Marketing effectiveness 2019 Wim Vermeulen and Peter Field Part 1
Focus on Few, Make Big Data 'Big'
1. MO off-site
Focus on Few – Make Big Data ‘Big’
Ahmed Saady Yaamin
29-Jul-2015
Presented @ NG Telecoms Summit Asia, Singapore
2. Bangladesh: a developing country,
a young nation with full of promises
160m
Total population
6%
GDP growth
24 years
Average age of Bangladeshis
8th largest
Population in the world
17 billion USD
international reserve
4. a flexible and generic structure needs to be developed to
convert insights into business outcomes
break down the challenge into
smaller use cases to analyze
get the buy-in from functional
team to proceed further
Implement and monitor fresh
insights to solve problems
Business
outcomes
from insight
identify and prioritize
business challenges
find out ACTIONABLE insights to
solve the business challenge
6. moving from traditional ARPU-AON based segmentation to VLR-MoU
segmentation helps to identify opportunities
1 to 100 100 to 250 250 to 500 >500
1
23 %
Learner
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
37 %
Social Caller
31 %
Gold
7 %
Platinum
22
23
24
25
26
27
28
29
30
Rev 3 %
Rev 13 % Rev 45 % Rev 36 %
Rev 3 %
38% of customers generates
81% of total revenue
62% of customers generates
only 19 % of total revenue, this
leaves a big growth potential
2%
Maverick
Irregular Spike
VLR
MOU
Data points are illustrative purpose only, not actual value
7. churn is caused by customer dissatisfaction - diving into deep
details of complain calls brings fresh insights
84.6% 81.7%
70.8%
77.7%
72.3%
64.8%
44.0%
84.6% 82.6%
1st call resolution – category wise
Special care on retailer and data service
related callers to increase rate of 1st call
resolution
1st call
resolution
2nd call
resolution
3rd call
resolution
4th call
resolution
Total revenue -16% -1% -31% 21%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
%ofRevenuedrop
revenue trend after complain
Every 3rd call resolution results in
a revenue drop of 31%
Data points are illustrative purpose only, not actual value
8. advance analytics can transform call center into a profit center by
reducing churn and by increasing revenue
CP VAS Service
Data Service
Gen Query
Complain
loggers
Campaign/Offe
rs
Voice
Product/Packag
e
Robi VAS
Service
0 1 2 3 4 5 6 7 8
Call Prioritization based on revenue
6%
7%
8%
9%
9%
10%
11%
12%
Robi VAS Service
CP VAS Service
Complain loggers
Retailer/RSP Query
Voice
Product/Package
Campaign/Offers
Gen Query
Data Service
Call category wise churn propensity
Data points are illustrative purpose only, not actual value
9. business doesn’t grow from voice only; data deserves equal
focus and analytics can increase data uptake
0
25
50
75
100
125
0 5 10 15 20 25 30
Traffic per day
of usage (MB)
Frequency of use
(days per month)
Scatter plot of all data users (scaled)
(high usage, high usage days)
High end users (4.92%)
Keep happy with loyalty
offers
Low usage, high usage days
Frequent checkers (6.67%)
increase traffic by
rewarding larger bundles
(low usage, low days)
Low-end users (85.2%)
Aim to increase both traffic
and frequency with free
tryout offers
(High usage, low usage days)
Incidental downloader
(3.19%) : Aim to increase days
of use by rewarding days of usage
Data revenue
increased from
20%-300% in
different micro
segment
Data points are illustrative purpose only, not actual value
