Link KPIs to corporate objectives

• Monetize data

Matrix Analysis

RESOURCES &

OBJECTIVES

MATRIX

ANALYTICS

LONG RUN

PROFITABILITY

AND GROWTH

Critical Insights on Strategic Planning

• “By 2018, data discovery and data management evolution will drive most organizations to augment centralized analytic architectures with decentralized approaches.

• Through 2017, less than 40% of self-service business intelligence initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business.

• By 2020, 80% of all enterprise reporting will be based on modern business intelligence and analytics platforms. The remaining 20% will still be on IT-centric, reporting-based platforms because the risk to change outweighs value.”

Source: Gartner Research Report

Opportunity or Problem?

Or will you use your data and turn it into something else?

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