bsi teradata: the case of the dropped mobile calls

The Case of the Dropped Mobile Calls

04/18/23 (c) BSI Studios, Teradata 2012 1

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


http://www.bsi-teradata.com/

http://www.youtube.com/


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

Cast of Characters


BSI: ITC

Level 3

WILLIEWALLANDER

Willie is an ITC project manager.We made him a “GuestInvestigator” for this case. He has connections within ITC with the marketing campaignmanagement team and the IT groups.

Jon Wold is the Chief Customer Insights Officer at Intergalactic Telephone Corp, responsible for customer satisfaction.


Cast of Characters - BSI

BSI Teradata

Level 2

ZOEYFELICIANO

BSI:

Level 2

JAKE RETSA

BSI Teradata

Level 5

JODICEBLINCO

Zoey is our guru on customer management and is a resident expert on Aprimo. Jake is our hot-shot data scientist and canwork wonders with Teradata Aster on big data sets.

Jodice is our boss, the director of BSI !


Scene 1: The Problem

– 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 !

• 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 …

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


Scene 2: At ITC HQ



ITC and BSI People at Project Kickoff Meeting

Willie

WallanderITC

Project

Lead

Jon

WoldITCVP –

Customer

Insights

Zoey

FelicianoBSI

Campaign

Mgmt Guru

Jake

RetsaBSI Data

Scientist


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


Northeast Region Dropped Calls

Jon Used Tableau ToCreate 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



Dropped Call with Financial Impacts


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

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



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


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


Boston Call Connection Graph

Can zoom in onjust a test sample – 3000 out of millions of customers

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



Sample Connection Graph

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).


http://www.gephi.com/

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


04/18/23 (c) BSI Studios, Teradata 2012

Some Customers (Red Nodes) Have Dropped Call Problems

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


Not All Customers Are Equal


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


Adding the Influencers (blue nodes)



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?


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 !

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!)


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



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):

Real-Time Monitoring and Actions

The thought is that if ITC can build a system that can detect dropped calls quickly (thegoal was within 10 minutes) then react withapologies and other inducements to stay with ITC, ITC might be able to turn around customerdefections and buy time to install towers

There are different kinds of campaigns, and eachof these workflows can be built using Aprimo

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


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


Sample Aprimo Campaign Flows


The RunTime Approach

Willie explains the three-step process for putting everything together

1. 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

2. 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.

3.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.



HotterData

Teradata Hybrid Storage

Putting It All TogetherWillie Explains How It Works

1. ITC Operationssends dropped call recordsto the Teradata system

Telco Operations

Stream of DroppedCallRecords

Colder Data: Billing History, Ops Data, Complaints

Dropped Call!


HotterData


Willie Explains How It Works


2. Dropped calls are matched to a previously computed and loaded Watch List from Teradata Aster

DefectorWatch List

Big Data Call Graphof High Value Customers

and Influencers


HotterData


Willie Explains How It Works


3: Aprimo decides whatCampaign, if any, to run

• Instant Apology• Free SW Fix• Discounts on Bill• Offer a Femtocell Micro-Booster


HotterData


Putting It All Together

1. ITC Operationssends dropped call recordsto the Teradata system

Telco Operations

Stream of DroppedCallRecords


Dropped Call!

3: Aprimo decides whatCampaign, if any, to run

• Instant Apology• Free SW Fix• Discounts on Bill• Offer a Femtocell Micro-Booster

2. Dropped calls are matched to a previously computed and loaded Watch List from Teradata Aster

DefectorWatch List

Big Data Call Graphof High Value Customers

and Influencers

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> Used most frequently> Very recent data > Last few seconds, minutes, days,

weeks> E.g., At Risk Customers (Watch List of

Numbers) > E.g., Call Detail – Dropped Calls

• ≈75% of data is warm/cold> Accessed infrequently> History – months ago> Deep detailed info> E.g., all history of dropped calls, so

we can do a comprehensive analysis of where new towers should go

Data

Usa

ge T

em

pera

ture

System Data Space

SSD

HDD

Typical Data WarehouseData Usage Pattern

Needed for this project!

04/18/23 (c) BSI:Teradata Studios, Teradata 2012 41

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

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



Aprimo Marketing Dashboard Campaign Cost Rollups

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:



Teradata View: What’s Really Happening:•5 “fast-busy” attempts/day•Drops 2 calls per day during commute •5 failed dialing attempts due to weak signals •5 failed handovers onto partner’s network •2 failed game download attempts

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: May falsely conclude: Customer is happy and low churn risk … but Experience by Location

RF QoS Experience

Roaming Experience

Content/Service Analytics

Handset Analytics


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.

Jon’s Goal: Customer Experience Management Architecture

Global Correlation – ELT and ETLGlobal Correlation – ELT and ETL

DataSources

DataSources Network/OSSNetwork/OSS CVM/BSSCVM/BSS

DataCollection

Layer

DataCollection

Layer

IntelligenceLayer

IntelligenceLayer

ProbesProbes ApplicationsApplications DPIDPI CDR,XDRCDR,XDREvents Events CRMCRM Self-CareSelf-Care DevicesDevices

OnlineOnline

ApplicationChannel

Layer

ApplicationChannel

LayerKPIKPI TrendingTrending AlarmsAlarmsDashboardDashboard Ad-hocAd-hoc PricingPricingModellingModelling CampaignsCampaigns

TeradataActive Data Warehouse

Web DealerSalesCall

CenterRetail

Active Enterprise Integration

Active EventsActive Access

Workflow and Applications

AsterData lab Aprimo

DPI = Deep Packet Inspection, CDR = Call Detail Record, XDR – any kind of Detail RecordOSS = Operational Support Systems (Network), BSS = Business Support Systems (Billing)

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


bsi teradata: the case of the dropped mobile calls

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