The BSI team helps Intergalactic Telephone Corp., whose customers are experiencing dropped mobile phone calls. Using Teradata Aster, Teradata Hybrid Storage, Aprimo and Tableau, the team develops some novel approaches to analyzing call detail records to identify the top potential defectors and to improve customer satisfaction with a powerful combination of real-time apolgies, credits on bills, software upgrades, and free femtocell boosters for selected customers. Watch the video at http://bit.ly/zDfmJH.

1. The Case of the Dropped Mobile Calls
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2. Context
• This is the “How We Did It” deck that accompanies the
“Case of the Dropped Mobile Calls” webisode, available at
www.bsi-teradata.com or on www.YouTube.com
(keywords “BSI Teradata”)
• The goal is to explain details of what you saw in the
episode and provide more technical background on how
the technologies shown in the episode work.
• We hope you liked the episode!
- Zoey and Jake
Business Scenario Investigators
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3. BSI Story Synopsis:
‘The Case of the Dropped Mobile Calls’
• Customer churn is a problem for telcos
– Especially when caused by poor network experience. Underlying issues:
lack of capacity, coverage geo-holes, handset and software issues
• Focus of story: Users with bad experiences churn - and influence people in
their calling network to churn, too.
• How BSI solved the case:
– Business analysis: Analyzed calling networks, identified high-value
customers and influencers with dropped calls, acted quickly to turn around
the potential defectors. Developed and deployed various campaign options:
• Fast apologies of various types/formats
• Discounts
• Software upgrades for people with older phones
• Femtocell boosters for high value customers or influencers with
problems in fixed locations.
• Towers in the longer-term fix the problem for customers
– Tech: used Teradata Aster for network analytics to detect call graphs and
influencers, used Teradata Hybrid Storage to get on top of dropped call
data quickly, used Aprimo for launch save campaigns
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4. Cast of Characters
Jon Wold is the Chief Customer Insights
Officer at Intergalactic Telephone Corp,
responsible for customer satisfaction.
Willie is an ITC project manager. BSI: ITC
We made him a “Guest
Investigator” for this case. WILLIE
He has connections within ITC WALLANDER
with the marketing campaign
Level 3
management team and the
IT groups.
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5. Cast of Characters - BSI
BSI Teradata BSI:
ZOEY JAKE
FELICIANO RETSA
Level 2 Level 2
Zoey is our guru on customer management
and is a resident expert on Aprimo. BSI Teradata
Jake is our hot-shot data scientist and can JODICE
work wonders with Teradata Aster on big BLINCO
data sets.
Level 5
Jodice is our boss, the director of BSI !
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6. Scene 1: The Problem
• Nancy Johnson and Barb Griesser are
talking about their experience with
Intergalactic Telephone Company (ITC)
– it’s not good
• They bought new Smartphones a month
ago, talked friends into buying, too
• Now they’re comparing notes …
– Nancy’s phone works fine at home, but drops once a week while on the
go at the gym or mall
– But Barb (on the right) is very unhappy with ITC, static on line, lots of
dropped calls to her husband and sister
– While they’re chatting, Nancy gets a phone call from her mom – and then
the line drops
– Barb tries to talk Nancy into cancelling service, switching back to their
previous carrier. Thinks they should break the contract without any fees
because of bad service – if ITC refuses, they’ll go on social media, tell
the world !
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7. The Problem
• NOT ALL CUSTOMERS ARE EQUAL is a key point in
this episode
• In this case Nancy might be a high value customer
with lots of phone services for her extended family, but
not that unhappy with ITC
• But Barb is an influencer when it comes to technology
choices and churn decisions – she isn’t as high value
as Nancy to ITC but she was the one that researched
which phone models to buy and can talk her friends
into upgrades and dropping service – as she’s doing
now
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8. Scene 2: At ITC HQ
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9. ITC and BSI People at Project Kickoff Meeting
Willie Zoey
Wallander Feliciano
ITC BSI
Project Campaign
Lead Mgmt Guru
Jon
Jake
Wold
Retsa
ITC
BSI Data
VP –
Customer Scientist
Insights
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10. Scene 2: Project Launched at ITC to Investigate
• Meanwhile, at Corporate HQ, the VP of Customer Insight Jon Wold
can see the customer KPIs for the new phone rollout going south. Big
uptick in calls to the care center with complaints and defections –
company reputation is suffering
• He launches a special project to investigate, led by ITC’s Willie
Wallander… with the help of BSI investigators
– Jake Retsa -- deep data insights expert
– Zoey Feliciano – an expert in using real-time data to launch
turnaround marketing and service campaigns
• Jon shows the team the latest dropped call numbers. He used
Tableau to build these screens and visuals about complaints
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11. Northeast Region Dropped Calls
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12. Jon Used Tableau To
Create Dashboard Displays
• Accessed Teradata system to pull up dropped call
information
• Can be locations of dropped calls or locations of
customer complaints to the contact centers – these
are overlaid on a map
• More calls => bigger nodes
• Then added Sales and Profit data from billing as well
as comparisons to Intergalactic Telephone
Corporation’s other regions
• Put multiple reports on one Tableau screen
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13. Dropped Call with Financial Impacts
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14. Scene 2: Project Launched at ITC to Investigate
• Jon asks Willie to lead a project to investigate the root causes and come up
with some short and long-term fixes. Clearly, more towers are the long-
term fix, but that takes time
• They brainstorm on problems and best fixes
– Technical fixes? more towers, maybe phone upgrades? Femtocells?
Better tower signalling antenna alignments
– Marketing/Sales fixes, reactions? – apologies, bill reductions?
– Overall optimization of $$ to spend to fix? Which towers need to go
first? What’s the minimum number of towers that will give ITC the
biggest short-term payoff?
• Zoey and Jake agree to work onsite at ITC until the problem’s fixed
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15. Willie’s Game Plan
• Jon and Jake, the BSI Data Scientist, will
explore causative factors for dropped service
• Willie and Zoey, the Campaign Management
Expert, will brainstorm offers for customers
• Create campaigns for potential defectors
with inducements to stick with ITC
• Will use some new capabilities: Teradata
Aster for big data analytics, Teradata Hybrid
Storage, and Aprimo Event-Driven
Campaigns
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16. Scene 3: One week later – INSIGHTS
Jake and Jon used Teradata Aster to find
some customer call/influencer insights
AND
Willie and Zoey have a game plan for how
they’re going to use Teradata insights to
launch Aprimo-based save campaigns for
customers
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17. Scene 3: Jake and Jon Study
Dropped Call and Customer Data
OVERVIEW – Loaded some sample data into Teradata Aster from Boston
•Built a call graph of in-network customers
•Looked at pairs - find who calls whom
•Can also look at who accesses what Web sites, what kinds of calls
happened (not just voice – can be SMS, MMS, gaming)
•Nodes in the graph represent callers or Web sites
•Arcs between nodes represent calls or data accesses
•Can color code nodes and arcs
•Arcs are black if calls went through OK
•Arcs are red if the call was dropped
•Arc width gets “fatter” based on the count of the number of calls (not
shown)
•Node color can represent (red) customers with significant # of dropped
calls
•Node size can get bigger based on customer value
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18. Boston Call Connection Graph
Can zoom in on
just a test sample –
3000 out of
millions of customers
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19. Jake loaded Teradata Aster with raw call details
• The calls come from ITC’s operational system and were ETL’d into
Teradata Aster
• Customer data on value was pulled into Teradata Aster, where it’s
regularly computed based on phone bills and service plan
information
• Jake used the Teradata Communications Industry Logical Data
Model to accelerate modeling of the calls as well as customers
• He screened out the non-dropped calls so he could focus in on just
the dropped calls
• He then focused on annotating the graph with customers
experiencing problems
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20. Sample Connection Graph
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21. To Show Visualizations, Jake Used Gephi
• Gephi is an open-source visualization tool, downloadable at
www.gephi.com
• There’s a User Guide about how to use Gephi at that Web site, and
some sample data sets
• Data for Gephi input is tabular, so easy to set up and use
• Note that the Call Graph (who calls whom) isn’t the same as the
Dropped Call Location Graph (shows calls on a map) – this episode
shows you the Call Graph at first, and then later (when we worked
on tower placement) shows the calls on a map. We used the
“Force” function to drive the overall node layouts with some “gravity”
settings to pull nodes closer to each other if they have high
interconnectivity (e.g., two people make lots of calls to each other).
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22. Gephi Can Highlight Dropped Calls
• If the call was dropped, we can turn the arrow red A->B (not shown)
• If a sufficient portion of the arcs turn red, then we turn the nodes
red, illustrating customers (and Web sites) with access problems.
This is done with color-code controls on the weights of the arcs, and
weights on the nodes. We could have gotten even fancier (e.g.,
used color gradients, not just black or red), but Jake didn’t want to
show off too much!
• We didn’t show it, but after doing this analysis, we could’ve then
used Tableau to put the weighted customer nodes back on a map to
see concentrations of “bad call areas” – which can also help drive
the decisions about locations for new towers
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23. Some Customers (Red Nodes) Have
Dropped Call Problems
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24. Gephi Can Show Impacted Customers
• Next, Jake computes “high value customers”
• A high value customer’s behavior includes
– ARPU (Average Revenue Per User) above a certain
THRESHOLD, or
– Average ARPU over the past 6 months has been increasing at a
rate above a GROWTH-THRESHOLD (Jake used 20%), or
– Is on the TARGET list for a current growth marketing campaign
• All this information was loaded into Aster from Teradata and/or
Aprimo
• Jake made those nodes proportionally larger
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25. Not All Customers Are Equal
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26. Gephi Can Show Influencers
• Next, Jake computes “influencers”
• An influencer is a customer (not a Web site) whose behavior
includes
– Dropped calls (was red) AND
– Bought a service or upgrade AND
– Someone in their calling network subsequently (later in time)
also bought the same service or upgrade
– Jake wrote a Teradata Aster procedure to compute this easily
• Jake used Gephi to turn those nodes blue
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27. Adding the Influencers (blue nodes)
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28. Summary of Analysis with Teradata Aster
• Data Sources for Deep Customer Insights
– Operations Data – loaded into Teradata Aster from Ops
• Voice and data
• Satisfactory and dropped calls
– Customer Data – loaded into Aster from Teradata and Third Party
• Customer value data – Lifetime Value, ARPU Per Month
• Social media links (LinkedIn and Facebook connectivity) – not shown
• Calculations on Teradata Aster
– Connection networks – who calls whom, who accesses what
– Geospatial information – where drops occur on a map
– Customer watch list information based on value and influence
• High value customers AND
• Influence scores for handset and service purchase
• Influence on churn
• Resulting Insights
– Who should be on our Watch List?
– Where to install new towers to get most payoff?
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29. Scaleup Study: Jake Used Teradata Aster
• Studied 8M customers. 7B service calls
analyzed from last 3 weeks
• Found 1M clusters of callers
• Found 120K “Dropped Call Watch List”
clusters, 40K Influencers
• Found 4000 Watch List customers who
already cancelled service and can influence
others; took along an additional 18K
customers when they cancelled
• Net impact: $28M in lost annual revenue
• This is real … and scary !
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30. The Watch List
• Jake adds Influencers to the
High Value Customer List to
create the list of phone
numbers on the Watch List
• This file is loaded into
Teradata system
• RED NODE = Customer,
BLUE = Influencer,
• (BLACK = Web site, not
loaded, but should be
watched, too!)
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31. New Tower Installation Map
Jake and Jon also used Teradata to
decide where to install new towers.
This uses Teradata’s geospatial
capabilities, coupled with Tableau’s
mapping capabilities
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32. Real-Time Monitoring and Actions
Zoey and Willie focus on improving (reducing) detection time for individual
dropped calls and take immediate preventative and remedial actions
Key idea: use Teradata (Hybrid) to closely watch the riskiest defector groups
from Jake/Jon. Stream operational call data into the highest (fastest) level of
storage, constantly run comparisons of dropped calls to the watch list. Use this
to then feed the retention campaigns (handled by Aprimo):
The thought is that if ITC can build a system
that can detect dropped calls quickly (the
goal was within 10 minutes) then react with
apologies and other inducements to stay with
ITC, ITC might be able to turn around customer
defections and buy time to install towers
There are different kinds of campaigns, and each
of these workflows can be built using Aprimo
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33. Campaign Types
Retention campaigns for customers experiencing dropped calls can
include these elements
•Immediate SMS, email, letter, and outbound care center apologies
•Credits on bills
•Free software upgrades
•Free or low-cost micro-booster offers for cases where people are
calling from fixed locations, like home or office
•For inbound calls: Updated maps for the call center agents so they’re
smart on areas people should avoid
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34. Architecture for Customer Management
• Last year ITC “bought into” the idea of becoming customer-focused
• As part of that, they bought the Aprimo Relationship Manager (RM)
software for running campaigns
• RM was used to drive consistent customer touches and monitor
campaign results
• The tool includes features like:
– Suppression so customers are not over-touched. In this case,
that ensures that customers receive the appropriate number of
apologies – for example, ITC doesn’t send an apology for every
dropped call; people who have 10 dropped calls per day get just
one apology that day. If the problem persists, they might get a
personal outbound call a few days later
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35. Sample Aprimo Campaign Flows
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36. The RunTime Approach
Willie explains the three-step process for putting
everything together
2. As dropped calls happen, ITC’s Operations
system collects signal data. It is then ETL’d using
trickle and mini-batch technology into Teradata
system
3. Willie worked with IT to use Teradata “hybrid
storage,” a.k.a. “multi-temperature” storage that
uses Solid State Disks for really fast access for “hot”
data. The Aster Watch List is loaded daily into “hot”
storage, and an algorithm matches the customer ID
on the dropped service request to the watch list IDs.
4.If there’s a match the record is sent to Aprimo for
processing. Which campaign to run, if any, is the
final step and depends on triggering the right Aprimo
workflow.
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37. Putting It All Together
Willie Explains How It Works
1. ITC Operations
sends dropped call records
to the Teradata system
Stream of
Dropped
Call
Records
Telco Hotter
Operations Data
Colder Data:
Billing History,
Ops Data,
Complaints
Dropped Call! Teradata Hybrid Storage
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38. Willie Explains How It Works
2. Dropped calls are matched to a
previously computed and loaded
Watch List from Teradata Aster
Defector
Watch List
Big Data Call Graph
of High Value Customers
Hotter and Influencers
Data
Colder Data:
Billing History,
Ops Data,
Complaints
Teradata Hybrid Storage
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39. Willie Explains How It Works
Hotter
Data 3: Aprimo decides what
Campaign, if any, to run
Colder Data: • Instant Apology
Billing History, • Free SW Fix
Ops Data, • Discounts on Bill
Complaints • Offer a Femtocell
Micro-Booster
Teradata Hybrid Storage
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40. Putting It All Together
2. Dropped calls are matched to a
previously computed and loaded
1. ITC Operations Watch List from Teradata Aster
sends dropped call records
to the Teradata system
Stream of Defector
Dropped Watch List
Call Big Data Call Graph
Records of High Value Customers
and Influencers
Telco Hotter
Operations Data 3: Aprimo decides what
Campaign, if any, to run
Colder Data: • Instant Apology
Billing History, • Free SW Fix
Ops Data, • Discounts on Bill
Complaints • Offer a Femtocell
Micro-Booster
Dropped Call! Teradata Hybrid Storage
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41. Willie Used Teradata’s Hybrid Storage
Dropped Calls are Hot – so placed in SSD, along with the
At Risk Customer Watch List
• ≈25% of EDW data is hot
Needed for this project!
> Used most frequently
Data Usage Temperature
> Very recent data
Typical Data Warehouse > Last few seconds, minutes, days,
SSD Data Usage Pattern weeks
> E.g., At Risk Customers (Watch List
of Numbers)
> E.g., Call Detail – Dropped Calls
HDD • ≈75% of data is warm/cold
> Accessed infrequently
> History – months ago
> Deep detailed info
System Data Space
> E.g., all history of dropped calls, so
we can do a comprehensive analysis
of where new towers should go
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42. Four Weeks Later, The System’s Up and Running
• The team put the system
together and started
measuring results
• Different customers get
different responses
• We measured how many of
each campaign type ran
and whether or not
customer responded
• We can also compute costs
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43. Architecture for Customer Management
• ITC also bought Aprimo Marketing Suite, used to
“manage” marketing. In this case, the costs for the
various campaign elements are also measured and
monitored
• A key cost item for the Save campaigns was the
Femtocell ($200 each including shipping). ITC used
Aprimo to ensure that they didn’t run out of these
• For campaigns like software upgrades or femtocells,
they could monitor how many were shipped, how many
offers were accepted (measuring downloads of software
or activations), so they have good dashboard information
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44. Aprimo Marketing Dashboard
Campaign Cost Rollups
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45. Overall, Jon Wold is Happy with the Our Work
• This case showed only some of
what can be done by analyzing
detailed data, using Teradata
Aster as well as Aprimo
• Since Jon is the VP of Customer
Insights, he’s eager to get even
more information into his
Teradata systems so he can see
the entire Customer Experience
• We give him some more ideas,
not in the episode. This problem
is much like an iceberg:
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46. Bottom Line with Teradata – We Know More
Customer info an operator knows today:
• Samsung handset is 3 months old.
• Pays monthly bills on-time.
• Calls to CARE 3X per year.
• Visited the retail store.
• Uses voice mail and SMS.
• Switch shows 2 dropped calls/day.
May falsely conclude: Customer is
happy and low churn risk … but Experience by Location
RF QoS Experience
Teradata View: What’s Really Happening: Roaming Experience
•5 “fast-busy” attempts/day
•Drops 2 calls per day during commute Content/Service Analytics
•5 failed dialing attempts due to weak signals
Handset Analytics
•5 failed handovers onto partner’s network
•2 failed game download attempts
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47. Team Comes Up with Many More Ideas for Analytics Using
Teradata Aster: Social Network Analysis (SNA)
• Who’s responsible for influencing heavy users and valuable customers?
• How should we retail them with our services if they’re profitable customers?
• How does their behavior affect others in their social network?
• Does that network extend to non-ITC networks?
• Can we attract those extended network members onto our network?
Actions
• Focus launch of next set of new handsets on Key Purchaser influencers in
the customer base – reuse. Rerun this analytic for customers with old
handsets.
• Monitor feedback at call center, emails, online and via user groups, plus POS
• Use viral campaigns targeted at key influencers to
– Trade out phones and retain new high value subscribers
– Extend existing customer contracts
Outcome
• Retention rates improve, new campaigns improve, can also grow share of
wallet of new customers.
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48. Jon’s Goal:
Customer Experience Management Architecture
Call
Application Web Retail Sales Dealer
Center
Dashboard Campaigns
Channel KPI Trending Alarms Ad-hoc Modelling Pricing
Layer
Workflow and Applications
Active Access Active Events
Active Enterprise Integration
Intelligence
Teradata Aster
Layer Aprimo
Active Data Warehouse Data lab
Global Correlation – ELT and ETL
Data
Collection Probes Events Applications DPI CDR,XDR CRM Self-Care Devices
Layer
Data
Sources Network/OSS Online CVM/BSS
DPI = Deep Packet Inspection, CDR = Call Detail Record, XDR – any kind of Detail Record
OSS = Operational Support Systems (Network), BSS = Business Support Systems (Billing)

49. Thanks for Watching!
• You can find more episodes at www.bsi-teradata.com
• Episodes are also posted to YouTube, search keywords “BSI
Teradata”
• If you’re in the telecommunications industry, you may enjoy
the “Case of the Defecting Telco Customers”
• For more product information, see:
www.teradata.com,
www.tableau.com,
www.asterdata.com and
www.aprimo.com
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