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The Challenge of Inactive Customers:
Using Data Analytics to Understand and
Tackle Low Customer Activity



Claudia McKay
Toru Mino
Paola de Baldomero Zazo                  February 2012
While the number of branchless banking services has grown rapidly, the
vast majority of registered customers are not actively transacting


         Most branchless banking providers struggle with high rates of inactive customers
  140

                    120
  120


  100


   80                                                                                                While 120 branchless
                                                                                                     banking
   60                                                                                                implementations have
                                                    1 in 6
                                                                                                     been launched since
                                                                                 1 in 11             2007 only 11 of those
   40

                                                    21                                               have reached 200k
   20                                                                             11                 active users

    0
        Implementations launched after Implementations with >1 million "Confirmed" implementations
                    2007                        registered               with >200k active users




 In a CGAP survey, 64% of managers said less than 30% of their registered customers were
 active, and active rates of less than 10% are not uncommon

                                                                                                                             2
               Source: CGAP and Coffey International, data as of Q1 2012.
It is challenging to develop a viable business model with low activity rates


Low activity rates sharply increase the cost a                 If acquisition costs per active customer are this
provider must invest to acquire each ACTIVE                    high, even in a best case revenue per customer
customer                                                       scenario it could take 10 years to breakeven
  CGAP estimates most services spend between USD 2-5 to
  register each customer. With 5% activity rate and a $5 per      Assuming M-PESA Kenya revenue per customer as a “best
  customer acquisition cost, a service must invest $100 to        case”, with an activity rate of just 5%, a service would take
  acquire each active customer                                    10 years to break even on the customer acquisition cost

        Acquisition Cost per Active Customer                                 Time to Break-even per Active Customer
$120                                                                    12

$100                                                                    10

 $80                                                                     8




                                                                years
 $60                                                                     6

 $40                                                                     4

 $20                                                                     2

  $0                                                                     0
          50% Active         15% Active          5% Active                      50% Active     15% Active          5% Active



With current activity rates, most branchless banking businesses cannot generate enough revenue
per customer to remain viable

                                                                                                                                  3
CGAP’s work on Inactive Customers


•   Moving a potential customer from awareness of a branchless banking service to
    regular use of the service requires different levers all to be working effectively and in
    an integrated manner: the agent network, product features, marketing, customer
    service, user experience and system/network. CGAP has developed a framework to
    map this process.

•   CGAP and others have produced publicly available materials on several of these
    critical issues such as agent networks and marketing.

•   One of the missing gaps in tackling this challenge is helping providers understand
    their customers and to identify the critical issues to resolve in their own services.

•   Ultimately, understanding customers is a complex undertaking and will rely heavily on
    a variety of tools such as focus groups and surveys. However, the first step for every
    provider should be to conduct data analysis on the veritable gold mine already at
    hand - its own database. CGAP has worked with four providers to understand how
    basic transaction level data can be used to shed light on customer activity.



                                                                                                4
Who should use this deck




                                                 Funders & Other Supporting
     Branchless Banking Providers
                                                       Organizations


This deck is primarily targeted towards    Funders and other supporting
branchless banking providers and aims to   organizations in the financial
help them use their data to better         inclusion/branchless banking space may
understand their customers and the         also benefit from this deck’s attempt to
reasons for low activity levels in their   identify the types of indicators and analysis
services.                                  that shed light on customer usage and
                                           value. This may help funders standardize
                                           performance metrics across multiple
                                           organizations.




                                                                                           5
How to use this deck




This deck does not provide answers on how to improve activity at any specific provider. Instead we
identify: (1) which data providers should collect about their customers and services, (2) what types of
analysis providers can conduct based on the data collected, and (3) what kind of follow-up actions
providers can take to further understand causes of customer inactivity in their own service.

 1. Collect                          2. Analyze                         3. Act
  This deck helps providers           This deck also identifies ways     This deck does not provide
  understand what data they           of analyzing data about            specific answers to improve
  should be collecting and which      branchless banking services        activity as we found that the
  indicators they should be           that we found gave the clearest    influence of indicators on
  tracking. CGAP studied              picture of how services are        activity are not automatically
  hundreds of different variables     being used and which factors       transferrable across markets.
  and only included those             are affecting activity rates.      Instead we spotlight interesting
  variables that had statistically                                       customer segments and
  significant impacts on activity                                        behavior patterns that can be
  levels in this deck.                                                   followed up by providers
                                                                         through qualitative research
                                                                         and interaction with customers.




                                                                                                            6
Research introduction: quantitative analysis of data from 4 providers
across 3 regions


Geography:
                                                                        2 South Asian

                        1 African




                                                                                   1 Southeast Asian
   Total customers: ~3 million



Provider types:             1 Bank-led                   2 MNO-led                   1 3rd Party-led

 • All services offer a mobile wallet focused on P2P transfers and bill payments


Selection Criteria:
 • In the market at least 18 months so we could track trends over time
 • Perceive low customer activity as a challenge (though we did not deliberately seek out providers with
   abnormally low activity rates)

                                                                                                           7
Research introduction: methodology of analysis


              Data utilized     Hypotheses guiding our         Types of quantitative
                                       analysis                 analysis conducted


                                Demographic segments             Analysis of activity
               Customer
                                 influence activity rates              trends
            registrations and
             demographics
                                                                 Regression analysis
                                                               (probability of activity as
                                Usage pattern segments           dependent variable)
                                  help explain activity
Providers




               Customer                                       Cross analysis of usage
            transactions (at                                  pattern and demographic
            least 6 months)                                           segments
                                Agents affect activity rate
                                of customers they register
                                                                 Analysis of Money
                                                                  transfer patterns
                  Agent
            registrations and   Service usage in the 1st         Analysis of agent
             demographics        month affects ongoing           demographics and
                                        activity                   registrations

                                                                                             8
Outline of Deck




1. General Activity
                        Slides 11 – 15: Analysis of overall activity rate trends
   Trends



2. Customer segments    Slides 16 – 22: Interesting findings about customer segments and
   by usage patterns    usage of services




3. Factors Affecting    Slides 23 - 29: Factors which were found to have a statistically
   Activity             significant influence on activity rates



                        Slides 30 – 34: Based on our analysis across these 4 example
4. Takeaways & Action   providers we can recommend certain indicators and analysis that
   Items                providers should be tracking to help them understand and tackle
                        the problem of low customer activity


                                                                                           9
Main Findings on Customer Activity



1. General       Activity rates are low across all providers: average activity rate was only 8% and
   Activity      54% of all registered customers had never tried the service. We defined activity
   Trends        as at least 1 transaction in the last 90 days.


                 Each service has super-users responsible for a out-size proportion of total
                 transactions. On average, 5% of users were responsible for 30% of total value
2. Customer      transacted. Providers should study these users to better understand the value
                 proposition of their service.
   segments by
   usage
   patterns      48% of transfers happened locally within a municipality or province, indicating
                 that P2P transfer patterns do not always follow the “send money home” pattern
                 that was seen in M-PESA Kenya.


                 The activity rate of customers registered by the best agents was over 40 times
                 higher than those registered by the worst agents, so providers must learn from
                 the best agents and intervene with the worst.
3. Factors
   Affecting
   Activity      Customer usage of a service in their first month after registration largely
                 dictates their future activity, so providers should focus efforts on getting
                 customers to not just transact in the first month, but to do specific types of
                 transactions.

                                                                                                   10
Main Findings: General Activity Trends




1. General           Average activity rate across providers was just 8%, and activity rates have
   Activity Trends   not increased much even as registrations accelerate



2. Customer
                     80% of customers who are active in their first month after registration have
  segments by
                     stopped transacting by their 4th month, so new subscriber growth masks a
  usage
                     real drop in activity rates over time
  patterns


3. Factors
                     To more accurately track activity rate trends over time we recommend using
   Affecting
   Activity
                     vintage analysis




4. Takeaways &
   Action Items




                                                     1.trends   2.usage    3.activity   4.takeaway   11
Activity rates are low across all 4 providers and have not grown along
  with customer registrations



Only 8% of registered customers were active                                Cumulatively, activity rates have remained
across the 4 providers we studied                                          relatively flat while registrations have grown
                                                                           rapidly*
     8%                                                                1,000                                                             50%
                        Only 8% of registered                           900                                                              45%
                        customers were active
                                                                        800                                                              40%
                        [range: 1% to 18%]
                                                                        700                                                              35%
     38%
                                                                        600                                                              30%
                                                                        500                                                              25%
                        38% of registered
                        customers had once                              400                                                              20%

                        been active but are now                         300                                                              15%
                        dormant                                         200                                                              10%
                        [range: 12% to 68%]                             100                                                              5%
                                                                          0                                                              0%
     54%
                        54% of registered
                        customers had signed up
                        but never transacted                                   Active customers       Registered customers       Activity Rate
                        [range: 25% to 87%]
                                                                                          *trend numbers are cumulative across 3 providers
            Notes:
            • These are straight averages across all 4 due to large
               differences in numbers of registered customers across
                                                                               1.trends        2.usage       3.activity   4.takeaway         12
               providers
The frequency of activity even among active customers is low



At one provider 63% of all active customers averaged less than 2 transactions a month, though
there is a small cadre of highly active customers




                            63% of all active customers
                            averaged less than 2
                            transactions a month




                                                   Top 5% of customers averaged
                                                   over 54 transactions per month




                                                      1.trends    2.usage    3.activity   4.takeaway   13
Customer activity drops the longer they are with a service, so aggregate
activity rates mask a real drop in activity over time



80% of customers who are active in their first             Even if aggregate activity rates are staying
month after registration become dormant by                 flat, new registrations could be masking a
their 4th month                                            real drop in activity rates

         % of customers transacting,                                           If registrations
           1st month – 4th month*                                              stopped today…
20%                                                                     900                                                             25%




                                                            Thousands
18%                                                                     800
16%                                                                     700                                                           20%
                                                                                                                         Overall activity
14%                                                                     600                                              rate would fall
                                                                                                                                        15%
12%                                                                     500
10%                                                                     400
                                                                                                                                        10%
8%                                                                      300
6%                                                                      200                                                             5%
4%                                                                      100
2%                                                                        0                                                             0%
0%                                                                             1     2     3       4   5    6        7     8        9
        M1        M2             M3            M4
                                                                                   Registered customers             Activity Rate


                       *data aggregated from 3 providers

                                                                        1.trends         2.usage       3.activity         4.takeaway          14
Vintage analysis is a more accurate way to measure activity rate trends
as it reduces masking effects of new registrations


Vintage analysis looks at whether customers              Vintage analysis provides a more accurate
are transacting 3 months AFTER registration,             picture of activity rate over time, as it reduces
eliminating masking effect of new customers              noise from new registrations
 Overall activity rates allow new registrations to
                                                                                                                                        This spike in activity is
 mask drops in individual activity over time                                                                                            due to a registration
                                                         40%                                                                            promotion
                                     Standard
 Activity rate




                                     trend line          35%

                                                         30%

                                                         25%                                                                            Vintage analysis shows
                                                                                                                                        that the 1 month activity
                                                         20%                                                                            spike did not actually
                      Time                                                                                                              lead to greater activity
                                                         15%
                                                                                                                                        rates 3 months later
 Vintage analysis looks at activity 3 months after       10%
 sign-up to give a more accurate trend view
                                                         5%

                                                         0%
 Activity rate




                                                                                                                                       Oct-10


                                                                                                                                                         Dec-10
                                                                                                   Jun-10
                                                                                                            Jul-10




                                                                                                                                                                  Jan-11
                                                                                                                                                                           Feb-11
                                                               Feb-10
                                                                        Mar-10
                                                                                 Apr-10




                                                                                                                                                                                    Mar-11
                                                                                                                                                                                             Apr-11
                                                                                                                     Aug-10
                                                                                                                              Sep-10


                                                                                                                                                Nov-10
                                                                                          May-10




                                                                                                                                                                                                      May-11
                                            Vintage
                                            trend line
                                                                                          % transacted in 3rd month (vintage)
                                                                                          % transacted in 1st month
                      Time                                                                                                             Note: data from one provider

                                                            1.trends                          2.usage                              3.activity                         4.takeaway                               15
Main Findings: Customer segments by usage patterns



                     Each service has super-users responsible for a out-size proportion of total
1. General           transactions. On average, 5% of users were responsible for 30% of total value
   Activity Trends   transacted. Providers should study these users to better understand the value
                     proposition of their service.


2. Customer
  segments by        44% of all active users only performed one type of transaction on the service,
                     despite a range of available transactions. This implies that providers should
  usage
                     customize their marketing messages to different user segments.
  patterns


3. Factors           48% of transfers happened locally within a municipality or province, indicating that
   Affecting         P2P transfer patterns do not always follow the “send money home” pattern that
   Activity          was seen in M-PESA Kenya.




4. Takeaways &
   Action Items




                                                        1.trends    2.usage    3.activity   4.takeaway   16
Studying “super-users” can help providers understand the real value
proposition of their service


Super-users included only the top 5% of customers by total value of transactions, but this 5%
was responsible for over 30% of the total value transacted on the service*
     We defined “super-users” as the top 5%
     of customers by total value of their     The average                  Avg value of
     transactions over the last 3 months      monthly value of        transactions for super          $242.63
                                              transactions for                users
       5%:                                    super users was 6.5
                                              times the average             Avg value of
       Super-users
                                              value of transactions      transactions for all   $37.64
                                                                               users
                                              across all users


                                              Super-users make                Total value transacted on
       95%:                                   up only 5% of total                      service
       Everyone else                          customers but
                                              represent more
                                              than 30% of total              30%
                                              value of transactions
                                              on a service               Super-users

*average across 3 providers


Takeaway: Super-users are valuable in and of themselves as they represent a major part of the
value of a service. Studying them can also help providers understand what value proposition
their service really offers to customers.

                                                         1.trends        2.usage         3.activity       4.takeaway   17
We do not recommend studying super-users based on the NUMBER of
transactions as that can give misleading results about usage of a service


Analyzing transaction patterns by NUMBER of transactions over-inflates the importance of
airtime top-up transactions for branchless banking systems and understates the true value of
P2P transfers*
  100%
                                                            Other transactions
   80%
                                                            Airtime top-ups
                                                            P2P transfers (sending)
   60%


   40%

                    5%                     46%             Super users defined by NUMBER of transactions are
   20%                                                     often using the service for unintended activities:
                    24%                                    For 2 providers, super-users defined by NUMBER of
    0%                                      5%             transactions were doing hundreds or thousands of
            % of total value of     % of total number of   airtime purchases a month, and turned out to be
              transactions             transactions        individuals acting as unauthorized resellers of airtime

*aggregated data from 2 providers



Takeaway: When analyzing super-users, a definition based on VALUE of transactions is likely to
give more accurate results than a definition based on NUMBER of transactions

                                                               1.trends       2.usage    3.activity   4.takeaway     18
Providers should understand why particular customer demographics are more
likely to be super-users and consider specifically targeting these segments



 Customer occupations seemed to affect the                   Gender also seemed to have an effect on
 likelihood of being a super-user at one                     whether a customer became a super-user at
 provider                                                    two providers

   Customers identifying as self-employed were far
                                                              Female customers were slightly LESS likely to
   more likely to be super users than average,
                                                              be super-users than males
   while students were far less likely than average



    19.0%                                                                        16.0%
                                                                     14.7%




            11.2%                    % of super users                                                 % of super-users
                           10.6%
                                     % of all active users                                            % of all active users


                    5.5%




   Self-employed      Student                                             Females



                                                               1.trends      2.usage     3.activity      4.takeaway           19
Low and high value users utilize branchless banking services for very
different purposes

Looking at two different providers, low and high value usage patterns are very different, but
differences across markets means each provider must analyze their own customer transactions

                                              Debits
                                                  Cash-out    Top-up    Bill Payments    Transfers sent   Balance Inquiries   Left in account

 Credits                                       100%                               5%
                                                               12%
                                                 90%
                                                                1%
                                                 80%                                                                            32.3%
    Cash-in                                                    29%                50%
                                                 70%
                        Customer                 60%
                        Account/                               19%                                                              13.3%
                                                 50%                                                        97.2%
                         Wallet                  40%
   Transfers                                     30%                              29%
   received                                                    50%                                                              48.5%
                                                 20%
                                                                                  11%
                                                 10%
                                                                                   9%
                                                  0%
                                                               Low                High                       Low                 High
                                                                     Provider 1                                  Provider 2



                                                                         Usage patterns for these 2 services differ widely, with
  Between low and high users, the biggest difference
                                                                         Provider 1 seeing significant bill payments usage while
  comes from the value of transfers sent
                                                                         Provider 2 seemed to be used as a store of value


               Note: “Low” refers to the bottom third of active users
               by value of transactions and “high” refers to the top
                                                                              1.trends         2.usage         3.activity      4.takeaway       20
               third, excluding “super-users”
Many users only perform one type of transaction out of a broader
 offering, indicating a variety of segments and use cases for services


 While all providers offered a mix of services,                           Certain demographic factors were tied to
 a large proportion of customers only                                     these single transaction-type segments,
 performed one type of transaction                                        which has implications for marketing efforts


      Across 2 providers, almost 50% of users                                  At one provider, those customers who only
      only performed one type of mobile money                                  received money were 47% more likely to be
      transaction                                                              female than average

                                                                         9%
                                                     Savers†             8%
                                                                         7%
                                                     Top-up
                                                                         6%

                                                     Receivers‡          5%
                                                                         4%
                                                     Senders‡
                                                                         3%
                                                                         2%
                                                     Variety of
                                                     transctions         1%
                                                                         0%
                                                                                Females as % of "receivers-        Females as % of total
                                                                                          only"                        customers
†Defined as those who have not done any transactions other than deposits and withdrawals
‡We did not exclude senders or receivers who had also used top-up
                                                                              1.trends       2.usage          3.activity    4.takeaway     21
P2P Transfer Patterns: Transfers do not always follow a standard “send
money home” pattern, with almost half of all transfers happening locally


Almost half of the total value transferred was         Looking at the number of transfers, the top
sent within the same region when averaging             sending cities are also receiving a major
across 2 of the providers                              portion of the transfers




            51.8%


                                     48.2%




                                                           1             2            3          4          5
                                                                             Top sending regions

     Transfers Inter-region   Transfers Intra-region                Transfers sent     Transfers received




Takeaway: Providers should not simply default to the “send money home” messaging and
strategy that worked for M-PESA Kenya as it may not fit in many markets

                                                         1.trends        2.usage       3.activity    4.takeaway   22
Main Findings: Factors Affecting Activity Rates




1. General
                     Customer demographic differences are highly correlated with differences in
   Activity Trends   activity rates, so providers should analyze demographic data to learn from
                     these segments.


2. Customer
  segments by        The activity rate of customers registered by the best agents was over 40
  usage              times higher than those registered by the worst agents, so providers must
  patterns           learn from the best agents and intervene with the worst.


                     Customer usage of a service in their first month after registration largely
    3. Factors       dictates their future activity, so providers should focus efforts on getting
Affecting Activity   customers to not just transact in the first month, but to do specific types of
                     transactions.


4. Takeaways &       For MNOs with mobile money services, high value voice/SMS/data customers
   Action Items      also tend to be higher value mobile money customers.



                                                       1.trends   2.usage    3.activity   4.takeaway   23
Demographic Effects: Customer demographics can have significant
effects on activity, so providers should collect and act on this data (1)


Gender: averaged across 3 providers, female        Age: age of customers had statistically
customers were 41% more likely to be active        significant effects on activity rates, but the
than males                                         results were not consistent across providers
           Active   Dormant   Never Transacted                              Provider 1   Provider 2      At provider 1 younger
                                                                            14.00%                       customers were more active
 100%                                                                                                    while at provider 2 older
  90%                                                                       12.00%                       customers were more active
                                                                                         12.38%
  80%




                                                    Average activity rate
                45%                     43%
                                                                            10.00%
  70%
                                                                                                                          9.87%
  60%                                                                        8.00%
  50%                                                                                                                              7.5%
                                                                             6.00%
  40%
                                                                                                      5.5%
  30%                                                                        4.00%
  20%
                                                                             2.00%
  10%           15%                     11%
  0%                                                                         0.00%
               Female                   Male                                             Youngest Quartile                Oldest Quartile




 Females were more likely to be active, but also   Because demographics affect activity differently
 slightly more likely to have never transacted     across different market contexts, providers must
                                                   collect and analyze their own data rather than rely on
                                                   generalizations

                                                                            1.trends      2.usage            3.activity       4.takeaway    24
Demographic Effects: Customer demographics can have significant
effects on activity, so providers should collect and act on this data (2)


Income level: At one provider, customers in            Occupation: Activity rates for some
the lowest income bracket were 3 times as              occupation segments was 3 times the overall
active as the wealthiest                               mean activity rate for the service*

             90 day activity rate by                         Activity rate by stated occupation
                monthly income                         35%

                                                       30%
           16.9%
                                                       25%

                                                       20%       Mean
                                                              activity rate
                                                       15%

                                    5.2%               10%

                                                       5%
        Lowest income           Highest income         0%




Only one provider collected data on income levels so
while we encourage other providers to collect this
data, we cannot make generalized claims on the
impact of income on activity                                                               *data from 1 provider



                                                         1.trends       2.usage   3.activity    4.takeaway         25
Registration Agent Effects: Agents differ widely on the activity level of
customers they register


Across 2 providers, the activity rate of customers registered by the best agents was over 40
times higher than those registered by the worst agents
                              Top 20% of agents         Profile of customers
       All Agents            by # of registrations   registered by these agents
     Top 20% of                 Top 10% by
     agents by # of             activity rate                         Activity




                                                         Top 10%
                                                                                    Customers registered
     registrations                                                     Rate
                                                                                    by these agents make
                                                                                    up 5.1% of total
                                                                      39.9%         customers




                                                         Bottom 10%
                                                                      Activity
                                                                                    Customers registered
                                                                       Rate
                                                                                    by these agents make
                                                                                    up 5.7% of total
                                                                      0.9%          customers
                                Bottom 10% by
                                activity rate


Takeaway: Agent registration incentives should take into account not only the NUMBER of
customers registered but also the ongoing ACTIVITY of those customers. To do so providers
must monitor activity rates for each agents’ customers

                                                     1.trends           2.usage   3.activity   4.takeaway   26
First Month Effects: Transaction behavior in the customer’s first month
significantly affects ongoing customer activity and value


The number of transactions in a customer’s                                      …but to get more VALUE from customers
first month is highly correlated with their                                     over time, the TYPE of transaction they are
activity in subsequent months…                                                  doing in their first month has a greater effect

                                35%                                      33%
 Likelihood of transacting in




                                                                                                                   $2.00




                                                                                Value of transactions 3rd month2
                                30%

                                25%
         3rd month




                                20%                                                                                $1.50

                                15%                         12%
                                10%
                                                                                                                                                   4X
                                                                                                                   $1.00
                                5%               3%
                                      1%
                                0%
                                      0         1-2          3-4          >=5
                                      Number of transactions in first month*                                       $0.50


           Across 4 providers, customers who did 6+
           transactions in their 1st month were 5.5 times
                                                                                                                      $-
           more likely to transact in their 2nd month than                                                                    Customers who only top-   Customers who do other
           those who did only 1-2 transactions                                                                                     up first month       transactions first month


*data aggregated from 3 providers                                                                                                             2weighted   avg across 2 providers

                                                                                                                   1.trends        2.usage       3.activity     4.takeaway         27
First Month Effects: Customers rarely “graduate” over time to higher
value transactions making the first month even more important


Across 2 providers, of the customers who did only cash-in + top-up first month, only 19% did at
least one other type of value transaction in their 3rd month, compared with 65% for those who
tried a variety of transactions in their first month

                     % of customers who do more than top-up in months 2-4
 70%

 60%                                                          65%                                    64%
 50%
                         51%
 40%

 30%

 20%
               20%                                 19%                                 19%
 10%

  0%
                 2nd month                           3rd month                            4th month

           Customers who only top-up first month         Customers who do other transactions first month




Takeaway: Usage patterns in the first month largely determine future usage, which may mean
providers should focus more on getting new customers to try a variety of transactions

                                                              1.trends      2.usage     3.activity     4.takeaway   28
Voice/SMS Usage Effects: How phone customers use voice and SMS
could be a predictor of their activity and value in a mobile money service


The likelihood of a customer being an active                  Total value of customer mobile money
mobile money customer was correlated with                     transactions seemed to be even more closely
their voice and especially SMS usage                          linked with voice/sms usage*
  At one provider, SMS usage in particular                                                                     $6.00




                                                               Value of customer transactions(last 3 months)
  affected activity rates, with high SMS users 20%
  more likely to be active than low SMS users                                                                                +21%             +17%               +20%
                                                                                                               $5.00
  High SMS Usage

                                                                                                               $4.00
  Low SMS Usage

                  0.00%       5.00% 10.00% 15.00% 20.00%
                                                                                                               $3.00
                                   Activity Rate

  At another provider, consistently active mobile                                                              $2.00
  money subscribers had a combined voice/sms
  ARPU (avg revenue per user) 20% higher than
  subscribers who had never transacted                                                                         $1.00

   Never transacted
                                                                                                                 $-
  Consistently active                                                                                                     Voice (minutes)    Data (GPRS)           SMS

                                                                                                                                 Low Usage     High Usage
                        $-        $5.00    $10.00    $15.00
                             Voice/SMS combined ARPU
                                                                                                                                                  *data    from 1 provider only

                                                                                                               1.trends        2.usage        3.activity      4.takeaway          29
Takeaways and Action Items:



                     Based on our analysis across these 4 example providers we can recommend
1. General           certain indicators and analysis that providers should be tracking to help them
   Activity Trends   understand and tackle the problem of low customer activity
                         In many cases providers are already collecting the requisite data and we are only
                         suggesting ways of analyzing and acting on this data that may be new to some
2. Customer              providers
  segments by
  usage                  In other cases we are recommending providers collect new types of data that can help
  patterns               them better understand, communicate with, and serve their customers



3. Factors           The following takeaways have 3 components: Collect, Analyze, and Act
   Affecting
   Activity            Collect:                       Analyze:                        Act:
                                                       Examples of the types of
                           Types of data and                                             Actions which can be
                                                        analysis providers can
                            specific metrics                                            taken as a result of the
                                                        conduct based on data
4. Takeaways &           providers should track                                              data analysis
                                                              collected
   Action Items




                                                            1.trends      2.usage      3.activity   4.takeaway     30
Takeaways and Action Items: General activity trends


Responding to Activity Trends
Collect                                 Analyze                                    Act
                                         Vintage analysis: tracking not              • Use vintage analysis to more
                                         only standard activity definitions            accurately judge the
 Customer registration and               but also how active customers are             effectiveness of customer
 transaction data already collected      by their 3rd month with the service           recruitment efforts (including
 by most providers’ systems is           (did customers do at least one                new promotions or pricing)
 sufficient for this analysis            transaction in their 3rd month)             • Drops in vintage activity rates
                                         gives a clearer picture of activity           are an early warning of problems
                                         trends over time                              with recruitment strategy

 For our analysis we defined 3 fairly                                                • If most inactive customers are
 standard categories:                                                                  dormant, this signals that
 • active [at least one transaction                                                    marketing & initial registrations
    in the last 90 days]                                                               are working, but the ongoing
 • dormant [transacted at least                                                        value proposition is broken.
    once but no transactions in the                                                  • If vice versa, then attention
    last 90 days]                                                                      should be focused on better
 • never transacted                                                                    customer recruitment




                                                                 1.trends      2.usage      3.activity   4.takeaway        31
Takeaways and Action Items: Customer Segments by Usage Patterns


Learning from Super-users
Collect                             Analyze                                      Act
                                     Compare demographics (age,                    • Set up interviews/focus groups
                                     gender, location, etc) against                  with a sample of super-users to
                                     inactive registered clients to                  understand why the find the
  Identify the top 5% of users by    identify differences                            service so valuable
  VALUE of transactions using
  customer registration and                                                        • Adjust marketing to capture more
  transaction data                   Compare their usage pattern and                 people like them
                                     demographics against average
                                     users to identify characteristics             • Recruit them as community
                                     which set super-users apart                     champions for your service


Understanding the real value proposition for transfers
Collect                             Analyze                                      Act
                                                                                   Conduct interviews/focus groups
  Identify top transfer corridors
                                     Try to find connections between               to better understand what the top
  using existing customer and
                                     demographics and transfer                     purposes are for money transfers
  transaction databases
                                     behavior (ie, are students all                on your service
                                     receiving long-distance transfers
  Additional demographics such as    while small-business holders are              Adjust marketing messages and/or
  customer occupation and            sending and receiving many                    operations such as agent
  income could be very valuable      transfers within their municipality?)         recruitment based on reality of
  here                                                                             transfer usage


                                                              1.trends       2.usage     3.activity   4.takeaway       32
Takeaways and Action Items: Factors affecting activity (1 of 2)


Monitoring effects of Agents on activity
Collect                               Analyze                                 Act
                                       Identify outlier agents who are          • Study these agents to
                                       signing up many customers who              understand why they have such
  Collect both the number of
                                       are INACTIVE or VERY ACTIVE                low/high activity rates
  customers each agent registers
                                       in subsequent months and see
  AND the percentage of the                                                     • Take action with low performing
                                       what sets them apart from each
  agent’s customers who are                                                       agents if they do not improve
                                       other and from average agents
  active in later months                                                          and generously reward agents
                                       (using demographic data
                                       collected)                                 with high active rates


                                                                                • Talk to these agents and
                                                                                  understand what factors are
                                       Study those agents who are both            increasing their activity (is it
  Collect any agent demographics
                                       signing up many customers who              demographics or their
  possible such as type and size of
                                       are ACTIVE to see what sets them           interactions with customers)
  business, location, age and
                                       apart from average agents (using
  experience of proprietor, etc                                                 • Take steps to spread any best
                                       demographic data collected)
                                                                                  practices identified at these
                                                                                  good agents to all other agents




                                                            1.trends      2.usage      3.activity    4.takeaway      33
Takeaways and Action Items: Factors affecting activity (2 of 2)


Understanding customer demographic effects
Collect                             Analyze                                    Act
                                     Analyze activity rates across              • Talk with members of these
                                     demographic factors and identify             interesting groups to understand
                                     groups with higher/lower activity            why the service works or does
 Collect customer demographic
 data such as gender, occupation,                                                 not work for their needs
 income level, age, etc                                                         • Determine which groups to target
                                     Identify interesting groups based
                                     on your service’s own data, not              and revisit marketing and product
                                     based on patterns in other markets           strategy where appropriate



Adapting to the effects of customers’ first month transaction behavior
Collect                             Analyze                                    Act
                                                                                 Incentivize users to transact in first
  Customers’ first month                                                         month, focusing on transactions
  transaction behavior               Using regression analysis identify          that are most correlated with
                                     what number and type of                     higher usage later
                                     transactions in the first month are
                                     correlated with higher value future
  Customers’ transaction             customers                                   Incentivize agents to promote
  behavior in following months                                                   beneficial transaction behaviors in
                                                                                 the first month for their customers


                                                             1.trends      2.usage      3.activity    4.takeaway       34
Advancing financial access for the world’s poor
                 www.cgap.org
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The Challenge of Inactive Customers

  • 1. The Challenge of Inactive Customers: Using Data Analytics to Understand and Tackle Low Customer Activity Claudia McKay Toru Mino Paola de Baldomero Zazo February 2012
  • 2. While the number of branchless banking services has grown rapidly, the vast majority of registered customers are not actively transacting Most branchless banking providers struggle with high rates of inactive customers 140 120 120 100 80 While 120 branchless banking 60 implementations have 1 in 6 been launched since 1 in 11 2007 only 11 of those 40 21 have reached 200k 20 11 active users 0 Implementations launched after Implementations with >1 million "Confirmed" implementations 2007 registered with >200k active users In a CGAP survey, 64% of managers said less than 30% of their registered customers were active, and active rates of less than 10% are not uncommon 2 Source: CGAP and Coffey International, data as of Q1 2012.
  • 3. It is challenging to develop a viable business model with low activity rates Low activity rates sharply increase the cost a If acquisition costs per active customer are this provider must invest to acquire each ACTIVE high, even in a best case revenue per customer customer scenario it could take 10 years to breakeven CGAP estimates most services spend between USD 2-5 to register each customer. With 5% activity rate and a $5 per Assuming M-PESA Kenya revenue per customer as a “best customer acquisition cost, a service must invest $100 to case”, with an activity rate of just 5%, a service would take acquire each active customer 10 years to break even on the customer acquisition cost Acquisition Cost per Active Customer Time to Break-even per Active Customer $120 12 $100 10 $80 8 years $60 6 $40 4 $20 2 $0 0 50% Active 15% Active 5% Active 50% Active 15% Active 5% Active With current activity rates, most branchless banking businesses cannot generate enough revenue per customer to remain viable 3
  • 4. CGAP’s work on Inactive Customers • Moving a potential customer from awareness of a branchless banking service to regular use of the service requires different levers all to be working effectively and in an integrated manner: the agent network, product features, marketing, customer service, user experience and system/network. CGAP has developed a framework to map this process. • CGAP and others have produced publicly available materials on several of these critical issues such as agent networks and marketing. • One of the missing gaps in tackling this challenge is helping providers understand their customers and to identify the critical issues to resolve in their own services. • Ultimately, understanding customers is a complex undertaking and will rely heavily on a variety of tools such as focus groups and surveys. However, the first step for every provider should be to conduct data analysis on the veritable gold mine already at hand - its own database. CGAP has worked with four providers to understand how basic transaction level data can be used to shed light on customer activity. 4
  • 5. Who should use this deck Funders & Other Supporting Branchless Banking Providers Organizations This deck is primarily targeted towards Funders and other supporting branchless banking providers and aims to organizations in the financial help them use their data to better inclusion/branchless banking space may understand their customers and the also benefit from this deck’s attempt to reasons for low activity levels in their identify the types of indicators and analysis services. that shed light on customer usage and value. This may help funders standardize performance metrics across multiple organizations. 5
  • 6. How to use this deck This deck does not provide answers on how to improve activity at any specific provider. Instead we identify: (1) which data providers should collect about their customers and services, (2) what types of analysis providers can conduct based on the data collected, and (3) what kind of follow-up actions providers can take to further understand causes of customer inactivity in their own service. 1. Collect 2. Analyze 3. Act This deck helps providers This deck also identifies ways This deck does not provide understand what data they of analyzing data about specific answers to improve should be collecting and which branchless banking services activity as we found that the indicators they should be that we found gave the clearest influence of indicators on tracking. CGAP studied picture of how services are activity are not automatically hundreds of different variables being used and which factors transferrable across markets. and only included those are affecting activity rates. Instead we spotlight interesting variables that had statistically customer segments and significant impacts on activity behavior patterns that can be levels in this deck. followed up by providers through qualitative research and interaction with customers. 6
  • 7. Research introduction: quantitative analysis of data from 4 providers across 3 regions Geography: 2 South Asian 1 African 1 Southeast Asian Total customers: ~3 million Provider types: 1 Bank-led 2 MNO-led 1 3rd Party-led • All services offer a mobile wallet focused on P2P transfers and bill payments Selection Criteria: • In the market at least 18 months so we could track trends over time • Perceive low customer activity as a challenge (though we did not deliberately seek out providers with abnormally low activity rates) 7
  • 8. Research introduction: methodology of analysis Data utilized Hypotheses guiding our Types of quantitative analysis analysis conducted Demographic segments Analysis of activity Customer influence activity rates trends registrations and demographics Regression analysis (probability of activity as Usage pattern segments dependent variable) help explain activity Providers Customer Cross analysis of usage transactions (at pattern and demographic least 6 months) segments Agents affect activity rate of customers they register Analysis of Money transfer patterns Agent registrations and Service usage in the 1st Analysis of agent demographics month affects ongoing demographics and activity registrations 8
  • 9. Outline of Deck 1. General Activity Slides 11 – 15: Analysis of overall activity rate trends Trends 2. Customer segments Slides 16 – 22: Interesting findings about customer segments and by usage patterns usage of services 3. Factors Affecting Slides 23 - 29: Factors which were found to have a statistically Activity significant influence on activity rates Slides 30 – 34: Based on our analysis across these 4 example 4. Takeaways & Action providers we can recommend certain indicators and analysis that Items providers should be tracking to help them understand and tackle the problem of low customer activity 9
  • 10. Main Findings on Customer Activity 1. General Activity rates are low across all providers: average activity rate was only 8% and Activity 54% of all registered customers had never tried the service. We defined activity Trends as at least 1 transaction in the last 90 days. Each service has super-users responsible for a out-size proportion of total transactions. On average, 5% of users were responsible for 30% of total value 2. Customer transacted. Providers should study these users to better understand the value proposition of their service. segments by usage patterns 48% of transfers happened locally within a municipality or province, indicating that P2P transfer patterns do not always follow the “send money home” pattern that was seen in M-PESA Kenya. The activity rate of customers registered by the best agents was over 40 times higher than those registered by the worst agents, so providers must learn from the best agents and intervene with the worst. 3. Factors Affecting Activity Customer usage of a service in their first month after registration largely dictates their future activity, so providers should focus efforts on getting customers to not just transact in the first month, but to do specific types of transactions. 10
  • 11. Main Findings: General Activity Trends 1. General Average activity rate across providers was just 8%, and activity rates have Activity Trends not increased much even as registrations accelerate 2. Customer 80% of customers who are active in their first month after registration have segments by stopped transacting by their 4th month, so new subscriber growth masks a usage real drop in activity rates over time patterns 3. Factors To more accurately track activity rate trends over time we recommend using Affecting Activity vintage analysis 4. Takeaways & Action Items 1.trends 2.usage 3.activity 4.takeaway 11
  • 12. Activity rates are low across all 4 providers and have not grown along with customer registrations Only 8% of registered customers were active Cumulatively, activity rates have remained across the 4 providers we studied relatively flat while registrations have grown rapidly* 8% 1,000 50% Only 8% of registered 900 45% customers were active 800 40% [range: 1% to 18%] 700 35% 38% 600 30% 500 25% 38% of registered customers had once 400 20% been active but are now 300 15% dormant 200 10% [range: 12% to 68%] 100 5% 0 0% 54% 54% of registered customers had signed up but never transacted Active customers Registered customers Activity Rate [range: 25% to 87%] *trend numbers are cumulative across 3 providers Notes: • These are straight averages across all 4 due to large differences in numbers of registered customers across 1.trends 2.usage 3.activity 4.takeaway 12 providers
  • 13. The frequency of activity even among active customers is low At one provider 63% of all active customers averaged less than 2 transactions a month, though there is a small cadre of highly active customers 63% of all active customers averaged less than 2 transactions a month Top 5% of customers averaged over 54 transactions per month 1.trends 2.usage 3.activity 4.takeaway 13
  • 14. Customer activity drops the longer they are with a service, so aggregate activity rates mask a real drop in activity over time 80% of customers who are active in their first Even if aggregate activity rates are staying month after registration become dormant by flat, new registrations could be masking a their 4th month real drop in activity rates % of customers transacting, If registrations 1st month – 4th month* stopped today… 20% 900 25% Thousands 18% 800 16% 700 20% Overall activity 14% 600 rate would fall 15% 12% 500 10% 400 10% 8% 300 6% 200 5% 4% 100 2% 0 0% 0% 1 2 3 4 5 6 7 8 9 M1 M2 M3 M4 Registered customers Activity Rate *data aggregated from 3 providers 1.trends 2.usage 3.activity 4.takeaway 14
  • 15. Vintage analysis is a more accurate way to measure activity rate trends as it reduces masking effects of new registrations Vintage analysis looks at whether customers Vintage analysis provides a more accurate are transacting 3 months AFTER registration, picture of activity rate over time, as it reduces eliminating masking effect of new customers noise from new registrations Overall activity rates allow new registrations to This spike in activity is mask drops in individual activity over time due to a registration 40% promotion Standard Activity rate trend line 35% 30% 25% Vintage analysis shows that the 1 month activity 20% spike did not actually Time lead to greater activity 15% rates 3 months later Vintage analysis looks at activity 3 months after 10% sign-up to give a more accurate trend view 5% 0% Activity rate Oct-10 Dec-10 Jun-10 Jul-10 Jan-11 Feb-11 Feb-10 Mar-10 Apr-10 Mar-11 Apr-11 Aug-10 Sep-10 Nov-10 May-10 May-11 Vintage trend line % transacted in 3rd month (vintage) % transacted in 1st month Time Note: data from one provider 1.trends 2.usage 3.activity 4.takeaway 15
  • 16. Main Findings: Customer segments by usage patterns Each service has super-users responsible for a out-size proportion of total 1. General transactions. On average, 5% of users were responsible for 30% of total value Activity Trends transacted. Providers should study these users to better understand the value proposition of their service. 2. Customer segments by 44% of all active users only performed one type of transaction on the service, despite a range of available transactions. This implies that providers should usage customize their marketing messages to different user segments. patterns 3. Factors 48% of transfers happened locally within a municipality or province, indicating that Affecting P2P transfer patterns do not always follow the “send money home” pattern that Activity was seen in M-PESA Kenya. 4. Takeaways & Action Items 1.trends 2.usage 3.activity 4.takeaway 16
  • 17. Studying “super-users” can help providers understand the real value proposition of their service Super-users included only the top 5% of customers by total value of transactions, but this 5% was responsible for over 30% of the total value transacted on the service* We defined “super-users” as the top 5% of customers by total value of their The average Avg value of transactions over the last 3 months monthly value of transactions for super $242.63 transactions for users 5%: super users was 6.5 times the average Avg value of Super-users value of transactions transactions for all $37.64 users across all users Super-users make Total value transacted on 95%: up only 5% of total service Everyone else customers but represent more than 30% of total 30% value of transactions on a service Super-users *average across 3 providers Takeaway: Super-users are valuable in and of themselves as they represent a major part of the value of a service. Studying them can also help providers understand what value proposition their service really offers to customers. 1.trends 2.usage 3.activity 4.takeaway 17
  • 18. We do not recommend studying super-users based on the NUMBER of transactions as that can give misleading results about usage of a service Analyzing transaction patterns by NUMBER of transactions over-inflates the importance of airtime top-up transactions for branchless banking systems and understates the true value of P2P transfers* 100% Other transactions 80% Airtime top-ups P2P transfers (sending) 60% 40% 5% 46% Super users defined by NUMBER of transactions are 20% often using the service for unintended activities: 24% For 2 providers, super-users defined by NUMBER of 0% 5% transactions were doing hundreds or thousands of % of total value of % of total number of airtime purchases a month, and turned out to be transactions transactions individuals acting as unauthorized resellers of airtime *aggregated data from 2 providers Takeaway: When analyzing super-users, a definition based on VALUE of transactions is likely to give more accurate results than a definition based on NUMBER of transactions 1.trends 2.usage 3.activity 4.takeaway 18
  • 19. Providers should understand why particular customer demographics are more likely to be super-users and consider specifically targeting these segments Customer occupations seemed to affect the Gender also seemed to have an effect on likelihood of being a super-user at one whether a customer became a super-user at provider two providers Customers identifying as self-employed were far Female customers were slightly LESS likely to more likely to be super users than average, be super-users than males while students were far less likely than average 19.0% 16.0% 14.7% 11.2% % of super users % of super-users 10.6% % of all active users % of all active users 5.5% Self-employed Student Females 1.trends 2.usage 3.activity 4.takeaway 19
  • 20. Low and high value users utilize branchless banking services for very different purposes Looking at two different providers, low and high value usage patterns are very different, but differences across markets means each provider must analyze their own customer transactions Debits Cash-out Top-up Bill Payments Transfers sent Balance Inquiries Left in account Credits 100% 5% 12% 90% 1% 80% 32.3% Cash-in 29% 50% 70% Customer 60% Account/ 19% 13.3% 50% 97.2% Wallet 40% Transfers 30% 29% received 50% 48.5% 20% 11% 10% 9% 0% Low High Low High Provider 1 Provider 2 Usage patterns for these 2 services differ widely, with Between low and high users, the biggest difference Provider 1 seeing significant bill payments usage while comes from the value of transfers sent Provider 2 seemed to be used as a store of value Note: “Low” refers to the bottom third of active users by value of transactions and “high” refers to the top 1.trends 2.usage 3.activity 4.takeaway 20 third, excluding “super-users”
  • 21. Many users only perform one type of transaction out of a broader offering, indicating a variety of segments and use cases for services While all providers offered a mix of services, Certain demographic factors were tied to a large proportion of customers only these single transaction-type segments, performed one type of transaction which has implications for marketing efforts Across 2 providers, almost 50% of users At one provider, those customers who only only performed one type of mobile money received money were 47% more likely to be transaction female than average 9% Savers† 8% 7% Top-up 6% Receivers‡ 5% 4% Senders‡ 3% 2% Variety of transctions 1% 0% Females as % of "receivers- Females as % of total only" customers †Defined as those who have not done any transactions other than deposits and withdrawals ‡We did not exclude senders or receivers who had also used top-up 1.trends 2.usage 3.activity 4.takeaway 21
  • 22. P2P Transfer Patterns: Transfers do not always follow a standard “send money home” pattern, with almost half of all transfers happening locally Almost half of the total value transferred was Looking at the number of transfers, the top sent within the same region when averaging sending cities are also receiving a major across 2 of the providers portion of the transfers 51.8% 48.2% 1 2 3 4 5 Top sending regions Transfers Inter-region Transfers Intra-region Transfers sent Transfers received Takeaway: Providers should not simply default to the “send money home” messaging and strategy that worked for M-PESA Kenya as it may not fit in many markets 1.trends 2.usage 3.activity 4.takeaway 22
  • 23. Main Findings: Factors Affecting Activity Rates 1. General Customer demographic differences are highly correlated with differences in Activity Trends activity rates, so providers should analyze demographic data to learn from these segments. 2. Customer segments by The activity rate of customers registered by the best agents was over 40 usage times higher than those registered by the worst agents, so providers must patterns learn from the best agents and intervene with the worst. Customer usage of a service in their first month after registration largely 3. Factors dictates their future activity, so providers should focus efforts on getting Affecting Activity customers to not just transact in the first month, but to do specific types of transactions. 4. Takeaways & For MNOs with mobile money services, high value voice/SMS/data customers Action Items also tend to be higher value mobile money customers. 1.trends 2.usage 3.activity 4.takeaway 23
  • 24. Demographic Effects: Customer demographics can have significant effects on activity, so providers should collect and act on this data (1) Gender: averaged across 3 providers, female Age: age of customers had statistically customers were 41% more likely to be active significant effects on activity rates, but the than males results were not consistent across providers Active Dormant Never Transacted Provider 1 Provider 2 At provider 1 younger 14.00% customers were more active 100% while at provider 2 older 90% 12.00% customers were more active 12.38% 80% Average activity rate 45% 43% 10.00% 70% 9.87% 60% 8.00% 50% 7.5% 6.00% 40% 5.5% 30% 4.00% 20% 2.00% 10% 15% 11% 0% 0.00% Female Male Youngest Quartile Oldest Quartile Females were more likely to be active, but also Because demographics affect activity differently slightly more likely to have never transacted across different market contexts, providers must collect and analyze their own data rather than rely on generalizations 1.trends 2.usage 3.activity 4.takeaway 24
  • 25. Demographic Effects: Customer demographics can have significant effects on activity, so providers should collect and act on this data (2) Income level: At one provider, customers in Occupation: Activity rates for some the lowest income bracket were 3 times as occupation segments was 3 times the overall active as the wealthiest mean activity rate for the service* 90 day activity rate by Activity rate by stated occupation monthly income 35% 30% 16.9% 25% 20% Mean activity rate 15% 5.2% 10% 5% Lowest income Highest income 0% Only one provider collected data on income levels so while we encourage other providers to collect this data, we cannot make generalized claims on the impact of income on activity *data from 1 provider 1.trends 2.usage 3.activity 4.takeaway 25
  • 26. Registration Agent Effects: Agents differ widely on the activity level of customers they register Across 2 providers, the activity rate of customers registered by the best agents was over 40 times higher than those registered by the worst agents Top 20% of agents Profile of customers All Agents by # of registrations registered by these agents Top 20% of Top 10% by agents by # of activity rate Activity Top 10% Customers registered registrations Rate by these agents make up 5.1% of total 39.9% customers Bottom 10% Activity Customers registered Rate by these agents make up 5.7% of total 0.9% customers Bottom 10% by activity rate Takeaway: Agent registration incentives should take into account not only the NUMBER of customers registered but also the ongoing ACTIVITY of those customers. To do so providers must monitor activity rates for each agents’ customers 1.trends 2.usage 3.activity 4.takeaway 26
  • 27. First Month Effects: Transaction behavior in the customer’s first month significantly affects ongoing customer activity and value The number of transactions in a customer’s …but to get more VALUE from customers first month is highly correlated with their over time, the TYPE of transaction they are activity in subsequent months… doing in their first month has a greater effect 35% 33% Likelihood of transacting in $2.00 Value of transactions 3rd month2 30% 25% 3rd month 20% $1.50 15% 12% 10% 4X $1.00 5% 3% 1% 0% 0 1-2 3-4 >=5 Number of transactions in first month* $0.50 Across 4 providers, customers who did 6+ transactions in their 1st month were 5.5 times $- more likely to transact in their 2nd month than Customers who only top- Customers who do other those who did only 1-2 transactions up first month transactions first month *data aggregated from 3 providers 2weighted avg across 2 providers 1.trends 2.usage 3.activity 4.takeaway 27
  • 28. First Month Effects: Customers rarely “graduate” over time to higher value transactions making the first month even more important Across 2 providers, of the customers who did only cash-in + top-up first month, only 19% did at least one other type of value transaction in their 3rd month, compared with 65% for those who tried a variety of transactions in their first month % of customers who do more than top-up in months 2-4 70% 60% 65% 64% 50% 51% 40% 30% 20% 20% 19% 19% 10% 0% 2nd month 3rd month 4th month Customers who only top-up first month Customers who do other transactions first month Takeaway: Usage patterns in the first month largely determine future usage, which may mean providers should focus more on getting new customers to try a variety of transactions 1.trends 2.usage 3.activity 4.takeaway 28
  • 29. Voice/SMS Usage Effects: How phone customers use voice and SMS could be a predictor of their activity and value in a mobile money service The likelihood of a customer being an active Total value of customer mobile money mobile money customer was correlated with transactions seemed to be even more closely their voice and especially SMS usage linked with voice/sms usage* At one provider, SMS usage in particular $6.00 Value of customer transactions(last 3 months) affected activity rates, with high SMS users 20% more likely to be active than low SMS users +21% +17% +20% $5.00 High SMS Usage $4.00 Low SMS Usage 0.00% 5.00% 10.00% 15.00% 20.00% $3.00 Activity Rate At another provider, consistently active mobile $2.00 money subscribers had a combined voice/sms ARPU (avg revenue per user) 20% higher than subscribers who had never transacted $1.00 Never transacted $- Consistently active Voice (minutes) Data (GPRS) SMS Low Usage High Usage $- $5.00 $10.00 $15.00 Voice/SMS combined ARPU *data from 1 provider only 1.trends 2.usage 3.activity 4.takeaway 29
  • 30. Takeaways and Action Items: Based on our analysis across these 4 example providers we can recommend 1. General certain indicators and analysis that providers should be tracking to help them Activity Trends understand and tackle the problem of low customer activity In many cases providers are already collecting the requisite data and we are only suggesting ways of analyzing and acting on this data that may be new to some 2. Customer providers segments by usage In other cases we are recommending providers collect new types of data that can help patterns them better understand, communicate with, and serve their customers 3. Factors The following takeaways have 3 components: Collect, Analyze, and Act Affecting Activity Collect: Analyze: Act: Examples of the types of Types of data and Actions which can be analysis providers can specific metrics taken as a result of the conduct based on data 4. Takeaways & providers should track data analysis collected Action Items 1.trends 2.usage 3.activity 4.takeaway 30
  • 31. Takeaways and Action Items: General activity trends Responding to Activity Trends Collect Analyze Act Vintage analysis: tracking not • Use vintage analysis to more only standard activity definitions accurately judge the Customer registration and but also how active customers are effectiveness of customer transaction data already collected by their 3rd month with the service recruitment efforts (including by most providers’ systems is (did customers do at least one new promotions or pricing) sufficient for this analysis transaction in their 3rd month) • Drops in vintage activity rates gives a clearer picture of activity are an early warning of problems trends over time with recruitment strategy For our analysis we defined 3 fairly • If most inactive customers are standard categories: dormant, this signals that • active [at least one transaction marketing & initial registrations in the last 90 days] are working, but the ongoing • dormant [transacted at least value proposition is broken. once but no transactions in the • If vice versa, then attention last 90 days] should be focused on better • never transacted customer recruitment 1.trends 2.usage 3.activity 4.takeaway 31
  • 32. Takeaways and Action Items: Customer Segments by Usage Patterns Learning from Super-users Collect Analyze Act Compare demographics (age, • Set up interviews/focus groups gender, location, etc) against with a sample of super-users to inactive registered clients to understand why the find the Identify the top 5% of users by identify differences service so valuable VALUE of transactions using customer registration and • Adjust marketing to capture more transaction data Compare their usage pattern and people like them demographics against average users to identify characteristics • Recruit them as community which set super-users apart champions for your service Understanding the real value proposition for transfers Collect Analyze Act Conduct interviews/focus groups Identify top transfer corridors Try to find connections between to better understand what the top using existing customer and demographics and transfer purposes are for money transfers transaction databases behavior (ie, are students all on your service receiving long-distance transfers Additional demographics such as while small-business holders are Adjust marketing messages and/or customer occupation and sending and receiving many operations such as agent income could be very valuable transfers within their municipality?) recruitment based on reality of here transfer usage 1.trends 2.usage 3.activity 4.takeaway 32
  • 33. Takeaways and Action Items: Factors affecting activity (1 of 2) Monitoring effects of Agents on activity Collect Analyze Act Identify outlier agents who are • Study these agents to signing up many customers who understand why they have such Collect both the number of are INACTIVE or VERY ACTIVE low/high activity rates customers each agent registers in subsequent months and see AND the percentage of the • Take action with low performing what sets them apart from each agent’s customers who are agents if they do not improve other and from average agents active in later months and generously reward agents (using demographic data collected) with high active rates • Talk to these agents and understand what factors are Study those agents who are both increasing their activity (is it Collect any agent demographics signing up many customers who demographics or their possible such as type and size of are ACTIVE to see what sets them interactions with customers) business, location, age and apart from average agents (using experience of proprietor, etc • Take steps to spread any best demographic data collected) practices identified at these good agents to all other agents 1.trends 2.usage 3.activity 4.takeaway 33
  • 34. Takeaways and Action Items: Factors affecting activity (2 of 2) Understanding customer demographic effects Collect Analyze Act Analyze activity rates across • Talk with members of these demographic factors and identify interesting groups to understand groups with higher/lower activity why the service works or does Collect customer demographic data such as gender, occupation, not work for their needs income level, age, etc • Determine which groups to target Identify interesting groups based on your service’s own data, not and revisit marketing and product based on patterns in other markets strategy where appropriate Adapting to the effects of customers’ first month transaction behavior Collect Analyze Act Incentivize users to transact in first Customers’ first month month, focusing on transactions transaction behavior Using regression analysis identify that are most correlated with what number and type of higher usage later transactions in the first month are correlated with higher value future Customers’ transaction customers Incentivize agents to promote behavior in following months beneficial transaction behaviors in the first month for their customers 1.trends 2.usage 3.activity 4.takeaway 34
  • 35. Advancing financial access for the world’s poor www.cgap.org www.microfinancegateway.org