profiting from customer profitability + big data fitzgerald analytics
DESCRIPTION
Measuring and managing customer profitability in the big-data era. How to capitalize on the opportunity. In today's era of Big Data and related technology, the benefits of "customer-centricity" are within our reach. Analysis of Big Data sources helps to better understand customer needs, preferences, attitudes, expectations, sentiments, and buying behavior. Yet to achieve this potential, organizations need to understand and apply the classic but essential concepts of customer profitability, customer lifetime value (CLV), and customer value management analytics. Join us for an event on how to approach this challenge. When linked with customer profitability metrics, these insights enable more profitable decisions in product design, sales, marketing, customer care, loyalty management, and risk management. This session will help attendees capitalize on this opportunity. We will cover the classic high-impact basics of measuring and managing customer profitability, customer lifetime value (CLV), as well as how to use new Big Data insights to get more value from these efforts. This tutorial which cover the topic in 5 practical steps: 1. Introduction to Customer Profitability Analytics: What is customer profitability analysis, why is it so valuable, and what are the key concepts and methodologies used to measure customer profitability, customer lifetime value (CLV), and related metrics? 2. High-Impact Use-Cases of Customer Profitability Analytics: What are the key ways customer profitability analytics is used enhance results? We will describe the highest-value ways to use customer profitability metrics to improve business results, with concrete examples in each of the following categories: o Customer Lifetime Value optimization ("CLV") o Customer loyalty and retention o Share of wallet maximization o Marketing ROI o Impact of Customer Service, Customer Experience, and Customer Satisfaction on Profit o Product design, pricing, promotion, and positioning o Allocation of resources (capital, budget, HR, etc) o Risk management 3. How to Calculate Profitability at the Customer Level : We will walk through the algorithms you need to use to turn raw data into customer profitability metrics, and share tips on how to customize them depending on your business. Related applications will also be covered, such as how to use the same algorithms to measure profit per household, salesperson, distributor, or other entity relevant to how your business makes money. 4. Data & Tech Requirements 5. Using Big Data to Maximize ROI on Customer Analytics: What are the top 5 opportunities to use Big Data to increase the benefits achieved through customer profitability analytics and related initiatives? Speakers: Jaime Fitzgerald, Founder and Managing Partner, Fitzgerald Analytics, and Konrad Kopczynscki, Director at Fitzgerald Analytics. Konrad and Jaime have applied customer profitability methodologies to dozens of clients.TRANSCRIPT
Architects of Fact-‐Based Decisions™
Profi%ng from Customer Analy%cs in the era of Big Data March 25th, 2014
2 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Introduc%on: Jaime and Konrad
17+ years advising clients in Financial Services, Retail, and Public Sector. Created the Data to Dollars Value Chain™ framework & methodology, used by to serve our clients at Fitzgerald Analytics. Now “open-sourcing” the methodology via: • The Book • Online learning resources • Training seminars on data-monetization • Customized training + consulting
Specialists in the process of turning Data into Results.
3 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
The Data to Dollars™ Stack
Insights
Analysis
Data
Tools, PlaCorms, Technology, People, and Processes
Decisions, Ac%ons, and Results
Made be'er by the right
Created by the right
Which depends on access to the right
And selec7on of the right
Plan:
Act:
4 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
The Stack is Also a Value Chain…
Insights
Analysis
Data
Tools, PlaCorms, Technology, People, and Processes
Decisions, Ac%ons, and Results Plan:
Act:
Dollars
To
Data
Made be'er by the right
Created by the right
Which depends on access to the right
And selec7on of the right
5 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
§ New Data Source Acquisi5on
§ Data Discovery
§ Data Quality
§ Data Governance
Analysis Insight
§ Decisions
§ Ac5ons
§ Financial Impact
§ New Data
§ New Opportuni5es
The Data to Dollars Value Chain™
3. Dollars
2. Analysis
1. Data
Naviga%on Tips: 1. Set Clear Goals
and translate into concrete plans
2. Stay Agile (loop back oQen)
3. Keep Oriented (“line of sight” / “why am I doing this?)
6 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Set Your Ul%mate Goal “Yes, that math works…”
“Yep, those are the two types sources of
gross profit”
“Yep…math works here too…”
Causal Models and Causal Clarity™ Causal Clarity™ is star@ng with our goal and then figuring out what we needs to be done in order to deliberately cause the goal to happen.
Source: CFNA / Bridgestone-‐Firestone Presenta@on
Service
Marke7ng
Compensa7on
Gross Profit
Store Expenses
Retail Store Profits
Sales
Gross Margin on Sales
Gross Margin on Sales
Sales
Tires
Overhead
Illustra%ve Example
7 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Table of Contents
1. Customer Profitability Analy%cs (CPA)
2. High Impact Use Cases
3. Calcula5ng CPA at the Customer Level
4. Data and Tech Requirements
5. Using Big Data to Maximize ROI on CPA
8 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Seeking the Origins of Profitability…
9 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Customer Rela%onships are the Source of Results
“There is only one valid defini5on of a business purpose: to create a customer”
-‐ Peter Drucker, The Prac@ce of Management, 1954
10 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Customer Profitability Defined (aka “CPA”)
Your P&L Statement
Deconstructed into a P&L for each of your customers
The contribu7on each customer makes to your total profit or loss. In other words, a “customer-‐level P&L statement”
11 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
History of Customer Profitability Analysis
§ Prac5ced since the early 1980s. Banks were early adopters
§ First Manha_an Consul5ng Group one of several firms to pioneer the method for clients
§ Massive results unlocked over the years and ongoing
§ Some notable mishaps along the way…
§ S5ll considered by many to be “obscure” or “not possible here” …which is unfortunate!
12 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Customer Profitability is The Ul%mate KPI
“There is only one valid defini5on of a business purpose: to create a customer”
(The Prac5ce of Management, ‘54)
13 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Loss per Customer
Example CPA Output: “Decile Chart”
Top (Most Profitable 10%)
2nd 3rd 4th 5th 6th 7th 8th 9th Bo_om (Least
Profitable 10%)
Profitability Deciles (each bar = 10% of customers, ranked by profitability)
Average
Best Customers Mid-‐Value
Losing Money
Profi
t per Customer
14 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
The “reality behind the averages” enables beaer decisions Loss per Customer
Top (Most Profitable 10%)
2nd 3rd 4th 5th 6th 7th 8th 9th Bo_om (Least
Profitable 10%)
Profitability Deciles (each bar = 10% of customers, ranked by profitability)
Average
Priori%ze for reten%on, target to acquire more….
Grow share of wallet Revisit costs to serve, pricing, and root causes
of unprofitability
Profi
t per Customer
15 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Example of an Individual P&Ls: Bank
P&L Item (Yearly) High Profit Customer Low Profit Customer Revenue Checking Account $300 $36 Savings Account $100 N/A Credit Card $600 $15 Mortgage $1,000 N/A
Cost Of Goods Sold (Interest Expense) $800 $5 Opera%onal Costs
Pro-‐Rated Customer Acquisi5on (Sales + Marke5ng Expense) $80 $40 Other Marke5ng $5 $5 Customer Service Offline / Online / Phone $5 / $2 / $5 $20 / $2 / $5
Statements Offline / Online $0 / $1 $30 / $1
Other Opera5ons $5 $5 Net Profit $1,097 ($62)
Large Varia7ons
Illustra%ve
16 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Classic CPA Output: “Waterfall Chart”
Product A, $50
Product B, $40
Services, $25 Cost to Aquire, $30
Cost to Serve, $30
Overhead, $20
Profit, $35
$0
$50
$100
Product A Product B Services Cost to Aquire
Cost to Serve
Overhead Profit
Key components of profit and loss per customer $ per Customer
16
17 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Maximizing profitability of the full customer rela%onship
Customer Life%me Value (aka CLV) = the accumulated profit or loss from each customer over the course of that customer’s rela5onship with you. Including: 1. Cost of acquiring the customer (genera%ng first purchase)
2. Revenue from all products over %me 3. Costs of goods and services sold (COGS) 4. Customer service costs 5. Opera%ng costs 6. Cost of capital
18 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Table of Contents
1. Customer Profitability Analy5cs (CPA)
2. High Impact Use Cases
3. Calcula5ng CPA at the Customer Level
4. Data and Tech Requirements
5. Using Big Data to Maximize ROI on CPA
19 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Managing Customer Life%me Value
Customer Behavior
Offers
Service
Customer Experience
Messaging
Our Offerings + Ac%ons
Business Impact
Advocacy
Recep5vity (to new info, offers, etc.)
Revenue $ Now $ Future
Intangibles Word of Mouth
Advocacy Referral
Nega5ve Word of Mouth
Costs
Loyalty Demographics
Customer Interac%ons
Aaributes Wants + Needs
Customer Knowledge
Psychographics
Profitability / History
Affini5es
Rela5onships
Etc.
Situa5onal needs
Situa5onal Aspira5ons
Price Sensi5vity
Service Sensi5vity
Channel Preferences
Etc.
20 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Elements of Maximizing Customer Life%me Value
Symbol Elements
Customer Acquisi5on / Marke5ng ROI
Share of Wallet Maximiza5on
Customer Loyalty and Reten5on
Product Design, Pricing, Promo5on, and Posi5oning.
Alloca5on of Resources (Capital, Budget, HR, etc..)
Impact of Customer Service, Customer Experience, and Customer Sa5sfac5on on Profit
Risk Management
In this sec%on we share a set of case studies, each of which involves the use of customer profitability analysis to improve one or more of the elements below
21 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Example: Credit Cards – Taking Profitable Risks
Life%me profit per dollar of credit card sales
$-
$0.02
$0.04
$0.06
$0.08
$0.10
1st Quartile 2nd Quartile 3rd Quartile 4th Quartile
Life
time
Prof
it pe
r Dol
lar o
f Sal
es
More Risk Less Risk Quartiles by Risk Level
The Riskier Half of The Card Company Customers Generate 6 to 9 Cents per Dollar of Sales….
…while the “Safer Half” of The Card Company Customers Produce only 1 to 3 Cents per Dollar of Sales….
CLV Elements Customer Acquisi5on
Product Design
Risk Management
22 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Example: High-‐Value Customers of Apple
“Apple Evangelists” -‐-‐ Buy Mul@ple Products…and Upgrade ORen -‐-‐ Self-‐sufficient / expert users – the need less support
CLV Elements Customer Acquisi5on
Share of Wallet
Customer Loyalty
23 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Example: Mid-‐Value Customers of Apple
“Limited Rela7onship” -‐-‐ Buy only 1 or 2 Apple Products…and rarely upgrade -‐-‐ Not self-‐sufficient, need more help from support
CLV Elements
Share of Wallet
Customer Service
Customer Loyalty
24 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Example: Nega%ve-‐Profit “Customers” of Apple “Resource Hogs” -‐-‐ Rarely buy, if ever, and buy lowest margin products -‐-‐ Consume dispropor@onate sales, service, and support
resources. -‐-‐ Frequent warrantee or insurance replacement claims
CLV Elements
Resource Alloca5on
Customer Service
Product Design
Risk Management
25 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
CLV Elements
Loyalty
Product Design
Resource Alloca5on
Risk Management
Customer loyalty: Delta’s Frequent Flier Program
Decision Implemented: Tie Tier Status to Revenue per Mile instead of solely miles traveled.
Key insight: Customer’s were gaming the system to gain lucra5ve 5er status
Behavior Observed: A surprising % of not profitable customers were earning elite status.
26 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Delta’s Loyalty Program: Causal Model
Revenue
Revenue / Mile
=
Miles Flown
X
Before the change, Delta was
incen7vizing miles flown
The new program is incen7vizing revenue
1
2
CLV Elements
Loyalty
Product Design
Resource Alloca5on
Risk Management
27 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
What Delta Must have Realized…
Decile: 1 2 3 4 5 6 7 8 9 10 % of All Elite Members 30% 20% 10% 10% 8% 8% 8% 3% 2% 1% Rev / Mile $10 $8 $5 $4 $4 $4 $2 $1 $1 $1
Illustra%ve
CLV Elements
Loyalty
Product Design
Resource Alloca5on
Risk Management
28 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Risk Management: American Express Forgets to Bill
Decision Implemented: discover and fix an opera5onal error that led to some customers not being charged their annual fee.
Key insight: Certain customers had not been billed a yearly fee in YEARS
Behavior Observed: A sub-‐sec5on of loyal customers appeared to be genera5ng no revenue from Annual Fees
CLV Elements
Product Design
Risk Management
29 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
American Express Pla%num: Illustra%ve Customer P&L
1-‐year Elements of P&L Customer #1 Customer #2
Revenue
Annual Fees $500 $0
Late Fees $20 $20
Interest Expense $30 $30
Other Fees $60 $60
Cost Of Goods Sold (Interest Expense) $50 $50
Opera%onal Costs $150 $250
This difference should not exist for the same product
CLV Elements
Product Design
Risk Management
30 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Guide to Capitalizing on CLV (use this to recap from examples)
If you Know This About Your Customers You Can Benefit in These Ways:
The right risky customers end up crea5ng a huge amount of value over their life5me.
ID the most important customers and retain more value from customers that on first glance seem risky.
Customers who only buy one or two items end up cos5ng us the most in in-‐person customer support
Create customer service alterna5ves that will migreate these customers to less costly customer support channels.
Frequent travelers make up the majority of your best customers, but a sizable minority of frequent travels are below average, in large part because they use other carriers most of the 5me.
Poach travellers from other carriers
If certain customer of the same product are not genera5ng fee revenue.
You can iden5fy where there may be an opera5onal lapse where you are leaving money on the table.
31 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Table of Contents
1. Customer Profitability Analy5cs (CPA)
2. High Impact Use Cases
3. Calcula%ng CPA at the Customer Level
4. Data and Tech Requirements
5. Using Big Data to Maximize ROI on CPA
32 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Gesng the Math Right
Key Drivers of Profit – Simple Map
Gross margin
Expenses
Customer Profit
Non-‐Capital Expenses
Gross Sales
COGS
Cost of Capital
33 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Gesng the Math Right: Rela%ve Difficulty
The challenge increases as you proceed downward…
Gross margin
Expenses
Customer Profit
Non-‐Capital Expenses
Gross Sales
COGS
Cost of Capital
Hard
er M
ath
/ To
ughe
r Cho
ices
34 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
The Math: Gross Margin
Gross Sales = The Sum of the Number of Sales of Each Product x the Selling Price of Each Product
Less The Sum of the Number of Sales of Each Product x the Cost of Each Product (to the company)
35 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Gross Sales: Product Examples from Financial Services
§ Personal Banking • Checking
• Savings
• Credit Card
• Mortgage
§ Brokerage Account with Checking • Investments/Trading
• Checking
• Savings
36 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Expenses: Variable vs. Fixed
Variable Expenses
Fixed Expenses
§ Expenses which vary from period to period based on the volume of a unit
§ Examples: ACH Transac5ons, Statements Printed, Receipts
§ Expenses which remain fixed despite fluctua5ng volumes
§ Example: Cost of DEVELOPING a Web-‐Based Banking Applica5on (although the cost of hos5ng + support is variable)
Expenses
Non-‐Capital Expenses
Cost of Capital
Fixed Expenses
Variable Expenses
37 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
The Math: Alloca%ng Variable Expenses
For each expense line item, Customer Expense equals Expense per Unit x Number of Units Example: 3 Bank Teller TXNS x $10 per Teller Transac%on
Expenses
Non-‐Capital Expenses
Cost of Capital
Fixed Expenses
Variable Expenses
38 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
The Math: Alloca%ng Fixed Expenses
For each category of fixed costs, allocate based on the factor that makes the most sense given your analy%c purpose. Common op%ons: 1) Per customer 2) Per transac%on 3) Per ac%vity 4) Per dollar of sales or Gross Profit
Expenses
Non-‐Capital Expenses
Cost of Capital
Fixed Expenses
Variable Expenses
39 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
What Affects “Cost to Serve”?
Low Cost-‐to-‐Serve Customers High Cost-‐to-‐Serve Customers
Order standard products Order custom products
High order quan55es Small order quan55es
Predictable order arrivals Unpredictable order arrivals
Standard delivery Customized delivery
No changes in delivery requirements Change delivery requirements
Electronic processing (EDI) (zero defects) Manual processing
Li_le to no pre-‐sales support (standard pricing and ordering)
Large amounts of pre-‐sales support (marke5ng, technical, and sales resources)
No post-‐sales support Large amounts of post-‐sales support (installa5on, training, warranty, field service)
Replenish as produced Require company to hold inventory
Pay on 5me Pay slowly (high accounts receivable)
Source: Kaplan & Narayanan with revisions by Fitzgerald Analy5cs
40 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Week of: 31-‐ Oct 7-‐ Nov 14-‐ Nov 21-‐ Nov 28-‐ Nov 5-‐ Dec 12-‐ Dec 19-‐ Dec 26-‐ Dec 2-‐ J an 9-‐ J an 16-‐ J an 23-‐ J an
Phase
1.4 Define methodological approach (methods, concepts,
technology options)
1.2 Determine potential
segmentation criteria
3.4 Troubleshoot data
Key Tasks2.3 Develop revenue
and costing algorithms
2.4 Account for cross-unit effects
4.4 Document recommendations for ongoing maintenance and
enhancement
1.1 Gather input via interviews
1.3 Determine data availability
1.5 Plan development of prototype
2.5 Document methodology and data sources
1. S trategy & Planning 2. Design Methodology and Algorithms 3. Build Prototypes 4. S egment Analysis
2.1 Understand data sources in detail
2.2 Request and test data extracts
4.3 Identify key insights to drive additional segmentation analysis
4.1 Rank customers by decile
4.2 Initial segmentation analysis
3.1 Program customer profitability algorithms
3.2 Validate and modify where necessary to ensure accuracy
3.3 Finalize documentation of data definitions and profitability
algorithms
Example Project Timeline (Aggressive Ini%al Prototype)
41 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Table of Contents
1. Customer Profitability Analy5cs (CPA)
2. High Impact Use Cases
3. Calcula5ng CPA at the Customer Level
4. Data and Tech Requirements
5. Using Big Data to Maximize ROI on CPA
42 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Data Requirements: Input Data
Data Type Purpose in CPA Crucial Considera%ons
Customer List + Aaributes Basis of Analysis.
Unique ID Defini5on of Customer (!) or relevant en55es (Household? B2B Account? Etc.)
Sales Transac%on Data Gross Revenue Transac5ons need to be product specific
Product Cost Data Gross Margin
How variable is cost for a given product? What product sourcing decisions might we make?
Expenses by Line Item Alloca5ng Costs How to categorize costs
Ac%vity and transac%on volume data
To allocate costs of ac5vi5es
Where possible, ac5vity data that is customer specific is best Where ac5vity data is not tracked by customer served, other categoriza5on is useful (example: product, geography, etc.)
43 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Data You Must Create to Implement CPA
Data Type Decisions
Cost Alloca%on Factors
Granularity of ABC cos%ng “Anomaly Management” Best way to allocate fixed costs
“Proxy Benchmarks”
What missing data needs to be es%mated with a proxy, and under what circumstances? What proxy best suits the purpose
44 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Example: Credit Card CPA Model
Revenue Side • The Customer Profitability process takes all customer
transaction activity * ( revenue - generating and charge - offs ) and organizes them by customer , by year , and by month
• Key assumption : calculated factor to assess direct mail revenue
Dimensions Customer
Month Year
Measures Customer Statement Balance
Risk Management Data
Dimensions Customer
Month Year
Measures Sales
Fees / Charges Direct Mail Bad Debt
TXN Data
Input Process Output
Dimensions
Customer Month Year
Measures
Customer Profitability Model
1 . Revenue line items *
2 , Expense generating line
items ** 3 . Profit
Expense Side Expense line item assumptions
• The model breaks down all expense line items and attributes them at the customer level
• The model attributes them at the customer level by applying cost factors ( to various customer activities that imply costs
Interest expense assumptions • Cost to private label card companyof its accounts receivables (i.e.
cost of borrowing money customer statement balances)
• Dependent on various interest rate indices
Expense Data
45 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Data Management
Good:
§ ETL Process feeding a superimposed external client structure (and for each dimension such as product, etc)
Beaer:
§ Single client iden5fier inside all systems for straight-‐through processing. Other standard reference tables.
Best:
§ An ability to adapt to changes in business structure with changes to data management and data quality. In short, companies who manage data well have an analy5c advantage.
46 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Example: Data Flow Data Used in CPA Analysis
POS SaleECSDS
HEMSHost ECSDS
Management SystemICD JDANEW marketing
Automation System
Customer Level Metrics
Customer Profitability Data
Prophix
Accounting System
Report Web
Accounting:P&L
Cost AdjustmentCost Master Book
Labor costParts cost
Generic product costNat’l Customer Database
HR database
future
ArcherOLD Marketing
Information System
47 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Table of Contents
1. Customer Profitability Analy5cs (CPA)
2. High Impact Use Cases
3. Calcula5ng CPA at the Customer Level
4. Data and Tech Requirements
5. Using Big Data to Maximize ROI on CPA
48 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Big Data + CLV Management: 3 Key Spots
Customer Behavior
Offers
Service
Customer Experience
Messaging
Our Offerings + Ac%ons
Business Impact
Advocacy
Recep5vity (to new info, offers, etc.)
Revenue $ Now $ Future
Intangibles Word of Mouth
Advocacy Referral
Nega5ve Word of Mouth
Costs
Loyalty Demographics
Customer Interac%ons
Aaributes Wants + Needs
Customer Knowledge
Psychographics
Profitability / History
Affini5es
Rela5onships
Etc.
Situa5onal needs
Situa5onal Aspira5ons
Price Sensi5vity
Service Sensi5vity
Channel Preferences
Etc.
1
2
3
Richer Customer Knowledge
Beaer predic%ons
Ac%ons
49 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Big Data + Customer Knowledge
Demographics
Attributes Wants + Needs
Customer Knowledge
Psychographics
Profitability / History
Affinities
Relationships
Etc.
Situational needs
Situational Aspirations
Price Sensitivity
Service Sensitivity
Channel Preferences
Etc.
1
Text Analy%cs: 1) Call center transcripts 2) Social Media (Listening + Service)
Social Media 1)“Graph Analysis” 2) Affinity signals Loca%on data High-‐performance processing!
Clickstream Analy%cs -‐-‐ Interests -‐-‐ Response to UI
Examples:
50 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Big Data + Customer Behavior
Advocacy
Receptivity(to new info, offers, etc.)
2Text Analy%cs: 1) Call center transcripts 2) Social Media (Listening + Service)
Social Media 1)“Graph Analysis” 2) Affinity signals Loca%on data High-‐performance processing!
Clickstream Analy%cs -‐-‐ Interests -‐-‐ Response to UI
Examples:
51 How to profit from Customer Analy5cs in the era of Big Data | Copyright Fitzgerald Analy5cs 2014, all rights reserved
Big Data + Our Offerings and Ac%ons
CustomerBehavior
Offers
Service
Customer Experience
Messaging
Our Offerings + Actions
Advocacy
Receptivity(to new info, offers, etc.)
Loyalty
Customer Interactions
2
3
Text Analy%cs: 1) Call center transcripts 2) Social Media (Listening + Service)
Social Media 1)“Graph Analysis” 2) Affinity signals Loca%on data High-‐performance processing!
Clickstream Analy%cs -‐-‐ Interests -‐-‐ Response to UI
Examples: