Network AnalysisFor Business Applications
Social network buildingand metrics computing
Onwards analysis uponparticular business events
Presenttime
Customers who
triggered some
business event
What happened withthe correlated customers?
Method to analyze the influence over time
n
Social metrics to describe the influence
Influence 1Considers the
links and nodes’ weights of the
adjacent nodes to n.
Influence 2Consider the links
and nodes’ weights of adjacent nodes to n in addition to the
links’ weights of the nodes adjacent to
adjacent nodes to n.
ClosenessThe average short path to all nodes
connected to node n.
DegreeThe number of
connections incident (in and out) to node n. Betweenness
The number of shortest paths which node n
partakes.
HubHow many
important nodes n points to.
AuthorityHow many
important nodes point to n.
Page RankThe
percentage of possible time
spent by other nodes to node
n.
Social Network AnalysisApplications in Telecommunications
Product Adoption Diffusion
Diffusion process for 3G bundle acquisition
3G bundleacquisition
event
activeresident
ial mobile
customers
2,724
95related
customerswho
bought 3Gafterwards
4 months
1,420related
customers
136randomcustome
rs
3G bundleacquisition
event
2,724
550related
customerswho
bought 3Gafterwards
4 months
3,585related
customers
136influent
ialcustome
rs
activeresident
ial mobile
customers
Diffusion process for 3G bundle acquisition
randomcustome
rs
4261influenti
alcustome
rs
0.7
101
relatedcustomers
influencedcustomers
6.6%
15.3%
132% more effective
479% wider
Diffusion process for 3G bundle acquisition
3G Bundle diffusion comparison
Comparison over six months
From 136 random customers, how do their related connections behave purschasing?
From top 136 influential customers, how do their related connections behave purchasing?
3G Bundle diffusion for 136 random influencers
3G Bundle diffusion for the top 136 top influencers
Adjusting the customer influence factor
Past behaviour of purchasing Future events
(network metrics)Influence Factor
=ƒ
ƒ is a function to adjust the network metrics in relation to the past events.
Computing the customer influence factor
networ
k
metrics
canonical
correlation
between
network
metrics
and the
event of
influence
relation
between
network
metrics for
influencers
and buyers
coeffi cient
of variation
standard
error of
the
mean
range
influenc
e
factor= x x x x
This formula predicts the influential customers for 3G bundle diffusion in
74% of the cases.
customers
whodid not buy 3G bundles
customers who
did buy3G
bundles
Predicted to BUY
customer base
Predicted to NOT BUY
Predicted to BUY
Predicted to NOT BUY
83%
17%
76%
24%
8%
92%
84%
Prediction model for 3G bundle acquisition
Possible approach for marketing and sales campaigns
Cross analysis by using the likelihood of purchasing and the probability of customers’ influence in diffusing 3G
bundles
Most influential customers
for 3G diffusion
Customers most likely to purchase 3G
Targeted customers
for 3G campaigns
CustomerBase
PREDICTIVE MODEL FOR 3G ADOPTION
PREDICTIVE MODEL FOR 3G DIFFUSION
CUSTOMERS MOST LIKELY TO PURCHASE AND TO DIFFUSE 3G
BUNDLES WITHIN THEIR SOCIAL NETWORKS
Social Network AnalysisApplications in Telecommunications
Viral Effect in Portability
M M+1 and M+2 M+3
LEADERS
Customers who have influenced others to port
out.
The leaders’ behavior is
described upon the social network
metrics.
The process to analyze the viral effect in portability
101.389Correlated customers
1-17
6.101Customers ported out
inOctober
419Ported outNovember
64%Mobile
36%Op1
22%Op2
42%Op3
Viral effect in portability
228Ported outDecember
75%Mobile
29%Op1
16%Op2
55%Op3
212Ported out
January
77%Mobile
17%Op1
29%Op2
55%Op3
3.478Pre
2.623Post
57%
43%
2.848
Op1
1.375
Op2
1.878
Op3
47%
22%
31%
47.476
Offnet
47%
6.953
Post
30.575Pre
16.385Fix
7%
30%
16%
677Leade
rs
11%
regularcustome
r
1,28
331leader
customer
0,14
171
correlatedcustomers
customersinfluenced
1,6%
8%
The leader might be up to 5 times more effective
Viral effect in portability
16
9
carrier’smobile
53%
48%
100% Max
99% 95% 90% 75% Q3 50% Median
25% Q1 10% 5% 1% 0% Min
Predictive score for leaders in portability
76%
Hit rate
Misclassification
Predictive model based on decision tree
Customers who had ported out
in the past have influenced
their peers in port out
afterwards
Viral effect in portability154%
92%
43%
1 32
3%
The propensity to port out increases as long as another port outs take place within the communities over the time.
10 32
49%
24%
10%
Viral effect of the portability within communities
OctoberChurn
Predictive Model
Hit rate
86%
Timeframe
Historical behavior Target
Predictive model for portability events
November
December
January
Performance of the artificial neural networks model
96%
59%
91%
Performance of the model during the training process
Predictive model for portability events
25 30 35 40 60 70 80 90 1005 15 20
74%
66%
63%
As
hig
her
the
scor
e as
bet
ter
the
pre
dic
tion
Outcomes from Social Network Analysis in portability
11% of the customers behave as leaders in afterwards portability, affecting
up to 8% of their peers. Up to 21% of the customers who port out are influenced by previous portability.
47% of all port outs goes to Op1. 51% of influenced port out goes to a Op3.
The viral effect of the leaders might be up to 5 times more effective than of
the regular customers. They talk up to 2 times more ion terms of frequency
and up to 3 times more in terms of volume.
The use of social network metrics, including communities topology, has
increased the performance of the predictive model up to 30%.
Previous portability within communities increase the propensity for
afterwards portability up tp 4 times.
Social Network AnalysisApplications in Telecommunications
Fraud Detection in Communities
Viral Effect in Social Networks for Churn and Purchase
Churners
11%Leade
rs
8%Influence
Buyers14%
Leaders
17%Influence
Communities
Previous
churn
3xMore churn
Communities
Previous
purchase
4xMore
purchase
The Viral Effect in Social Networks for Fraud
Fraudsters
0,05%
Leaders
0,1%Influence
Communities
Previous
fraud
0xMore
frauds
Outlier Analysis upon Communities
If a particular node X, within a
community with Y members, has a
Degree-In greater than Z then trigger a
alert...
Outilier Analysis upon the Differences
Difference between each node and its community
Network Metrics Computing
Communities Detection
Nodes & Links
Calls
Outlier Analysis upon Communities
Mean of Degree-In
Mean of Degree-Out
Mean of Hub
Mean of Authority
Mean of Links-In
Mean of Links-Out
Degree-In
Degree-Out
Hub
Authority
Links-In
Links-Out
Outlier Analysis upon Communities
DifDegree-In
DifDegree-Out
DifHub
DifAuthority
DifLinks-In
DifLinks-Out
Outlier Analysis upon Communities
Mean of Degree-In
Mean of Degree-Out
Mean of Hub
Mean of Authority
Mean of Links-In
Mean of Links-Out
Degree-In
Degree-Out
Hub
Authority
Links-In
Links-Out
DifDegree-In
DifDegree-Out
DifHub
DifAuthority
DifLinks-In
DifLinks-Out
Mean of Degree-In
Mean of Degree-Out
Mean of Hub
Mean of Authority
Mean of Links-In
Mean of Links-Out
Degree-In
Degree-Out
Hub
Authority
Links-In
Links-Out
DifDegree-In
DifDegree-Out
DifHub
DifAuthority
DifLinks-In
DifLinks-Out
1
n
Communities
Searching Outliers in Social Network Metrics
Nodes
Degree-In
Degree-Out
Hub
Authority
Links-In
Links-Out
DifDegree-In
DifDegree-Out
DifHub
DifAuthority
DifLinks-In
DifLinks-Out
Outiler Analysi
s
Nodes with unexpected
behavior
Month 1Churn
Predictive Model
Accuracy
78%
Observation Window
Training Behavior Target
Predictive Model to Detect Fraud
Month 2 Month 3 Month 4
86%
83%81%
5%
15%
25%30%
Average accuracy of 78%
Target population
97%
25 40 50 60 70 80 9010 15 20 305 100
10%
20%
77%
91%
54%
Predictive Model to Detect Fraud
Soc
ial N
etw
ork
Met
rics
hav
e
impro
ved the
pre
dic
tive
mod
el in
mor
e th
an 3
0%
Social Network AnalysisApplications in Telecommunications
Community Layering
1,034,675,130 links within the network.
863,229,202 UNDIRECTED links within the network.
84,493,587 nodes within the network.
34% on-net. 73% pre-paid.
2,234,496 communities detected within the network.
38 members in average.
Overall Figures about the network
A set of distinct types of clustering methods was performed, including distances, hierarchy, disjoin, and dimension reduction.
The method that best explained the distribution/variation of the observations against the database was the clustering variable.
This method creates a clustering coeffi cient for each observation, allowing the scoring process and the computation of a cluster’s propensity for each
observation.
36 variables were used on the clustering process, mostly describing the differences within the link and the differences between links.
Basically, two sets of variables were created, one called internal derivative, and the other one called cross weighted.
Classifying the Links
Internal Derivative describes the distribution within the link:
Voice | SMS | MMSEarly Morning | Work Morning | Work Afternoon | Travel | Leisure | Night
Very short | Short | Normal | Long | Very Long | Extreme
voice / ( voice + sms + mms ) * 100
Cross Weighted describes the weight of a particular link upon all links for the pair of nodes:
Voice | SMS | MMSEarly Morning | Work Morning | Work Afternoon | Travel | Leisure | Night
Very short | Short | Normal | Long | Very Long | Extreme
voiceAB / ( ∑voiceA + ∑voiceB ) * 100
Classifying the Links
Additional variables were used to describe the links:
Same Cell | Same Community | Credit Transfer | Both Operator’s customersVoice Duration (Weighted) | Voice Relation Weighted (Duration/Amount)
Variables suggested to be quite relevant are rare:
Same Cell: 6%MMS: 1.15%
Credit Transfer: 0.14%
Even though they were weighted to standardize the observations:
Same Cell: x17MMS: x 87
Credit Transfer: x 741
Classifying the Links
All relationships (undirected links) were clustered, turning into 3 clusters:
FRIENDS28%
FAMILY35%
BUSINESS37%
Clustering Communities upon Links Classification
Main characteristics
FRIENDS28%
2/3 of SMS and 1/3 of Voice
39% during Work time
46% during Travel and Leisure time
15% during Morning and Night time
2/3 Very Short calls
1/3 of Short calls
Clear indication of SMS, Leisure, and Very Short
Propensity to SMS, MMS, Night, and Very Short
FAMILY35%
100% of Voice
35% during Work time
56% during Travel time
9% during Morning and Night time
44% of Normal calls
32% of Long calls
11% of Very Long calls
2% of Extreme calls
Clear indication of Voice, Leisure, and Very Long
Propensity to Voice Duration, Leisure, Long, Very Long, and
Extreme
BUSINESS37%
100% of Voice
3/4 during Work time
16% during Travel and Leisure time
9% during Morning and Night time
1/2 of Normal calls
30% of Short calls
15% of Long calls
Clear indication of Voice, Work Afternoon, and Short
Propensity to Voice, Early Morning, Work Morning, Work Afternoon, Travel, Short, and
Normal
Clustering Communities upon Links Classification
Clusters were also sub-clustered, turning into 12 sub-clusters:
FRIENDS28%
FAMILY35%
BUSINESS37%
MEDIUM30%
SMALL7%
CORPORATE18%
WORKERS26%
PROFESSIONALS18%
COUPLE24%
WEDDED21%
DOMESTIC23%
APART32%
CLASSMATES35%
MATES36%
WORKMATES31%
Sub-Clustering Communities upon Links Classification
FRIENDS 28%
CLASSMATE35%
95% of SMS
2/3 during Work time
25% during Work Morning time
23% during Night time
94 % Very Short calls
Indication of SMS, Night, and Very Short
Propensity to Voice Duration, SMS, MMS, Early Morning,
Work Morning, Work Afternoon, Travel, Night, Very Short, Long, Very Long, and
Extreme
MATE36%
63% of Voice
37% of SMS
69% during Leisure time
18% during Travel time
44% of Very Short calls
43% of Short calls
11% of Normal calls
Indication of Voice, Leisure, and Short
Propensity to Voice and Short
WORKMATE31%
63% of SMS
37% of Voice
52% during Work Afternoon time
27% during Work Morning time
67% of Very Short calls
22% of Short calls
8% of Normalcalls
Indication of SMS, Work Morning and Very Short
Propensity to Relation between Voir Duration and
Amount of Voice and Normal
Sub-Clustering Communities upon Links Classification
FAMILY 35%
COUPLE24%
92% of Voice
8% of SMS
32% Work Afternoon
25% Travel
21% Leisure
12% Night
43% Long
28% Very Long
7% Extreme
Indication of Voice, Travel, Very Long and
Extreme
Propensity to Voice Duration, SMS, MMS,
Very Long and Extreme
WEDDED21%
100% of Voice
37% Travel
26% Leisure
13% Work Afternoon
10% Night
74% Normal
Indication of Voice, Travel and Normal
Propensity to Voice, Early Morning, Work Afternoon, Travel,
Leisure, Night, Very Short, Short and
Normal
DOMESTIC23%
100% of Voice
71% Work Morning
47% Normal
37% Long
Indication of Voice, Work Morning and
Long
Propensity to Work Morning
APART32%
100% of Voice
86% Leisure
48% of Normal
36% of Long
Indication of Voice, Leisure and Long
Sub-Clustering Communities upon Links Classification
BUSINESS 37%MEDIUM
30%
100% Voice
72% Work Afternoon
11% Travel
93% Normal
Indication of Voice, Work
Afternoon and Normal
SMALL
7%
91% Voice
8% SMS
96% Work Afternoon
36% Normal
27% Very Short
24% Short
Indication of Voice, Work
Afternoon and Very Short
Propensity to MMS
CORPORATE
18%
98% Voice
2% SMS
77% Work Afternoon
8% Travel
7% Leisure
60% Normal
23% Long
Indication of Voice, Work
Afternoon and Long
Propensity to Voice Duration, Long Very Long
and Extreme
WORKERS
26%
99% Voice
1% SMS
57% Work Afternoon
11% Travel
10% Leisure
9% Night
76% Short
20% Normal
Indication of Voice,
Workafternoon and Short
Propensity to Travel, Leisure, Night and Short
PROFESSIONALS
18%
98% Voice
2% SMS
47% Work Morning
34% Work Afternoon
7% Travel
57% of Normal
28% of Short
Indication of Voice, Work Morning and
Normal
Propensity to Voice, SMS, Early
Morning, Work Morning, Work
Afternoon, Very Short and Normal
Sub-Clustering Communities upon Links Classification
Communities were classified based on the links distribution
4 FRIENDS 6 FAMILY 11 BUSINESS
BUSINESS
Layering Communities upon Relationships
Communities layers distribution
FRIENDS
38%
FAMILY
21%
BUSINESS
41%
Community Layers
However, communities are shaped by different types of links.
FRIENDS well concentrated on the
38% types of friends links
FAMILY many business links
21% similar behavior in usage
BUSINESS many family links
41% similar behavior in usage
FRIENDS
19%
FAMILY
35%
BUSINESS
46%
FRIENDS
18%
FAMILY
44%
BUSINESS
37%
FRIENDS
52%
FAMILY
23%
BUSINESS
25%
Community Layers
FRIEND’s communities
CLASSMATE
77%
MATE
12%
WORKMATE
11%
Community Layers
Different types of links in layer FRIENDS
CLASSMATE well concentrated on the
77% types of classmates links
MATE many classmates links
12% distributed behavior in usage
WORKMATE concentrated on the types
11% of family links
CLASSMATE
30%
MATE
20%
WORKMATE
50%
CLASSMATE
30%
MATE
47%
WORKMATE
23%
CLASSMATE
59%
MATE
18%
WORKMATE
23%
Community Layers
FAMILY’s communities
COUPLE
16%
WEDDED
26%
DOMESTIC
27%
APART
31%
Community Layers
Different types of links in layer FAMILY
COUPLE well biased by apart
16%
WEDDED biased by apart
26%
DOMESTIC biased by apart
27%COUPLE
18%
WEDDED
21%
DOMESTIC
38%
COUPLE
17%
WEDDED
40%
DOMESTIC
21%
COUPLE
37%
WEDDED
20%
DOMESTIC
21%
APART biased by wedded
31%COUPLE
19%
WEDDED
22%
APART
38%
APART
22%
APART
23%
APART
22%
DOMESTIC
21%
Community Layers
BUSINESS’ communities
MEDIUM
9%
SMALL
1%
CORPORATE
9%
WORKERS
56%
PROFESSIONALS
26%
Community Layers
Different types of links in layer BUSINESS
MEDIUM well workers and
9% professionals bias
SMALL many concentrated
1% on small
CORPORATE workers and
9% professionals bias
MED
18%
SML
7%
CORPORATE
35%
MED
12%
SMALL
46%
CORP
15%
MEDIUM
32%
SML
6%
CORP
19%
WORKERS concentrated on small professionals
56% bias
MED
17%
SML
5%
WORKES
41%
WORKERS
23%
WORKES
15%
WORKES
21%
CORP
14%
PROFESSIONALS small workers
26% of bias
MED
17%
SML
5%
PROFESSIONALS
37%
CORP
17%
PROF
21%
PROF
13%
PROF
20%
PROF
23%
WORKES
24%
Community Layers
Additional information
Network Analysis for Business Applications
SAS Business Knowledge Series
Dr. Carlos Andre Reis PinheiroVisiting Professor, KU Leuven, [email protected]
Lecturer, FGV, BrazilLecturer at Neoma Business School, [email protected]
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