HOW TO: Move from Data Silos to Enterprise-wide Data Analytics

Stefan Spaar & Jim Hayden

The Possibilities are “Unlimited”

● Unlimited, Flat-Rate Mobile

Voice and Data Services

● Simple, All-Inclusive Pricing &

Predictable Bills

● No Contracts & No Long-Term

Service Commitments

● High-Quality Feature Rich

Devices

● Access to High-Quality

Nationwide 3G and 4G LTE

Networks

● Low-Cost Provider

Big Data Paradigm Shift

IT

Structures the data to answer that question

IT

Delivers a platform to enable creative discovery

Business Users

Explores what questions could be asked

Business Users

Determine what question to ask

Monthly sales reports

Profitability analysis

Customer surveys

Brand sentiment

Product strategy

Maximum asset utilization

Big Data Approach

Iterative & Exploratory Analysis

Traditional Approach

Structured & Repeatable Analysis

Adopting Variety, Velocity & Volume

Persistent Data In-Motion Data

Traditional Data

Combination of Non-traditional/ traditional data

Reuse Warehouse Data

Filters incoming data

Real-time

Big Data

Data Warehouse

Variety

Velocity

Volume

Cricket’s Data Evolution

Big Data Analytics Methodology • Create a comprehensive 360o view of customer in order to monetize our data assets. Goal

• Combine multiple Big Data sources to allow for analytics along any dimension. Process

• Incrementally leverage data produced from ROI based initiatives based on value added. Strategy

Big Data – “Goldmine”

• Location Determine the latitude and longitude of your customer at any time..

• Travel Patterns Identify frequent routs that your customer traverses.

• Application Use Distinguish the applications that customers most frequently use.

• Calling Habits Associate call types and call destinations for customers.

• Perceived Service Quality Understand the customer experience with Cricket service.

Customer Behavior

• Music Tastes Characterize customer preferences with music (Muve).

• Browsing Patterns Identify the web sites that customer most frequent.

• Interests Extrapolate customer interests based on search histories.

Customer Preferences

• Social Circles Realize how individuals interact with one another.

• Customer Sentiment Evaluate customer opinions of services or products they purchase.

• Influencers Highlight those individuals that persuade the habits of others.

• Brand Loyalty Determine the brands that our customers choose.

Social Media

TEOCO’s Role at Cricket • What:

Optimize service delivery costs &margin

• Benefits: Cost, time and resource reduction; achieved over 5x ROI

• What: Optimize network availability & performance

• Benefits: Maximize performance, capacity and quality

• What: Optimize RAN network performance

• Benefits: Maximize coverage, capacity and quality

OSS/BSS Solutions

Big Data Customer Analytics Insights

• Who is using what service? • How much is being spent?

• When was last use?

• How often used?

• What are common attributes attributes of customers for behavior X?

• What are the most popular services, devices, plans?

• End-to-end network health

• What elements, services, devices were affected by network errors?

• What services are seeing high error rates?

• What services, devices, customers were affected by network errors?

• What are the most common errors?

• Where did errors happen?

• Where are the heavy use hotspots & deadspots?

• Where is subscriber X, and where has he been?

• Billions of usage recs XDRs -- Data, SMS, MMS, AAA, 2G/3G/4G Data, Music, Roaming, etc.

• Customer info

• Product, service & bundles

• Rate plans

• Market

• Hundreds of millions of events, errors, alarms

• 2G, 3G & 4G network infrastructure from 3 vendors, Muve Music servers, PDSNS, etc.

• Billions of 2G/3G/4G network mobile measurements from RNCs

Data Sources

Usage Analytics Performance Mgmt RAN Optimization

Roaming activity by handset model

Call & Texting Behavior by Age

-

100

200

300

400

500

600

<1

8

18

to 2

4

25

to 3

4

35

to 4

4

45

to 5

4

55

to 6

4

65

to 7

4

>7

5

Average #Texts by Age Band

-

100

200

300

400

500

600

700

<1

8

18

to 2

4

25

to 3

4

35

to 4

4

45

to 5

4

55

to 6

4

65

to 7

4

>7

5

Average #Calls by Age Band

0%

50%

100%

150%

200%

250%

300%

-

100

200

300

400

500

600

700

<18 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65 to 74 >75

Call:Text Ratio by Age Band

Avg SMS Avg Calls Calls/SMS Ratio

Average Cost vs. Detailed Cost

Subscriber Call Quality by Location

Geo-Location: Usage By Age Segment

Future Applications: Subscriber Location Pattern Analysis

Subscriber 1

Subscriber 2

Subscriber 3

Home: location 837

Work: location 482

Classic 9 - 5

Home: location 919

Work: location 1537

night worker

Home: location 275

Work: location 278

non-standard workweek,

multiple jobs

Location Day of Week/Time of Day Summaries

Future Applications: Mobile Advertising geo-temporal

Predict future location of subscriber relative to 3rd party locations

Lessons Learned & Next Steps

• Incremental approach beats Big Bang

• Prioritize use cases based on ROI/perceived value

• Engage departmental sponsors

• Don’t get hung up on technology

• Experiment using Analytics Sandbox

• The value of exploratory analytics is harder to quantify