Spotlight 10

8/3/2019 Spotlight 10

http://slidepdf.com/reader/full/spotlight-10 1/12

www.mfc.org.pl/research/

MFC Spotlight Note 10

This paper presents a summary of an approach taken by Prizma 2 - a Bosnian microfinance

institution supported by the Microfinance Centre for Central and Eastern Europe and the New

Independent States (MFC)3 under the Imp-Act program4 - for monitoring and understanding

exiting clients. This paper focuses on the exit monitoring rationale, final system charac teristics,

the design process, evaluation of the pilot tests and subsequent launches in all Pr izma branches,

the costs and benefits of this approach, and issues to be taken into account for improving the

system.

In Februar y 2003 MFC and Pr izma designed an exit monitoring system (EMS). This system was

based on MFC’s experience in working with other regional practitioners on client “drop-out”

related issues and was tested in the Prizma’s Mostar branch. The data was collected in March,

April and May 2003. The data was then analyzed by MFC and presented to Priz ma management.

Discussions of lessons learned from the pilot test, potential uses of exit data, other benefits,

costs, and ways to overcome the challenges contributed to revising the exit monitoring system

and a decision to institutionalize the system in all branches. The EMS roll-out in all branches

took place in December 2003 and the f irst data was collected over next three months. In March

2004 Prizma had the first reliable, organizational wide exit results to act on.

Prizma has defined a drop-out as a person who has repaid any type of loan but has not taken

any new loan during the next 90 days. An analysis of historic drop-out data indicates that

90 days is the point at which almost all clients intending to drop out will have already done

so (even at 500+ days the drop out rate was only slightly less than at 90 days). Therefore,

among Prizma drop-outs there are not many who return to Prizma after 90 days – referred to

further in the text as “sleepers.” However, it was agreed that the return rates among the drop-

outs will be monitored in order to be able to systematically reduce the “sleeping” period forfinancial and social reasons. Another important point in defining a drop-out is distinguishing

between voluntary and “forced-out” drop-outs. The forced-out drop-outs are those who are

expelled by an institution or, in the case of group lending, by other group members because

they did something that suggests that they may be a bad credit risk. Prizma has agreed that

every drop-out can be potentially retained. The question is rather how this can be done (and

if microfinance is sufficient for retaining the drop-outs). The table below sums up the general

drop-out categories Prizma has agreed to monitor.

Katarzyna Pawlak and Selma Jahic11 January 2004Beyond Numbers: Prizma’s Exit

Monitoring System

Introduction

How Does Prizma

Define a Drop-out?

Michal Matul and Sefika Vejzovic1 March 2004



2

Beyond Numbers: Prizma’s Exit Monitoring System


As most of Microfinance Institutions (MFIs) in CEE and the NIS, the Prizma is experiencingsignificant drop-out rates5. Prizma has undertaken to discover the underlying reasons for theclient exit problem in order to be more active in reta ining its ta rget clients.

Why Has Prizma

Decided to

Develop an Exit

MonitoringSystem?

Figure 1: The main drop-out profiles

Core follow-up questions to be considered:

Voluntary – satisfied

(reasons not related to Prizma)

• Forever or “asleep”?

• Can microfinance assist in solving their problems?

• Loyal or not?

Voluntary – dissatisfied

(Prizma-related reasons)

• Forever or “asleep”

• Dissatisfaction factors?

• Went to competition?

• Will come back if services improved?

Forced out• Bad character or bad services? If latter, what should be adjusted?

• Went to competition?

During staff discussions, the following reasons were mentioned as justifications for thisaction:

• Increasing Prizma’s income – An Activity Based Costing exercise showed that Prizma invests a lot

in its first-cycle clients. If these clients leave the institution, Prizma loses money.• Working with target clientele – Prizma’s actions are driven by its mission. Monitoring drop-outs

among its target clientele is a tool for verifying that Prizma’s services and procedures are adapted to

target clients’ needs and preferences.

• Building a loyal client base for the future - This will be particularly important when MFIs

start introducing savings services. To be successful in mobilizing savings in the “post-conflict”

environment, it is necessary to have a loyal client base. This is also linked to a competition-surviving

(long-term) strategy. Those MFIs that build a loyal client base will survive on the Bosnian market.

• Empowering staff to become more client-focused - An important rationale for implementing EMS

was the need to open up the minds of front-line staff by giving them an opportunity to analyze

client behavior from another perspective. It was hoped that staff would then be better equipped to

reach their drop-out targets. Moreover, Prizma’s middle and top management were confident that

interviewing drop-outs would stimulate action-oriented learning.

Figure 2: Drop-out Rates at Prizma 2001-03



3

MFC Spotlight Note #10


Box 1: Pilot Test Findings - Main Drop-out Profiles

The drop-out rate in Prizma’s Mostarbranch was at 52,3% for the periodDecember 2002 – February 2003 (source:Prizma MIS).

The Prizma’s EMS generated basedon Mostar branch data the followingbreakdowns in 5 main drop-out profiles(representing 87,9% of all drop-out s):

1. voluntary satisfied lost due to external

reasons – 18,3%;

2. “sleepers” – 24,4%;

3. voluntary dissatisfied eager to return if services are improved – 30,5%;

4. voluntary dissatisfied lost to competition

– 11%;

5. forced out – 3,7%.

An important group of drop-outs can be

retained (profiles 2&3 =54,9%).

Moreover, EMS allows to further profile exiting clients. Here are some significant differences incharacteristics across main drop-out profiles (in the pro file X there are more A, B, C):

• voluntary satisfied lost due to external reasons: Bosnian, single women, service providers,

those who have taken more than 3 loans in Prizma;

• “sleepers”: Croats, Married women, Traders, Those having new businesses, After repayment of the 1st loan, Small last loan size, Those who never had any repayment problems

• voluntary dissatisfied eager to return if services are improved: Serbs, younger women, big

number of dependents, traders, those having “old” businesses, big last loan size;

• voluntary dissatisfied lost to competition: Serbs, older women, big number of dependents,service providers, those who have taken more than 3 loans in Prizma;

• forced out: Sample too small to run any analysis on forced out.

In addition, Prizma has identified lots of other uses of the EMS that were perceived as important forinstitutional development. It was decided that EMS should not be implemented as an ad-hoc researchactivity because obtaining reliable information on exiting clients was not an exclusive goal. Prizma’smanagement hoped that through institutionalized exit monitoring significant positive changes toward anorganizational client-focused culture would be achieved.

Consequently, the EMS has to provide Prizma’s different users with timely answers to the followingquestions:

• Who is leaving?

• What is the magnitude?

• Why does a specific group leave?

Only if one has answers to all the questions that result in the detailed drop-out profiles with specific needsthat should be considered by an MFI, can one take financially/socially viable action to adjust and developmicrofinance products and policies for these particular market segments.

What isPrizma’s EMS?

Prizma has decided to sample drop-outs randomly, not excluding any drop-out client from the list6.

Prizma EMS is based on the semi-structured interview7. The first part of the interview is an in-depthinvestigation of different reasons for client drop-out (dissatisfaction with Prizma services, external reasons,

repayment problems, etc.). At the end of the first part, the interviewer summarizes the two main



4



exit reasons with the respondent. The second part is devoted to the current use of financial services bythe ex-client, competition analysis, and his/her intent to take another loan in Prizma. Upon completionof the interview the loan of ficer (LO) classifies the drop-out according to the nature of his/her reasons,current use of other financial services, and intent to take an additional loan to achieve the main profileof the drop-out.

How often is the data collected? Prizma still has to decide how often it needs the data. Probably, exitingclients will be monitored every year over 2-3 subsequent months (e.g. from January to March). Extendingthe data collection period over 2-3 months is a good balance between additional overload of front-linestaff and ensuring timely reports for the management.

Who collects and analyzes the data? The exit form is administered by a LO interviewing the clientsof another LO. The interviews are done over the phone and last no more than 15-20 minutes. The LOsare trained in interviewing (probing) techniques to be able to collect high-quality data. The head of fice’sMIS manager generates every month (over the data collection period) a list of drop-outs with all the MISdata about them. The branch administrative assistant updates the list, prepares a sample to interviewand divides it among branch field staff. There are no specific sampling criteria. The only l imitation isthat each LO should not get more than 6-9 drop-outs to interview per month. The target for each branchfor the data collection period is to interview 80-90 drop-outs during one period - the sample that isreasonably suf ficient for some multi-level statistical analysis. The branch manager plays a very importantrole in monitoring data quality, data coding and stimulating learning among LOs. It is assumed that therisk of obtaining biased and not credible data due to a LO’s bias is compensated by the LO’s commitmentto better customer service in the future.

Box 2: Pilot Test Findings - Frequency of Reasons Cited

It was agreed in Prizma thatwhile monitoring the profilesby different breakdownsprovides a reliable basis fortaking management decisionsthe figure below (based onaggregated data) tells nothingto management. It is dif ficultto act on such an aggregatedpiece of information withoutknowledge about the exitreasons for specific groups of clients. Unfortunately, it is veryoften a state of art in reportingon microfinance drop-outs.

Box 3: Further Segmentation of Main Profiles

In Prizma case more sophisticatedreporting will be enriched bybreakdowns by following variables:last product type used; use of multipleservices; poverty status (usingPoverty Scorecard); loyalty level(length, breadth, depth); seasonality;repayment performance; business,household, individual (demographic)characteristics; loan use.

All of the above breakdown variablescan be extracted from the MIS.An example of additional usefulinformation might be:

(source: data from all the branches)



5



Box 2. Exit Rates (CGAP formula)

However the branch manager’s role is to mitigate this bias and to stimulate learning to improve practice.The data from the interviews is inputted by the branch administrative assistant and then consolidatedwith the MIS data. A simple analysis (the main profiles and trends) can be done at the branch level. Thedataset is sent to the head of fice for more sophisticated analysis8.

How is the data used? Upon completion of the analysis the findings are communicated to different

information users within the organization. Prizma EMS has three main types of analysis andreporting:

• Head of fice reporting – provides the rates, main drop-out profiles (as in box No. 1), branch

comparisons and a trend analysis. Some more detailed segmentation analyses can be run to

understand better the composition of the main groups.

• Branch learning – monthly branch staff meetings during the data-collection period to discuss the

main reasons for drop-out at the branch level and potential improvement in operations.

• Ad-hoc data mining - detailed analysis on the whole drop-out database linked to MIS to respond to

some detailed questions to serve strategic business planning.

There are three important guidelines to developing the EMS:

• It must respond to the internal needs of an institution. The management has to be sure that this

is the right moment to learn more about drop-outs. If this need is not identified it does make sense

to start development, as only some costs will be incurred and the design will be abandoned sooner

or later.

• It should be developed in a participatory way involving staff from different levels and

departments. As you will see in the text below, it might have potential uses for different users. It is

important to identify the priority needs as they should drive the design of the system. In addition,

participatory discussion meetings are the best way to get staff buy-in.

• The approach to design must be holistic. The inter-disciplinary task force should consider each of

the following questions:

• WHY do you want to investigate drop-outs?

• WHAT do you want to learn from drop-outs?• FOR WHOM will the information be useful?

• HOW would you like to monitor drop-out?

• BY WHOM the data will be collected, analyzed and used?

• HOW OFTEN do you need this information?

• WHEN do you want to start doing it?

These questions guide the design phase. A thorough understanding of these issues is crucial to developinguser-friendly EMS.

As always in development work the following steps should be followed: 1) identifying the need;

2) designing; 3) testing; 4) revising; 5) rolling out. If the testing phase is omitted the costs of any

modification in all branches might be very high. If the system is not yet optimal, all your staff may

be disappointed with the process and results. It would be very dif ficult to persuade them that the

revised system will work better. As the Prizma example shows, a pilot test contributes to further

increasing benefits and reducing costs of the EMS.

The following innovative design features make Prizma’s EMS a cost-effective way of monitoringand learning from drop-outs:

• Semi-structured interview design with in-depth probing – allows better understanding of drop-

out issues through the dissecting of a wide range of exit reasons and prioritizing their importance for

the exiting client. The format of semi-structured conversation builds a relationship with ex-clients

and makes it possible to encourage them to come back.

What Does It Take

to Design It?

Innovative Design

Features



6



• Investigating not only the exit reason but also current financial behavior – enriches the analysis

and makes the findings more reliable. Interviewing the drop-out three months after repayment of the

last loan makes it possible to gain a better understanding of the real exit reason.

• Identifying the five main drop-out profiles9 that are important in terms of MFI operations

– limits burdensome analyses to a manageable and still useful minimum.

• Simple trend analysis of main profi

les by head of fi

ce – allows for the identifi

cation of changes inthe magnitude and general composition of drop-outs over time, which is useful for the head of fice and

branch management in determining the impact of various changes in the MFI and its environment on

clients’ behavior.

• Using existing MIS variables for further segmentation – allows for the detailed identification of

drop-out groups that are strategically important for Prizma in order to further research their needs,

adjust MFI services and focus other efforts to retain them.

• Regular meetings at the branch level – improves understanding of client problems by the branch

team, enhances team work to improve their branch performance and helps the branch manager to

identify some operational defects.

• Developing and doing it internally – opens up a new opportunity for field-staff learning and

promotion of a client-focused culture. If well stimulated the development and application processes

can contribute to field staff learning, changing their attitudes and perspectives, leading therefore to

better use of learning from clients and improving customer service.

Based on the MIS data, we can analyze who is leaving, what the magnitude is, and if this group isstrategically important for an institution. The added value of the EMS is that it explores the exit reasons.The Prizma staff identified the following potential uses of EMS data:

• Improving the retention rates through better understanding of desertion problems, especially with

regards to the distinction between those dissatisfied and “sleepers” (probably different incentives

must be applied to both groups to retain them);

• Learning in a constant way about the quality of service, identifying key operation areas to be

improved, and adapting services and products to changing markets;

• Monitoring how many clients are lost to competition that will prompt learning from competition and

developing competition survival strategies;

• Developing exit prevention (and loyalty building) strategies, adjusting promotion and marketing, and

improving products by adapting them for the target groups (especially those dropouts classified as“sleepers”);

• Being more realistic in designing a retention bonus system (e.g. rate of drop-outs living due to

external reasons cannot be higher than the client exit performance target);

Core Benefits:

How is Prizma

Using the EMS?

• If well introduced and managed the EMS provides very important information for

an institution operating on a competitive market.

• Timing of data collection should be adjusted to seasonal fluctuations of the level of effort

of LOs and well coordinated with other additional projects.

• The costs are relatively high and the ways to reduce them must be identified.

• Communication tofi

eld staff is a key. The staff must understand the process, benefi

tsfor them and for the institution before the proper training in the tool is conducted.

• Close branch manager monitoring is important for: supervising data quality, standardizing

coding activities, promoting probing and discouraging prompting, and stimulating

learning at the branch level.

• LOs are (even if they do not consider themselves) the right persons to conduct

the interviews because they know how to talk to clients.

• Good communication and successful change management are key to achieving high data

quality.

• There must be additional stimulation of learning among the LOs by branch manager.

LOs are too busy to analyze the data and benefit directly from the results of their work.

• You can get reliable information over the phone.

• Ex-clients are not especially dif ficult to interview. If the right approach is in place and

vinterviewers are well prepared they are not different than any other respondents.• It is true that ex-clients are dif ficult to identify (travel, etc.) and the help of branch

administration is necessary.

Box 4: Key Learning Points from the Pilot Test and Launch in AllBranches



7



• Allowing field workers to be more effective in reaching their drop-out performance targets, and

branch managers to understand what are their LO support needs in client exit management;

• Adjusting staff incentive systems with a better understanding of the magnitude of Prizma-related

dropout;

• Comparing branches in terms of retention performance in order to create an effective cross-branch

learning network;

• Giving a stronger decision base for head of fice and branches (supported by figures and

understanding);

• Informing about geographic expansion potential and supporting more realistic planning and

projections in the business plan (at head of fice and branch levels);

• Strengthening communication to clients about new services offered by Prizma.

If the right systems, especially MIS, are in place, there is therefore no need to redesign them, andthe costs of designing the system are low. It requires about 4-5 staff meetings and a championdevoted to preparing materials.

Box 5: Reducing Costs and Increasing Benefits

Recommendations from the pilot test in Mostar branch, roll-out in all branches, and discussionswith key staff.

Reducing costs:

• Limiting number of questions in the EMS form – 3-4 questions were found less useful and not

necessary to ask in the routine monitoring. The form has been revised.

• Involving administrative branch staff in preparing lists of drop-outs, updating contact

information, and making contacts with dif ficult to reach drop-outs.

• Explaining well to the staff of rationale and benefits of EMS.

• Emphasizing the introductory part of the interview and the ways how it can be easily done.

• Detailed training in interviewing skills and close assistance in the beginning to reduce

psychological costs.

• Enhancing teamwork among LOs and other branch staff

• Limiting monitoring frequency to necessary minimum (1-2 a year) while keeping it as

institutionalized system.• Simple data analysis done internally (trends of different profiles); more sophisticated analyses as

more detailed segmentation can be outsourced when needed.

• Timing of data collection adapted to downs and peaks of demand and overload with other

projects.

Increasing benefits:

• Facilitating field staff learning by organizing periodic meetings at the branch level to discuss

the results of routine monitoring and get staff feedback on the ways the services and policies

could be improved.

• Close monitoring to collect high-quality data.

• Clear reporting formats with simple information; and periodic reports with more detailed

information.

• Including “drop-out management” in the operations manual (likewise delinquency management

is). The staff is appraised by drop-out as they are appraised by delinquency. But in the operationsmanual there is nothing like drop-out management. That is why the field staff cannot connect

the dots and learn effectively from the EMS. This change can also contribute to gain another

perspective – the field staff will not be focused only on productivity and delinquency but also

on loyalty and desertion.

• The field staff should see their benefits. Middle management must ensure discussion of the

results with the field staff and show them how operational and strategic decisions assisting them

in their work are based on the EMS data they collect.

• Staff must be well trained in standardized questioning andfinal drop-out classification (last table

in the EMS form).

• There should be an intensive collection period. One data collection cannot be extended over

a very long period of time. If it is, then some changes introduced in the meantime can distort

EMS data analysis. Literally, drop-outs from January and from July can be drop-outs of two

“different” institutions. In order to avoid the distortions (putting different drop-outs in the same

basket) it must be ensured that one EMS dataset does not include drop-outs from more than 3to 4 following months.

• There must be a policy for an MIS of ficer to make all necessary drop-out data available (all MIS

variables and credit history for all drop-outs) in a timely way.

How MuchDoes it

Cost?



8



Costs of monitoring are as follows:

• Financial and physical costs are low: telephone calls, photocopies, etc.

• Staff opportunity costs are relatively high. Each LO had to interview 6-9 drop-outs per month over

half a year. The interview lasts 15-20 minutes, which distracts them from their daily duties and

creates added stress.• Staff psychological costs are relatively high in the beginning. The field staff are afraid to interview

“dif ficult” drop-outs as it is stressful and influences their work performance. However, as it was

observed by branch managers the psychological costs should be lower while the field staff get used

to the added task.

In general, Prizma perceives benefits to be higher than costs and plans to continue its efforts to monitorexiting clients. However, the EMS has just been implemented and there is still big scope to making itmore cost-effective when considering the following issues:

• Effective use of exit information in the decision-making processes - the exit monitoring

data is a new source of managerial information for microfinance practitioners. Prizma

management is aware that it must be handled with care as there are no blueprints on how

to use it. Detailed knowledge about the market and its evolution is needed to classify

the exit information that is important to act on versus the information that is of less

operational use for an MFI. As a rule of thumb, further work should be undertaken only

on these drop-out segments that are significant for an MFI in terms of realizing its social

objectives or achieving financial goals. In other words, it does not make sense to change

anything for a specific group of drop-outs tha t corresponds to a negligible share of an MFIs’

current number of clients.

• Requirement for good interviewing skills and analysis unit – an important requirement for

the EMS. The field staff must have interviewing and some analytical ski lls. It must be decided

individually which of LOs need more training and supervision. The branch managers must

be committed to supervising the monitoring correctly. Otherwise it is difficult to collect

high-quality data. This can result in misleading results and bad decisions. Last but not

least, there must be a focal analytical point at the organizational level. Its role will be to

combine data from different sources (including unstructured staff knowledge), includingexit monitoring and any other primary research, to provide insightful reports to the

management.

• Identifying an optimal sampling plan – up to now Prizma has randomly sampled drop-outs

without any specific targeting based on organizational needs. It is widely acknowledged

at Prizma that interviewing some groups of drop-outs does not provide reliable and useful

information (e.g. forced out drop-outs, some interviews with basic needs clients). MIS

can be used more effectively to do preliminary analyzes of drop-outs and segment them

by demographic and product use history variables10. If this knowledge is combined with

market trends and strategic priorities it should allow the identification of more specific

groups of drop-outs to explore their exit reasons. Even if it does not give an overview

of the underlying reasons for the client exit problem at the organizational level it can give

more focused and easier-to-use information to management, making the whole EMS more

cost-effective. Additionally, exit monitoring on both general and specific samples canbe combined in a way that the former is conducted less frequently and the latter is done

whenever there is a need (can be i nitiated even at the bra nch level).

• Promoting multi-level benefits from EMS – one of biggest challenges is to make sure

that the information reaches the right people who are able to use it. It concerns stimulating

field staff learning, empowering middle managers a nd other operations staff, and providing

top management with reliable client feedback. The EMS might be used as a collective

learni ng vehicle for all internal sta keholders. If it is stimulated (e.g. in a form of workshops)

it can allow LOs and managers to share their experiences across branches linking them to

the overall organizat ional strategy, therefore producing useful outputs at all levels regarding

improving client retention.

• Standardizing drop-out classification – the most demanding task dur ing the exit interview

is to classify drop-outs into two main profiles (questions a9 in the form attached). In order

to aggregate the results there is a need for strong standardization across interviewers.

Issues in

Improving EMS



9



• Identifying low performance of field staff as a reason for exit – the current EMS design

does not allow for the identification of dissatisfaction with staff performance or attitude

as a reason for client exit. Even if LOs do not interview their own drop-outs there is

a natural collusion among them that demotivates them to probe further on these delicate

issues. It is widely acknowledged that customer service is an import ant element in buildinglasting relationships with clients. Other mechanisms should be put in place to control field

staff performance.

• Capturing seasonality of exit reasons – during the action-research project the exit

data collection was conducted twice in the Mostar branch in different periods

(March-May; December-February). The fact that findi ngs were significantly different supports

the seasonal nature of the client exit problem. The reasons for leaving will differ to

a certa in extent in different seasons. Therefore, if data for a trend a nalysis is not conducted

over the same months the results will be distorted by seasonal differences. Incorporating

staff feedback about understanding seasonal differences in the analysis phase is of utmost

importance.




ANNEX – PRIZMA’S SEMI-STRUCTURED EXIT MONITORING FORM (REVISED):

X1. Client ID X2. Client name X3. Telephone Number X4. Interviewer Name

(This form is supposed to be administered by loan of fi cers guided by core questions. The form supports an informal telephone interview with a use of the probing techniques. Nor the questions or pre-coded answers should be used to prompt answers from the respondents.)INTRODUCTION: Hello! I am calling from Prizma – microcredit organization. In Prizma, we alwayswant to improve our services to adjust them to our clients’ needs. We are now in the process of callingactive and former Prizma clients to learn about your opinion on Prizma and its services in order tobetter tailor our offer to your needs and preferences. I would appreciate if you can devote 10 minutes ofyour precious time. I would like to emphasize that all replies will be treated in strictest confidence. Theanswers provided by you will be reported in general statistical tables. They will never influence any ofyour applications to Prizma. Do you agree?(encourage, but if not willing ask when you can call again)WARM UP QUESTIONS:

Do you remember Prizma? With who have you worked in Prizma? How was it? How did Prizma credit helpyou? How is your business? Have you constructed the house? etc.

A-1. What are 2 things that you did not like the mostabout Prizma and its products and services? (If you could improve Prizma services what two things will be priority?)

Do not prompt! If dif fi culty in responding. You can mention broad categories: promotion, place, people,product, price, process…

A-2. Which of the external reasons (not linked toPrizma services) discouraged you to takeanother loan?

(multiple responses possible)

Ask the question, if no response list possible reasons.Even if the respondent mentions spontaneously the reasons probe delicately on other listed reasons

1. [_] loan size2. [_ ] repayment period

3. [_] repayment frequency4. [_ ] instalment size5. [_] collateral6. [_] guarantors7. [_] eligibility requirements (access)8. [_] group methodology policies9. [_ ] interest rate level

10. [_ ] application fees11. [_] penalty system12. [_ ] staff professionalism13. [_ ] approach to client14. [_] staff flexibility15. [_ ] handling of non payment16. [_ ] application process (incl. simplicity of forms)17. [_] waiting time between application and disbursement18. [_ ] way of loan repayment19. [_] office location20. [_] office opening hours & days21. [ _] promotion and communication channels used22. [_] incentives for loyal clients23. [_ ] range of products24. [_ ] availability of other than credit products25. [_] difficulty in getting money back from other group

members

OTHER:

40. [_] seasonality41. [_] business/household has no need for further

financing42. [_] other financing from informal sources43. [_] approval of credit in formal institution44. [_] closing down the business45. [_] lack of market demand46. [_] other business problems47. [_] family health problems48. [_] travel / migration49. [_ ] other personal / family reasons

OTHER:

A-3. Do you feel that problems in repayment (yoursor other group member) influenced the fact thatyou have not taken the next loan in Prizma?

It is not a question if the respondent had delays butrather if the delays caused her desertion.Look in the repayment history and probe further if youfeel the respondent is not honest.

60. [_] No (go to question X6 / A4)

61. [_] Yes, because of my repayment problems

62. [_] Yes, because of other group member repaymentproblems


10 www.mfc.org.pl/research/




X5. CHECK if all the reasons mentioned in X5 are reflected in A1, A2, A3. If not get back to relevant questionand probe. If there is no space to put the respondent answer put it here:100. [_] _________________ _____________

A-4. SUMMARY – decide together with respondent which are the 2 most important reasons (from A1,

A2, A3, X6) why she has not taken the next loan:(put code and description; use the same codes as in questions A1, A2, A3, X6)

1. [ ] _________ __________ _________ ___ 2. [ ] _________ _________ _____ ________

A-5. Which other financial services are you and your household members using now (are about to use)?(this question is totally independent of Prizma)

99. [_ ] I do not use any financial services

1. [_ ] Enterprise credit (incl. supplier credit) 2. [_] Leasing 3. [_] Housing credit4. [_] Consumer credit (incl. hire purchase agencies / department store credit)

A-6. Which institutions are sources of the above (A-5) mentioned loans?(after spontaneous answer list all sources and probe carefully if the respondent or any of her householdmembers is using (is about to use)any of the credit services)

1. [_ ]Family / friends

2. [_]Suppliers

3.[_]Moneylenders(loan sharks,private persons)

4. [_ ]Hire purchaseagencies

5. [_ ]Other MCOs

6. [_ ]Banks

A7. Do you plan to take another loan in Prizma? 1. [_ ] Definitely No (go to question A8)2. [_] Probably No (go to question A8)3. [_] Probably Yes4. [_] Definitely Yes

A- 8. SUGGESTIONS: What will encourage you to take another loan from Prizma? Other suggestions?

……………………………………………………………………………………………………………………………………………

A-9. For interviewer only: please classify a respondent as a drop-out who: (select one statement in each column A-9aand A-9b)

Thanks!

A-9a A-9b

was voluntary and satisfied(reasons not related to Prizma) 1. [_]

does not have any need for creditservices and will not come back toPrizma in a near future

1. [_]

was voluntary and dissatisfied(Prizma related reasons)

2. [_]is not using now any other creditservices and plans to come back toPrizma in near future (“sleeper”)

2. [_]

was forced out by Prizma 3. [_]will come back to Prizma only if services will be improved / expanded(can use or not other services)

3. [_]

is using now credit services of competition and do not plan to comeback to Prizma

4. [_]

11 www.mfc.org.pl/research/




This paper was published with the support of the Open Society Institute.



12

Documents

Spotlight 10