Intro - Today’s Speaker
• Dr. Dave Schrader
> 20 years at Teradata, in both advanced development / engineering
(8 years) and marketing (12 years)
> Futurist, tracks lots of sources of innovative ideas
> Many interactions with leading edge companies, worldwide
> Focus on Active Enterprise Intelligence
> Ph.D. Computer Science (Purdue, Go Boilers!)
• Teradata
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Leader in data warehousing
$1.7B/yr revenues
Invented parallel database processing
Advocate of single source of data for decision-making
Blue chip customer base: Wal*Mart, JCPenney, Ace Hardware,
Continental, Southwest, Delta, Bank of America, Wells Fargo,
Apple, Western Digital, Ping, Cisco, eBay, AT&T, Verizon ….
> “Raising Intelligence” as a theme
> “Agility” is this year’s focus
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Workshop at The Ohio State University, Feb 2010, © Teradata
Managing by the Numbers:
From Art to Science
Workshop at The Ohio State University
February 26, 2010
Dave Schrader, Teradata
Why Are We Doing This Workshop?
• “Better, Faster Decisions” is the theme
• “Manage by the Numbers” is the strategy
• Improving Service is one goal (and tie to IMS)
• “From Art to Science” is why we’re at Ohio State
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Workshop at The Ohio State University, Feb 2010, © Teradata
Pop Quiz
1. How many decisions
were made yesterday
in your company?
Real Time Enterprise Decisions
2. How many decisions
were made using data
and BI?
3. What % of decisions
should have been
made using data?
4. Do you know the
value of making
faster decisions?
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Workshop at The Ohio State University, Feb 2010, © Teradata
Motivation: It’s Possible
• Tom Davenport’s “Competing on Analytics” has
numerous examples.
• Key aspects
>
>
>
>
Executive sponsorship
Metrics
Training
Feedback loops
• Two Motivational Stories
> Overstock.com: Patrick Byrne
> Harrah’s: Gary Loveman
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Workshop at The Ohio State University, Feb 2010, © Teradata
Dropping
Margins
Active Enterprise Intelligence at
Faster Financial Insights
Situation
Fast-moving online retailer of overstocked
merchandise. Needs to quickly bring up and
take down new offers.
Problem
Needs to set pricing to clear merchandise
quickly but without demand over-running
supply. Needs to constantly monitor all
systems, including financials, tying all parts
of the business together
Solution
Constructed 70 dynamic dashboards for use
across all business groups. Corporate
Profit and Loss rollups happen every 2
hours.
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Impact
• CEO can “see”
problems more quickly,
focus energy on
problem areas
• Corporate decisions
driven by data
• Decisions reflected
quickly in financials
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at
Player “Comps”
Situation
Over-compensating some customers
(winners), under-compensating others
(losers) with comps. Saw opportunity to use
excess show tickets to improve overall
experience for selected gamblers.
Impact
Problem
Lacked the ability to track real-time
customer gaming behavior and relate to
comps. No mechanism to communicate or
react to specific behaviors and trends.
Solution
Used the DW to drive real-time comps to
selected customers at each gaming point,
through Good Luck Ambassadors.
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• Higher customer sat
• Increased trip
frequency of regular
players from 1.2 to 1.9
times per month
• Annual ROI: 389%
• Payback period: 3
months
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence for Decisions
Make Better Decisions, Faster – Across the Enterprise
Strategic Intelligence:
Great Insights about the Business
Align and Accelerate
Operational Intelligence:
Operations People and Systems
Become Smarter and Faster
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Workshop at The Ohio State University, Feb 2010, © Teradata
Typical Strategic Intelligence Decisions
At the Strategic Level:
• Which customers are the highest
priority to retain AND what pricing
discounts should I give?
• Which products drive profitability?
• What are the operational issues
and
driving the most cost?
• How can I build more value into
relationships?
WHY?
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Workshop at The Ohio State University, Feb 2010, © Teradata
Operational Decisions
“We judge leaders by how well they make
big strategic decisions. But corporate
success also depends on how well rankand-file employees make thousands of
small decisions. Do I give this client a
special price? How do I handle this
customer’s complaint? Should I offer a seat
upgrade to this passenger? By themselves,
such daily calls – increasingly made with the
help of technology – have little impact on
business performance. Taken together they
influence everything from profitability to
reputation”*
*Frank Rohde, Harvard Business Review – June 2005
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Workshop at The Ohio State University, Feb 2010, © Teradata
The Approach
• Number of decisions at the Strategic
level is lower, impacts are larger,
cycle times are larger, decisionmaking processes are people-centric:
harder to measure
• Number of Decisions at the
Operational level is higher, cycle
times are smaller, decision-making
processes can often be automated:
easier to measure
• This workshop focuses on exploring
the “easier” area of Operational
decision-making.
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Workshop at The Ohio State University, Feb 2010, © Teradata
Agenda for Today – 4 Topics
1. The state of the art
2. Best practices – case stories
BREAK
3. Role plays to apply the concepts
4. Typical impediments, as well as
suggestions for you to help your
company manage by the metrics
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Workshop at The Ohio State University, Feb 2010, © Teradata
The State of the Art
A quick survey of the best sources on decision-making
“Organizational Agility: How Business Can Survive
and Thrive in Turbulent Times”
Research at MIT:
“Agile firms grow revenue
37% faster and generate
30% higher profits”
N=349 Business Execs in 19 Industries
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Workshop at The Ohio State University, Feb 2010, © Teradata
“Organizational Agility: How Business Can Survive
and Thrive in Turbulent Times”
The Economist Intelligence Unit, 2009
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Workshop at The Ohio State University, Feb 2010, © Teradata
“Does BI = Better Decision Making?”
Gartner - Gareth Herschel
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Workshop at The Ohio State University, Feb 2010, © Teradata
“Does BI = Better Decision Making?”
Gareth Herschel
Gartner - Gareth Herschel
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Gartner Group, 2009
Workshop at The Ohio State University, Feb 2010, © Teradata
Tom Davenport, Babson College
• “Competing on Analytics”, 2007: “What did they do?”
> Motivational stories about companies who have achieved
success
• “Analytics at Work: The New Age of Smart DecisionMaking”, 2010 – “How did they do that?”
• Focus on analytics as tools for better decision-making
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Workshop at The Ohio State University, Feb 2010, © Teradata
Smart (Enough) Decisions, 2007
Neil Raden and James Taylor
Best forwarding looking book I’ve read in the past 5 years
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Workshop at The Ohio State University, Feb 2010, © Teradata
The Link Between
Strategic and Operational Intelligence
Using Data to Drive Strategic Decisions
Strategic Intelligence on Attrition
Why did you change your primary
bank, other than moving or
job change?
Customer Service
25%
Rates, fees, minimum balance
22%
Location of branches or ATMs
17%
Access to electronic banking
12%
Bank mergers
10%
No response
5%
Causes are
OPERATIONAL
Causes
• Long wait time for
service
• Bank employee lack
of knowledge
• No employee
ownership of issue
resolution
• Hand off to other
bank employees
Source: Federal Reserve Bank (Kiser 2002)
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Workshop at The Ohio State University, Feb 2010, © Teradata
Why Customer Satisfaction is So Important
and Why Service is Our Focus Today
After Positive
Experience
Perceived value,
did nothing
Purchased
new product
13%
After Negative
Experience
Perception worse,
did nothing
29%
28%
58%
N=1528
Decreased value
of products
purchased at bank
23%
20%
14%
Increased value
of products
purchased at bank
Source: “The ‘Moment of Truth’ in Customer Service”, Beaujean, Davidson, Madge,
McKinsey Quarterly , Number 1, 2006
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Bought product
at another
bank
Workshop at The Ohio State University, Feb 2010, © Teradata
15%
Stopped
product
Switched
banks
N=701
GOAL: Link Customer Insights With Actions
Then Measure Effectiveness
STRATEGIC INTELLIGENCE
OPERATIONAL INTELLIGENCE
Insights
Into
Action
Intelligence
INSIGHTS
ACTION
Results
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Workshop at The Ohio State University, Feb 2010, © Teradata
Why Multi-Channel + Real-Time?
Generation Y and Z people have …
• Never lived in a Batch world
• Think Enterprises who don’t have
their act together in terms of
information and interfaces are
“beyond incompetent”
• E.g. they update their Facebook
information and it's accessible
2 seconds later to their friends via
their mobile phones
Next Gen systems need to move from
“Inside Out” thinking to “Outside
In” thinking:
Total Customer Focus
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Workshop at The Ohio State University, Feb 2010, © Teradata
Mobile is “Hot”
Intelligent interchange of data ensures consumers receive:
‘anticipated, timely & meaningful’ messaging
Promotions/Coupons:
Surprise & delight
Service Alerts:
“first/last few
moments”
Timely reminders; first
(last) hours/minutes of
sale
•
•Send offers/ sales/
promotions to the customer
•Link with loyalty/ VIP
information
Special Events/
Sweepstakes/ Door
Busters:
Incremental Visits/Shops
• Invite to (store) events
•Entertainment
• Provide critical account
information: delivery
options, balance, status,
just been OUTBID
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•Cooking Class
•Product Tastings
Workshop at The Ohio State University, Feb 2010, © Teradata
Even Bidding on Google Keywords Is Real-Time
• Daily decision: how much to pay for Google keywords?
• Daily answer: use BI to decide, based on traffic and
quality of who is clicking
> Not all keywords are equal, and not all clickers are equal
> Suppose a business bids on placements for 1,000 keywords
in 20 geographic regions using 10 creatives and 5 different
landing pages. That’s 1 million bids to manage, day after
day. Add seasonality, multiple search engines, and different
keyword matching types, and the number is even bigger.
> Using automated analytics, businesses can double the ROI
of their paid search spend. For example, you may be able to
double the visits, registrations, applications, sales, revenue,
or profit obtained without increasing your ad budget
> For more info: www.optiminesoftware.com
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Workshop at The Ohio State University, Feb 2010, © Teradata
State of the Art: Work with Ohio State
on “Active Value Curves”
Ohio State MBA Interns:
Nikunj Poddar
poddar_7@fisher.osu.edu
and
Prasant Balakrishnan
balakrishnan_29@fisher.osu.edu
Customer Cardiograms Are Filled with Events
Each Event Can Create or Destroy Value
Bought
Returned
Bought
Value
Lost
Customer
Service
Problem
Bought
Bought
Acquired Customer
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Another Service
Problem:Cancels
Workshop at The Ohio State University, Feb 2010, © Teradata
Time
Explore “Better” and “Faster” By the Numbers
• Expand the Richard Hackathorn Value Curve
Business event – Like A Service Incident
Value
Data captured
Insight developed
Decision
taken
Time
New
Decision window
Decision
window
TDWI The Business Case for Real-Time BI, Based on concept developed by Richard Hackathorn, Bolder Technology
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Workshop at The Ohio State University, Feb 2010, © Teradata
Service Failure – Generic Example
Situation
When a service incident occurs, customers
have expectations that must be met with
regard to remedies. Not meeting these in a
timely way can destroy loyalty and cause
customer defection.
Problem
Companies typically do not know, and have not
measured, the impact of apologies and in
particular, how time plays a factor.
Solution
Use technology to create a Value Curve so the
impact of near real-time vs. “later” remedies
and apologies can be seen, vs. the costs.
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Impact
• Better understanding of
the impact of response
time for service incidents
• Use this knowledge to
make quantifiable
improvements in service
remedy processes
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Value Curve
Are Faster Apologies Better?
Gains Value
Resolution and notification before customer knows
Business Event – e.g., a Service Incident
Apology within 15 minutes
Apology within 3 hours
Apology within 24 hours
Loses Value
Time
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Apology within 3 days
No
Apology
Workshop at The Ohio State University, Feb 2010, © Teradata
Value Impacts – Apology By Formats
“Better” Can Also Involve Format
Values
Service Incident
$0
Immediate text message apologizing with discount
OR Immediate text message with no discount
Outbound Contact Center apology
Inbound Contact Center employee apology
Time
IVR apology
Apology letter in the 3 day mail
No
Apology
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Workshop at The Ohio State University, Feb 2010, © Teradata
Comparison of Two Treatments – Better
Request for Car Insurance Quote on the Web
Gains Value
Treatment 2:
Quote based on Zip Code
plus ethnic pictures
Treatment 1:
Quote based on Zip Code
RFQ
Take
Rate
1.7%
Higher
Time
Loses Value
50 msec
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Workshop at The Ohio State University, Feb 2010, © Teradata
Financial Case: Stock Purchase
Not All Curves Have the Same Slopes
Value Curve – Financial Trading
Perceived or Actual Value
Market Information
Value
decays
slowly at
start
Time
Value could
decay
rapidly
Stale
information
0
Decision
window
1 – 2s
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Negative Value
Workshop at The Ohio State University, Feb 2010, © Teradata
Phone Store Geospatial Case
Not All Curves are Equal!
Potential Value
Geolocation based marketing
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Customer enters
Zone conducive to
Geolocation
marketing
Customer leaves
the location
Decision
window
10-15
minutes
Workshop at The Ohio State University, Feb 2010, © Teradata
Time
Better Target Marketing with Geospatial
Time and Space
• Which customers should I target for my campaign?
> Typical data
–
–
–
–
Customer segmentation
Sales history (RFM)
Demographic information
Customer loyalty
> Enhanced with geospatial data
– How far will customers drive to
shop at my store?
– Which of my competitor’s
customers can I draw to my
store with an aggressive
campaign?
– Which customers live close
to my store?
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Customer
Profile
•
•
•
•
•
•
•
Demographic
Recency
Frequency
Monetary value
Segment
Loyalty score
Price sensitivity
Geospatial Intelligence
• Willing to drive 30 miles for
25% discount
• Lives 25 miles away from Store
ID: 143
• Lives within 10 miles from my
competitors
Workshop at The Ohio State University, Feb 2010, © Teradata
Target Delivery Service to Profitable Customers
Zip Codes
92024
92009
92009
Regions
5 mile radius
15 mile radius
*Deliver Zone
Customer
Segment:
Store
Customer
<$50
Profit/order
Score
>=$50
Profit/order
Score
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92024
Workshop at The Ohio State University, Feb 2010, © Teradata
Retail - Timing of Product Marketdowns
Impacts on Revenue and Margin
For products that are
Price-elastic, use data
to compute the optimal
curve as well as timing
Margin
Full Price
10% off
25% off
$0
50% off
70% off
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Workshop at The Ohio State University, Feb 2010, © Teradata
Time
Product
Cleared
Retail - Timing of Product Introductions
Impacts on Revenue and Margin, inventories
Margin
For products that cannibalize
sales in the same product
category, compute optimal
rollout timing.
$0
Product A
Product B
Product C
Time
Customize rollouts, operationally: Target
fashion-forward and fashion-lagging customers
Over the Web or Contact Center
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Workshop at The Ohio State University, Feb 2010, © Teradata
Airlines – Yield Management
Revenue or Margin
Price = $498
87%
$350
$300
$295
Bookings
$0
Time
360 days
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Workshop at The Ohio State University, Feb 2010, © Teradata
A Sampler of Best Practices
Across various job functions … 14 more examples
Topics
• Active
• Active
• Active
• Active
Customer Management
Customer Service
Finance
Supply Chain / Logistics / Asset Management
• And – A Mystery Guest Speaker From the Audience
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Workshop at The Ohio State University, Feb 2010, © Teradata
JD Williams:
Retail Customer Case Study
• JD Williams Limited is the UK's
leading direct home shopping
compan
• Operates 30 successful
catalogue brands, with 50+
websites and newly acquired
10+ store locations.
• Today the company has over 2
million customers and 4,000
employees.
• JD Williams is one of the most
profitable online and home
shopping (catalogue) retailers,
with annual sales of around
£560 million.
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Workshop at The Ohio State University, Feb 2010, © Teradata
JD Williams:
Talk in London on Feb 1st
• One of 3 finalists at the Gartner Group
Business Intelligence event
• “One of the biggest mail order
companies you have never heard of”
• 39% of business now comes from the
web
• Target audience is “large ladies” and
“high and mighty gents”
• New effort to collect much more web
information
• Business goal – “recovery” of possible
orders that didn’t go through
> Out of stock
> Customer bailouts
• Another insight
> “Silver Surfers” made 15,000 iPhone
web orders in January
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Workshop at The Ohio State University, Feb 2010, © Teradata
Integrated Web Intelligence at JD Williams
‘Abandoned Baskets and Dropped Demand’
Situation
Unable to capture on-line customer browsing and
purchasing behaviour to gain insight into how
customers were shopping within their multichannel business.
Problem
Impact
Could not identify true customer abandoned
purchases, nor ‘dropped demand’ (out-of-stock
item) as browsing behaviour was not captured
and integrated with their call centre and
catalogue customer data.
Solution
Integrated customer on-line behavioral (not just
transactional) data using Speed-Trap technology
alongside their off-line customer data. Use the
data to drive personalized targeted recovery
campaigns from true abandoned purchases.
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JD Williams Group
• Understood accurate level
of customer abandonment
versus alternate channel
purchase and subsequent
purchase. Insight into
product affinities.
• Recovery from Out of
Stock.
• Enabled personalized
targeting campaigns
delivering “significant and
material” improvements
on campaigns
Workshop at The Ohio State University, Feb 2010, © Teradata
Proof of Concept at J D Williams
Dropped Demand (Out of Stock) – Results So Far
• Dropped demand (or “out-of-stock” items) was measured to be
exactly the same as for other channels, such as phone or store
• If you do nothing
> Reorder rates against the same product by the next day was 5% and
after 5 days was 8%
> Reorder rate within the same merchandise category by the next day
was 11% and after 5 days was 21%
• Remaining 79% of dropped demand was fed into Relationship
Management Customer Management tool for inclusion in targeted
recovery programmes.
• Results still being measured, looking very good.
• Writeup coming from Bill Gassman, Gartner Group report
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Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Travelocity
Personalized Web Offers
Situation
Response rates of targeted e-mail
campaigns were good but not increasing.
Conversion rates of offers during web
shopping visit also not meeting goals.
Problem
Offers presented were “generic” - did not
make use of detailed customer specific
information – origin, shopped destinations,
travel interests, and “deals” (30% price
drop over 30 day moving average)
Solution
Used the DW to display personalized realtime web offers and send more focused
email offers reflecting recent and specific
interests during the web shopping session.
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Impact
• Leveraged existing
information in the data
warehouse
• Personalized email
offers are 8-12X more
successful than generic
offers
• Online 7x more clicks,
4-5x more bookings
• 1.5M additional queries
per day to do
personalizations (50ms
RT) used less than
0.03% of system
Workshop at The Ohio State University, Feb 2010, © Teradata
This Just In (2/24/2010)
• I browsed on Monday 2/22/2010 for flights to Puerto
Vallarta for my nephew’s upcoming wedding this fall.
• Didn’t buy anything, just looked at flight schedules.
• This came in on Wednesday:
• My perception: “The GNOME ROCKS!”
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Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence™ at DirecTV
Saving Potentially Defecting Customers
Situation
14,000 Customer Service agents field
600,000 calls / day. Some are
disconnection requests.
Problem
Need to quickly identify good
customers who are unhappy and
cancelling service, then launch “save”
campaigns to turn them around
Solution
Goldengate rapidly uploads
information from the Call Center to
the DW, which drives churn reports for
Save Teams.
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Impacts
• Churn Report runs every 15
minutes
• Save Team contacts highrisk / high-value customers
within 3 hours with special
incentives to stay
• Saves 25% of “At Risk”
subscribers from
discontinuing service
• Churn is 1.33% per month
(lowest in industry), a 10
year low
Workshop at The Ohio State University, Feb 2010, © Teradata
AEI™ at Etisalat Misr (Egyptian Telecomm)
Enterprise-wide Information Helps the Call Center
Situation
• Need to have more data at the fingertips
of more employees, needed a Single
Version of the Truth
• Ongoing need for more insights about
customers to reduce churn, add to sales
Problem
• Typical silo’d operation, 2 previous
efforts failed
Solution
• 15 people built new EDW in 7 months,
feeds from 17 source systems
• Enterprise-wide; now 50% (900) of
employees use the ADW to do their jobs
• Marketing built churn models, Sales
information available to the Call Center
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Impacts
• Call Center First Call
Resolutions increased
50%
• Average Handling Time
dropped from 240 to 90
seconds
Workshop at The Ohio State University, Feb 2010, © Teradata
Organizations and Applications Using ADW
Wider Use than Just the Customer Care Group …
• Sales:
> Provide daily & monthly Sales KPIs, Sales Channels ARPU Analysis, Sales
Channels Profitability analysis.
• Marketing:
> Product and Services Management, Tariff Analysis, Customer Retention
Activities, Customer Lifecycle Management, Segmentation, Promotion
Management, and Creating & Assessing Business Case.
• Customer Care:
> Customer Call History, Back office support
> Risk Management: Collection and High Usage reports & alerts.
> Fraud Management: reports and Payment Behavior Scoring.
• Finance:
> Revenue booking, End Month Closing, External Auditors analysis, Service Assurance:
Set of Reconciliation Processes between different Source systems.
• Engineering:
> Sites Deployments. Sites importance based on revenues and customer
segment served.
• Regulatory:
> Provide needed information to the local authorities.
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Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Wells Fargo
MySpending: Customer Web Access to Transactions
Situation
Opportunity to use the Web to provide direct
customer access to their bank account
activities. Adds convenience and decreases
number of calls to the customer service
centers. Also provides opportunity to add
value-added services, e.g., budgeting.
Problem
Information needed to be loaded frequently,
not in batch mode at night. Also needed to
add Web front-end software and apps. Also
needs to be highly available.
Solution
Impact
• Millions of accesses
each month; 20,000
queries/hour
• Decreased costs for
Customer Service
• Higher loyalty
Web software constructs spending reports
from the EDW, loads 15 million records/day.
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Workshop at The Ohio State University, Feb 2010, © Teradata
My Spending Report
• An example of
an AEI
Operational
Application
that is Fairly
Simple
But
• Is Accessed by
Millions of
Customers
• It’s a Mission
Critical System
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Workshop at The Ohio State University, Feb 2010, © Teradata
My Spending Report
• Customization  Stickiness
for WF Customers
• Spending Categories
 opportunity for Further
Value-added Analytics
• “Track Every Dollar you
Spend”  Pressure to Supply
Detailed & More Fresh Data
• “Spending History” 
Pressure to Store More
Historical Data
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Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Highmark
Health Coaches
Situation
Need to improve health of people with chronic
conditions (diabetes, heart conditions) with
condition management programs. Also cut costs.
Problem
Disjointed information systems. No single view of
the patient and all follow-up services.
Solution
Uses predictive analytics to identify the right
subset of customers to target with specific
condition management programs. When a
member is discharged from the hospital, a
trigger goes off in Teradata to drive outbound
calls to enroll in the program and get follow ups
from a health coach.
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Workshop at The Ohio State University, Feb 2010, © Teradata
Impact
• Better chronic
condition
management
• Analytics reduced
false positives,
reducing list of
35,000 potential
diabetic patients
by 25%.
Active Enterprise Intelligence at Unum
Customer and Broker Self-Service
Situation
Need for 2500 internal business users to see upto-date claim and customer information.
Need for 1000s of customers to get claim status.
Problem
Impact
Disjointed information systems. No single view of
up-to-date data. Interactive Voice Response
(IVR) and Call Center not connected to the EDW.
Solution
Integrated daily information from 8 feeds (750M
rows/day) on policies and claims, providing daily
forecasts to manage $12B of reserves. 3 new
web and IVR self-service systems with intelligent
call center routing. Claim status via self-service
for customers. Real-time visibility for quicker
underwriting at the broker portal.
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Workshop at The Ohio State University, Feb 2010, © Teradata
• Customer calls
handled within
targets went up from
60% to 95%
• Faster call routing let
company re-assign
30 agents to higher
value tasks
• Added 250,000
tactical queries/day
to 325,000 BI
queries
AEI™ at Norfolk Southern
Self-Service Operational Information
Situation
Increasing numbers of internal and
external customers with BI needs
Problem
IT group overloaded, falling behind
on constructing custom reports
Solution
Built self-service portal with custom
reporting capabilities, backed by 3
person help desk. Data loads near
real-time on freight shipments, crew,
payroll etc. into 6800 tables.
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Impact
• >3,000 internal and
>12,000 external users
• 500,000 queries/day
• 9500+ variations of reports
and 4400+ new reports
built by users
• 1900+ standard reports
pushed each week
• “They love it”
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Teradata
Improved Revenue Management Reporting
Situation
Need for more timely and consistent views
into financial data, easy for management to
use; do deep dives on Teradata revenues.
Problem
Needed single view of the business, wider
availability of information throughout the
company, more timely data (lots of activity
in last week of Quarter).
Solution
Moved to “self-service” for financial
information with real-time data feeds.
Built Management Analysis Portal with
drill down capabilities to view important
financial drivers by customer/geography/
sales group over time.
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Impact
• ERP data loaded 3X/day
• Visibility via Dashboard access
to Financial information
throughout Teradata
• Provides data to Sales –
checks of accuracy, plans,
expenses
• Provides data to Marketing –
e.g., should Teradata invest
more in a reseller or partner
• Provides Sales/Service
Management with up-to-date
estimates of results
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Teradata
“Closing the Books Faster”
Situation
Needed to close quarterly/annual
books faster and more accurately using
an automated and auditable process.
Problem
Complex sources (including outsourced
Accenture front-ends). Needed single
view across multiple ledgers. Need
ability to do complex reporting.
Solution
Trickle-feeds general ledger and
other financial information 3+ times
a day from worldwide locations into
Teradata.
59
Impact
• By getting the best data
into the systems as fast as
we can, we can spot and
fix errors quickly
• Takes 45 minutes to do
G/L loads, speeds up
ability to see consolidated
financials
• Now takes 2 days to do
sub-ledger closes, 6 days
for quarterly results
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Teradata
“Faster Accounts Receivables”
Situation
Teradata VP Bob Young set goal to reduce
receivables from 100 to 60 days average.
Wanted to move to automatic detection of
overdue accounts vs. lots of phone calls
with account teams to get status.
Problem
Needed a single source of information to
obtain global receivable information on
accounts over 90 days.
Solution
Use ADW to provide weekly reports at the
Customer/Invoice level to management
that includes most recent action taken on
aged receivables and highlights invoices
that are under dispute.
60
Impacts
• Provides visibility of
aged global A/R reports
to Senior Management
at a customer/invoice
level
• Average receivables
timeframe has shrunk to
70 days, a 10%
reduction in backlog
from $507M in 2007
to $451M in 2008
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at GE Rail
“Shoptimizer” – Optimize Repairs of Boxcars
Situation
Major railcar equipment provider with 245,000
assets provides leasing, financing, repair, and
tracking services. Repair and maintenance costs
over $100M/yr, with 400,000 repair events.
Problem
Rail cars were repaired reactively. Weeks of
inefficient routing. Shop selection made without
knowledge of capacity, capability, rail car
destination, or history. Proliferation of tools and
systems made it hard to optimize operations.
Solution
Built “Shoptimizer” application on the EDW to
decide when to take rail cars offline for repair or
maintenance. Finds the optimal and most
economical repair shop to provide railcar repairs
subject to shop capacity and capability, plus 20
more constraints and dynamic parameters.
61
Impact
• CSRs have dynamic,
predictive insights into shop
capabilities and capacities,
best place to repair
• Estimated incremental
annual savings of >$1M in
freight and transit costs
• Fewer repeat repair events
and recalls, reduced freight
and rental credit $$$
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Lufthansa
Optimize Check-In Agent Staffing
Situation
122,000 check-ins / day. Surges in
passenger check-ins can cause long queues
at airports resulting in passenger dissatisfaction and occasional missed flights.
Problem
Need to balance check-ins and staffing
levels, move staff on-demand to smooth
out check-in surges.
Solution
Carrier uses the EDW to monitor online
check-ins (including checked bag inputs)
by passengers, then computes hourly
expected at-airport check-ins. This drives
dynamic staffing decisions for both counter
and baggage stations.
62
Impact
• Reduced queue lengths
for airport checkin
• Better utilization rates
for airport staff
• Higher customer
satisfaction
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence in Insurance
Flu Outbreak Detection
Situation
Flu kills 36,000 people per year in the USA, lasts
9-10 weeks, causes 7% of deaths. Insurance
company monitors flu outbreaks to identify and
mitigate risks in their patient population.
Problem
Need to integrate CDC information with
information from sister Blue Cross / Blue Shield
divisions. Predict where flu outbreaks will happen
in patient population, order vaccines, and advise
doctors to give flu shots.
Solution
EDW users capture/store/analyze inbound data
feeds, run risk analytics, create action plans, and
drive those through channels to doctors and client
companies, then monitor compliance and impact.
63
Impact
• Saves lives
• Lowers health care
costs, fewer
hospitalizations
• Optimizes allocations
of flu vaccines to
needed locations
Workshop at The Ohio State University, Feb 2010, © Teradata
Watching the Spread of Flu
• Flu tends to start at either coast and move inland
• From December 2005
To February 2006
• Able to predict flu incidents in their mid-west coverage areas
from the data feeds
• Predictions drive recommendations to doctors and
appointments for immunizations of high-risk insureds
64
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at NCR
Active Service Supply Chain Optimization
Situation
Top 10 Vendor, $2B/yr service revenues
from break/fix on 3.5M products in the
field. 10,000 customers in 110 countries,
22 customer care locations, 12,000
employees to schedule/optimize. $500M
inventory to manage, >1000 stock
locations.
Problem
Impact
20,000 service interactions/day. Needed a
closed loop system to optimize scheduling,
ensuring SLA coverage, dynamic updates
to hourly break/fix plans.
Solution
Built Intelligence Services Architecture to
capture, report, and optimize all aspects of
service.
65
• Higher customer
satisfaction
• Higher achievement of
SLAs
• Constant closed loop
process improvements
Workshop at The Ohio State University, Feb 2010, © Teradata
NCR’s Intelligent Services Architecture
SERVICE
APPLICATIONS
NCR SERVICE
DELIVERY FORCE
1a
CUSTOMER
POPULATION
1,000’s of
customers
1,000,000’s of
devices
across:
Industries
Geographies
Technologies
Environments
66
1,000’s of
service professionals
1,000’s of locations
1,000,000’s of
service actions
leveraging:
Industry Knowledge
Global Infrastructure
Technical Expertise
Incident Creation
Entitlement
Dispatch
Help Desk
Parts
Resolution
Device Management
Billing/Invoicing
Asset Management
Deployment PM
5a
Management
Scorecards
5b
5c
Managed Services
Performance Reports
Workshop at The Ohio State University, Feb 2010, © Teradata
3
Teradata Data
Base Engine
20,000 service
actions/day
actionable IT intelligence
= Business Value
1b
NCR
DEVELOPED
SERVICES
DATA MODEL
2
4
STRUCTURED
ANALYSIS
• Service delivery
• Product performance
• Customer related
6
Sigma
Customer Breakpoint Intervention at Nationwide
Situation
Benefits
Primary and industry research indicates that
proactive customer communications around
certain customer lifecycle events had
significant impact on customer’s satisfaction
and retention.
Problem
Number of proactive communications were
too great for the operations to contact every
customer experiencing key events.
Solution
Integrated customer contact history, product
ownership and payment information into
customer data warehouse. Enabled behavioral
analytics team to create prioritization models
that identified which interaction for which
customer was most important at that time.
68
 1 point improvement in
retention rate among
highest priority contacts.
 Improvement in customer
enthusiasm scores for
contacted customers.
 Full adoption of highest
priority action for one
intervention will drive
additional premium of more
than several million dollars
per year.
 Insights distributed
through this framework
have delivered more than
$100 million in incremental
sales.
Workshop at The Ohio State University, Feb 2010, © Teradata
Time for a 15 Minute Break!
Please
SIT NEXT TO SOMEONE YOU DO NOT KNOW
Role Play #1: Columbus Telecomm
Role Play #1:
Telecommunications Case
CONTEXT
• You work for a major Telecomm company named
Columbus Telecomm (CT). Fiercely competitive ads by
competitors focus on your inadequate coverage in
selected markets.
• Key performance indicators are available. You are losing
some market share, about 10% in the last year.
• You are launching an effort to improve cell coverage but
this will take some time. In the meantime, you must
“save” potentially defecting customers.
• You have a Telco Data Warehouse, the TDW. You can
make recommendations to IT on what data should be
added to it, and what data should be loaded at what
frequencies.
71
Workshop at The Ohio State University, Feb 2010, © Teradata
What Just Happened
• At 9am, your customer “Mike Smith” is driving from his
home in Columbus to a Cool Conference in Nashville.
• At 10:12am, he tried calling his wife and 10 seconds into
the call, it was dropped.
• At 10:20am, his boss called and the reception was not
very good. 3 minutes into the call, it was dropped.
• At 3:12, he arrives in Nashville and decides to call your
Contact Center to complain.
• Let’s Listen In …
72
Workshop at The Ohio State University, Feb 2010, © Teradata
The Contact Center Screen
Customer
X
Michael B. Smith
1214 Northern View Drive
Columbus OH 43223
Renewals: 07/02/09
Affinities: e-Nest3
Product links
Trigger
Personalized offers
Revs
$182 /mo business account
$280 /mo family account
Billing
No auto pay
On time pay: 100%, never late
Tier
8 of 10 profitability
614.224.2190
708009838228
MBS@gmail.co
m
Joint account
Customer History
!
X
X
Contact
Summary
Call Ctr
Inbound
email
Outbound ! 08/18/09
Call Ctr
Inbound
Date
07/02/09
08/21/09
My Sales Targets & Scores
Offers Made
Target
75
Actual
63
Sales
$ Target
81%
X
Hand
offs
21
>
Workshop at The Ohio State University, Feb 2010, © Teradata
>
<
73
Acct Age: 12
Last order: 08/21/09
Last offer: B707
<
Customer View
What Would You Do?
• Options?
• Give him
>
>
>
>
>
74
XXX Free Minutes
$$$ Discount off bill
An extra service for free for some period of time
New equipment
Better plan
Workshop at The Ohio State University, Feb 2010, © Teradata
More Information From Finance and Marketing
• Financial Data
> Lifetime value prediction: $42,000
> Cost of re-acquiring lost account: $359
• Marketing Data
>
>
>
>
>
>
Churn predictor: 2.5 on 10 (normally unlikely)
An early technology adopter
Responds quickly to new product offers
Influencer (new SNA campaign): net promoter
Target of campaign to start in 2 weeks to upgrade
From what we can tell, is the target of competitors’ latest
round of acquisition campaigns
• 2rd round inputs – what do you do?
75
Workshop at The Ohio State University, Feb 2010, © Teradata
More Information From Operations
• Operations Data
> Dropped calls in this region will be resolved with new cell
tower capabilities on 3/10/2010
> We are loading dropped call information now real time
• 3rd round inputs – what could you do?
76
Workshop at The Ohio State University, Feb 2010, © Teradata
Service Remedy – Telecommunications
Situation
Mobile phone company drops 2 calls while Mike is
driving from Columbus to Nashville. Mike calls when
he arrives in Nashville to complain.
Problem
Contact Center agent doesn’t have enough context to
know what to say – just records the complaint but
cannot build any loyalty with remedies – or must use a
one size fits all remedy – e.g., all complainers get $5
off next bill, or an apology form letter 2 weeks later.
Solution
Before Mike starts complaining: “We are terribly sorry
about the 2 dropped calls this morning at 10:12 and
10:20 in the Wilmington OH area. We are installing a
new cell tower in this area within 1 month, and
because you are very valuable customers, I’ve already
taken $50 off your next bill. Can I help you with
anything else?
77
Workshop at The Ohio State University, Feb 2010, © Teradata
Impact
•
•
•
•
•
Faster
Higher loyalty
Fewer defections
Wow factor
Opens up other
options –
immediate SMS or
dynamic IVR –
“push Button 1 for
dropped calls this
AM”
Better Decisions in the Contact Center
3 Places Where Insights Can Help
Call
ACD
Monitor
Screen Pop
Queue 1
Agent 1
InHouse
Agent
Agent 2
Agent 3
IVR
Queue 2
At
Home
Agent 1
ACD
Agent 2
Agent 3
And Even
Here?
78
And Here?
Can Analytics Help Here?
Workshop at The Ohio State University, Feb 2010, © Teradata
Off
Shore
Business Impact of Analytics on Agents
Call Centre
Next Best Offer on the Screen
Inbound:
• Doubled sales (+122%) to high potential customers
• > 24% decline in average handling time for low potential
customers - with no negative influence on customer
satisfaction
79
Workshop at The Ohio State University, Feb 2010, © Teradata
Improving the ACD: Better Rules
Smart Call Routing
Queue
Arrivals
Agent 1
Agent 2
Call sent
to Agent
with sales
skill set
Agent 3
High
propensity
to buy caller
Lost calls
• abandon
• busy
Goal: “One and Done”
80
Workshop at The Ohio State University, Feb 2010, © Teradata
Active Enterprise Intelligence at Belgacom
Inbound IVR Call Speedups
Situation
Need to speed calls through the call
center, improve self-service in the IVR by
increasing relevance.
Problem
System not connected to the CC. IVR
programmed for one-size-fits-all.
Solution
Connected AEI to the IVR. Based on
account data, program the IVR to only
offer button push options that correspond
to existing or potential product add-ons.
81
Impact
• Better customer
satisfaction (faster)
• Increased customer
pre-conditioning on
offers
• Increased sales
Workshop at The Ohio State University, Feb 2010, © Teradata
Social Networking – Vodafone Germany
• Vodafone D2 Germany study: Holger Muster, “Evaluating
social network analysis in the telecommunications industry”,
presented at Teradata Partners, October 2008
• Who talks to whom – build social network graph
• Who are the Influencers? Kingpins?
• 3 Key investigations
> When they churn, do the people they talk to churn?
> If they do not churn, do they prevent others from churning?
> What are the impacts of the Influencers on upselling?
• Who to target with your marketing dollars, and why?
> By knowing Influencers, can you spend dollars more wisely?
82
Workshop at The Ohio State University, Feb 2010, © Teradata
Vodafone D2 – Graph Approach
Who Calls Who, Who Influences Who
83
Workshop at The Ohio State University, Feb 2010, © Teradata
Vodafone D2 – Actual Campaign Impact
• Beyond churn, also
expected to see
influence on crosssell and upsell
• Experiment with
“Happy Family”
campaign with
Turks in Berlin
• Top 30% of
influencers had
1.7% take rate
upside impact on
the campaign
84
Workshop at The Ohio State University, Feb 2010, © Teradata
Role Play #2: Columbus Airways
Role Play #2: Airline Scenario
CONTEXT
• You work for a major new airline, Columbus Airways,
which has been in business for 2 years now.
• You work in the Operations group, and are responsible
for handling mis-connecting passengers.
• Word just came in that there is a new Misconnect
Situation at O’Hare. 4 people are trying to get to
Columbus from various parts of the country. There are 2
available seats right now on the next and last flight of
the day on Columbus Air.
• How do we pick the 2 people who get to go to CMH,
and the 2 who stay overnight in Chicago?
• Good news: you have an Enterprise Data Warehouse.
86
Workshop at The Ohio State University, Feb 2010, © Teradata
Passenger Information
87
PASSENGER
Frequent Flyer Routing
Miles / Lifetime
Miles
Sue Burger
4522 / 28,750
SFO -> ORD > CMH
Lynn Johnson
450 / 2,250
ORD -> CMH
Martin
Schneider
0 / 0 - No
information,
1st time flyer
Narita -> SFO
-> ORD ->
CMH
Jill Towne
8400 / 41,500
Frankfurt ->
ORD -> CMH
Round 1: Who goes to CMH?
Workshop at The Ohio State University, Feb 2010, © Teradata
Who Gets the
2 Seats?
More Information From Finance / Marketing
• Lifetime Value Projections
>
>
>
>
Sue - $28,220 (Confidence 72%)
Lynn - $41,250 (21% - low amt of data)
Martin – not scored yet
Jill - $89,000 (82%)
• Profitability Scores
>
>
>
>
88
Sue only books lowest margin flights
Lynn books last minute, pays full fare
Martin – no info
Jill – often upgrades to business class
Workshop at The Ohio State University, Feb 2010, © Teradata
More Information From Web and Marketing
• Booking Behavior
> Sue spends considerable time comparing
fares against our top competitor
> Lynn looks at city pairs online, but only books through the
contact center, never online.
> In last contact center interaction, Lynn inquired about a
family reunion, discounts using Columbus Air for travel for
20 people in August
> Jill uses the web to book
• Competitor Behavior
> Cincinnati Air just announced a direct flight from SFO to
CMH, bypassing Chicago
89
Workshop at The Ohio State University, Feb 2010, © Teradata
More Information from Operations
• Sue is traveling with an infant.
• Bags from SFO did not make the Chicago flight.
• The first flight tomorrow morning at 8am has only 1 free
seat. The second flight at noon has 12 free seats.
• Martin Schneider’s original flight from Narita was
cancelled; he waited 4 hours at Narita for that flight. He
missed his original connection at SFO so we rebooked
him on the next available flight with seats, but he had an
additional 5 hour delay.
• Round 2: What do suggest NOW? Who goes to CMH?
90
Workshop at The Ohio State University, Feb 2010, © Teradata
More Information from Engineering
• (Roll the clock forward, same scenario)
• We have added Internet seatback capabilities
• It will be possible to engage the customers in rebooking
notifications or planning
• Round 3:
What could you do?
91
Workshop at The Ohio State University, Feb 2010, © Teradata
CMH
Role Play #2: Airline Scenario
• Create your rank ordering for handling customers
• Ask people in rank order while they are on planes and
going to the airport whether they want to go to
Columbus. It could be that some people are coming for
business meetings so delays would make a difference in
their wanting to go to CMH at all. Others may have
friends in Chicago so an overnight is not a problem.
Good news: when you ask …
> Lynn Johnson is originating in Chicago and in return for a
free flight coupon, will take a flight anytime the next day
> Jill Towne says she would welcome a hotel room in Chicago
after her flight from Frankfurt, to rest up and will fly out
first thing the next day
Patent Pending!
> Problem solved!
92
Workshop at The Ohio State University, Feb 2010, © Teradata
Integrated Analytical View of Customer
Service Recovery at Continental Airlines
No Centralized Compensation Rules:
Squeaky Wheels
$900
$800
$700
High Value
Low Value
$600
$500
$400
$300
$200
$100
$0
$900
$800
$700
High Value
Low Value
$600
$500
$400
$300
$200
$100
$0
0
93
Enterprise Compensation Rules:
Commensurate to Value
20
40
60
Year 1999: Amount of
Compensation to 100
Sample Customers
0
20
40
Post EDW: Amount of
Compensation to 100
Sample Customers
Workshop at The Ohio State University, Feb 2010, © Teradata
60
Some Takeaways from These Cases
• You can use data to make decisions
• The decision may be complex – with
tradeoffs because of capacity - which
is why you may need to automate the
decisions
• The rules you use will evolve over
time, be refined
• You can measure the consequences of
decisions – good vs. bad
• You can tie decisions to business goals
94
Workshop at The Ohio State University, Feb 2010, © Teradata
Getting Started
Survey
At your company
> Who are your change agents?
Your risk takers?
> How do new initiatives get
started?
> How are they identified?
> How are they justified?
96
Workshop at The Ohio State University, Feb 2010, © Teradata
Anticipate Typical Impediments
What Will You Be Up Against?
Top 5 Areas from Sid Adelman research and my own
interactions with ourCustomers
1. Big Company Syndrome – frontline and backend groups
are organizationally separated, no forums
2. No Strong Business Participation – the IT groups can’t do
this on their own
3. No “Culture” of managing by the numbers, accountability
4. Simple Inertia, No Rock the Boat!
5. No Visionary / Leader
97
Workshop at The Ohio State University, Feb 2010, © Teradata
Set Goals: Tom Davenport’s DELTA Model
Start Simple and Do Things Incrementally
98
Workshop at The Ohio State University, Feb 2010, © Teradata
Pick An Area: Think Like A CEO
Tie Your First Target to Your Strategy
Customers
Sales
Differentiation
Enhance
Revenue
Service
Differentiation
Effectiveness
Revenue
Margins
Costs
Reduce
Costs
The Profit
Wedge
Optimization
Efficiency
Asset
Productivity
Operations
Productivity
Operations
99
Workshop at The Ohio State University, Feb 2010, © Teradata
Tip:
Scoreboards
Can Help
Decision-Making at Southwest Airlines
Balanced Scoreboards, Based on the Numbers
100
Workshop at The Ohio State University, Feb 2010, © Teradata
Continuous Process Improvements
By the Numbers at SWA
Section Of An Airline Strategy
Map
Financial
Objective:
One aspect of
what the strategy
is trying to
achieve
Measure:
How performance
against the objective
is monitored
Target:
The level of
performance
required
Initiative: Projects
or programs
required to reach
the target
Return on Net
Assets
Plane
Utilization
Customer
Attract & Retain
More Customers
Objectives
• Fast Ground
Turnaround
Measures
Targets
• On Ground Time
• On-Time Departure
• 30 Minutes
• 90%
Initiatives
• Six-Sigma
cycle time
reduction
Lowest
Prices
Turnaround time between flights
Internal
Fast Ground
Turnaround
People & Knowledge
Ground
Crew
Alignment
101
Activities
Current
minutes per
step
Lean techniques*
Best practice A.Stricter controls on carry-on bags,
fewer passengers moving back in
minutes per
aisle to find bags
step
Unload passengers
Wait for cleaning crew to board
Clean airplane
Wait for cabin crew to board
Wait for first passenger to board
Load passengers
Wait for passenger info list
Close aircraft door
Detach boarding ramp
5:14
0:24
10:48
4:11
4:06
17:32
1:58
0:57
1:39
4:38
0:18
7:40
0:00
0:00
14:00
0:13
0:09
0:43
Total on-ground cycle time
48:18
29:11
Workshop at The Ohio State University, Feb 2010, © Teradata
B. Cleaning crew in position ahead of
time
C.Standardized workflow, timing and
methods, such as prearranged kits
D.Visual signal from cabin crew to
agent when plane is ready to board
E. Active management of overhead
storage bins by flight crew
F. Passenger information list
delivered by agent following last
passenger on board
G.Agent ready at aircraft to close door
© 2009 Palladium Group, Inc.
SWA Customer Service – From Website
102
Workshop at The Ohio State University, Feb 2010, © Teradata
103
Workshop at The Ohio State University, Feb 2010, © Teradata
How Can You Get on the Right Path?
Where to Start? Rubik’s Cube
Processes
• People
> Who has power?
> Who are the change agents?
> Alignment of Biz and IT?
• Business Processes: “Pain and Gain”
> What’s most painful for customers?
> What’s most painful internally?
> Where are big opportunities to add alerts and real-time
actions?
• Technology
>
>
>
>
104
What’s working well
What’s not
Where can IT help the business?
Where can IT take out cost?
Workshop at The Ohio State University, Feb 2010, © Teradata
Get People Talking
Bridge the Gaps Between Biz and IT
“Activating” begins with MOTIVATION and EDUCATION
PROCESS FOCUS
Biz
Owner
Joint
1st
Meeting
TALKS
DB
Owner
Business
Opportunity
Assessment
B Owners Aware of
Possibilities
DB Aware of
Possibilities
SIs or 3rdParty Tech
Vendors
POC
Full
Use
Technical
Opportunity
Assessment
DATA FOCUS
Will probably require a Cross-Org committee: “Joined-up,
Cross-channel CRM: we now go to meetings with the CIO, CTO
CMO, CRM Managers and Online channel managers in the same room”
105
Workshop at The Ohio State University, Feb 2010, © Teradata
Process
Manage by the Numbers: “Pain and Gain”
GLOBAL TERADATA FINANCIAL SERVICES
CUSTOMER FOCUS GROUP
PAIN: Increasing numbers of customers are opting not to be
contacted by out-bound campaigns
GAIN: Significant opportunities and desire to shift to in-bound
marketing
• There are 5-10X more opportunities to cross-sell and
up-sell within in-bound settings vs out-bound solicitations
• Relationship pricing helps with in-session
communication
PAIN: Channel conflicts and customer drop-off between
channels are serious issues
106
Workshop at The Ohio State University, Feb 2010, © Teradata
Build a Business Case
Find the Value of Aligned Decisions?
STRATEGIC
INTELLIGENCE
Which
suppliers
contribute
the least to
profit?
What’s our
quarterly
outlook?
Active
Enterprise
Intelligence
Vendors
Expedite
overnight
for
10:00 a.m.
delivery?
Is the new
promo
driving
sales this
morning?
OPERATIONAL
107
Target
customers
to acquire?
Retain?
At what cost?
Customers
Do I give
this
customer a
discount?
INTELLIGENCE
Workshop at The Ohio State University, Feb 2010, © Teradata
Technology: Exploit the Data You Have to
Make Better, Faster Decisions
OPERATIONAL INTELLIGENCE
ACTIVATING
MAKE it happen!
Align
OPERATIONALIZING
WHAT IS
happening now?
STRATEGIC INTELLIGENCE
PREDICTING
WHAT WILL
happen?
ANALYZING
WHY
did it happen?
Link to
Operational
Systems
REPORTING
WHAT
happened?
Predictive
Models
Ad Hoc,
BI Tools
Batch
Reports
108
Automated Linkages
Accelerate
Operational Intelligence is the
application of Strategic Intelligence to
operational systems and processes, when it
can make a business impact
Today
Workshop at The Ohio State University, Feb 2010, © Teradata
Typical Project Plan
What Could Your Company Do?
Web Team
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
109
Call Center Team
Create a game plan
Pick a first project
Systems architecture
Extend your data models
Analyze your analytics
Redesign workflows
Check performance
Measure results
Think multi-channel
Retrain people
Workshop at The Ohio State University, Feb 2010, © Teradata
EDW Team
Active Enterprise Intelligence in Action
Front Line “Operational” Users
Customers
Call Center
Logistics
Back Office “Strategic” Users
Suppliers
Executive
Product
Finance
Marketing
Internet / Intranet
Transactional Services
Decision Making Services
ASP / JSP
Service Brokers
Enterprise Message/Service Bus
Event
Notification
POS
Finance
Vendor
ERP
Business
Rules
Event
Detection
Business Process Automation
EDW — ADW
RDBMS Based
Event
Processing
Streaming
Batch
110
Transactional Repositories
Acquisition
Workshop at The OhioData
State University,
Feb 2010, © Teradata
Decision Making Repositories
Feedback Loops - Continuous Improvements
• Treat decision-making as a set of
business processes that need to be
measured … and improved
• Instrument and capture
> Quantity of decisions, rules
> % of decisions made with help of
data
> Quality of decisions (e.g., impact of
alternate treatments)
> ROI of decisions (benefits vs. costs)
> Create your own Value Curves!
> Use interactive channels to do more
Experiments (do more Science!)
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Wrap Up
Sources of Additional Information
Suggested Reading / Listening List
• Teradata Website, Teradata Magazine (online)
>
>
>
>
Case study writeups for many of the customer examples
Active Enterprise Intelligence Overview articles
Active Dashboards (“Think Fast”)
Q2 issue 2010: “Build a better, faster value chain”
• Blogs by James Taylor – anything he does is good
• Wayne Eckerson from TDWI, James Kobielus from
Forrester, Gareth Herschel and Bill Gassman from
Gartner
• Bill Franks (Teradata) Retail article reprint – will be sent
as a followup
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Workshop at The Ohio State University, Feb 2010, © Teradata
Take Action: Read 3 Books
• On the Value of Analytics:
> Thomas H. Davenport and
Jeanne G. Harris,
Competing on Analytics:
The New Science of
Winning, Harvard Business
School Press, 2007
114
• On Applying Analytics
> Thomas H. Davenport,
Jeanne G. Harris and Robert
Morison, Analytics At
Work: Smarter Decisions,
Better Results, HBS Press,
2010
Workshop at The Ohio State University, Feb 2010, © Teradata
Take Action: Read 3 Books
• On Decision Frameworks:
> James Taylor and Neil
Raden, Smart (Enough)
Systems: How to Deliver
Competitive Advantage
by Automating Hidden
Decisions, Prentice-Hall,
2007
Tips
1. “Competing” is good
for your executives
2. “Analytics at Work” is
good for project drivers
3. “Decision Frameworks”
is good for business and
IT to get on the same
page, long term goal
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What’s Your Active Value Curve?
Get Promoted ….
Gains Value
Highlight Success
Pick a 1st Pilot
Do an IMS Study
Hold a Workshop
Discuss with Others
Do the readings
Loses Value
Download and re-read the Slides
116
Attend
the Workshop
Time
And … can you move the
curve to the Left?
Workshop at The Ohio State University, Feb 2010, © Teradata
Questions? Thank You!
Contact info for any followups:
Dr. Dave Schrader
Teradata Product and Services Marketing
Mailstop 14.199
El Segundo, CA USA90245
David.schrader@teradata.com
310.616.2186
Will be back in
Columbus on
April 8th