1. Client targeting and social performance data: Comparing FINCA and Fonkoze

2. Objective & Outcomes How to practically apply understanding of customer insights, derived from social performance data, to better target and improve our products

3. FINCA still focuses on itself more than clients… Product-driven Market-driven Our wonderful product will sell itselfAll we have to do is sell the product hard We will create a product that the market demands We will understand what the market demands FINCA needs to target X groupWe know X group, and we have a product to improve their lives

4. Marketing Fundamentals Segmentation Identify your customers by a specific set of common traits. Marketing Mix Provide a product or service on terms that are tailored to the demands of each segment. 4 2. Collect client and enterprise data 3. Analysis 1. Identify differentiating criteria Physical Evidence

5. Criteria for ideal market segments Measurable Large enough to earn profit. Stable enough that it does not vanish after some time. Possible to reach potential customer via organization's promotion and distribution channel. Internally homogeneous (potential customers in the same segment prefer the same product qualities). Externally heterogeneous that is Heterogeneity between segments (potential customers from different segments have basically different quality preferences). Responds similarly to a market stimulus. Cost-efficiently reached by market intervention. Useful in deciding on marketing mix

6. Basic Microfinance Segmentation Source: KIVA

7. A more detailed segmentation: Mexico Source:CGAP

8. How does FINCA segment? Wants to segment by PRODUCT Is segmenting by CHANCE Should be segmenting based on meeting NEEDS OF CLIENT Social mission means finding those segments that have traditionally been excluded from formal finance

9. Business Individual Lending RURAL SEMI URBAN URBAN Level of entrepreneurial sophistication FINCA Product Mix? Village Banking Agricultural Small Group Lending Agricultural Individual Lending Business Small Group Lending

10. Fonkoze as an example

11. Fonkoze’s Staircase out of Poverty “Road to a Better Life” “Small Credit” “Poorest of the Poor” Are headed by women with multiple children; No income-generating assets, no children in s chool, no reliable access to food or healthcare “Near-extreme poverty” “for women who are not yet ready to manage a loan as large as $75”, “too poor to succeed in traditional microfinance programs, “ Fonkoze’s core program “available to women who already have a good commerce or business.” “perfect program for ti machann ….Solidarity l ending program for a lo ng time…have been in creasing and repaying t heir loan amounts well …perhaps in the formal economy”

12. A FINCA staircase?

13. VB vs SGL

14. Ask: Who are our clients? Social performance data gives us rich data set to analyse our clients So…let’s see how well we could this data for segmentation purposes. We start by testing our basic assumptions about the characteristics of our clients…. The goal is to determine simple defining criteria to differentiate our clients into target groups

15. Common assumptions in microfinance Village banking is best for poor women living in villages Large group products do not work well in urban areas The poorer the client, the smaller the loan they will receive Clients are using loans exactly for the original purpose that they specified in their loan application People in rural areas tend to be poorer than those living in urban areas Microfinance increases client income (so those who have been with FINCA for longest are better off than those who have not) Individual loan clients are richer than SGL clients, are richer than village banking clients Individual loans are bigger than SGL, are bigger than VB The destitute cannot benefit from financial services => our ability to help them is limited and unprofitable

16. Fonkoze Poverty Scores Source: Fonkoze (using PPI)

17. Loan size by Daily Per-capita Expenditure (Kyrgyzstan, local currency) Loan Size % ofClients by daily per-capitaexpendituer level

18. Profile-based segmentation Areas that are often segmented by profiles include: Age Gender Geographic location Time with FINCA / loan cycle Type of business Income / expenditure levels

19. A simple approach to segmentation 1.Identify clusters 2.Differentiate clusters according to characteristics

20. Example: Segmentation by occupation

21. How do our clients compare to the wider picture? Compare to external data sources Census data (local statistical agencies often collect significant & relevant data) MIX Market (source of microfinance data), CGAP, local microfinance bodies World Bank (Open Data) There is a LOT of data out there! Related to efforts to geographicallly map data

22. Example of information Income / poverty statistics by department, Guatemala

23. Steps to Segmenting our Market 23 Preparation Validate marketing mix decisions to be taken Specify segmentation criteria Present “dummy report” to management Research Research Design (sampling, data collection) Review existing data sources Design any needed data collection tools Collect, clean and store data Analysis Descriptive analysis (tables) Relational analysis (regression: correlation, strength and significance) Repeat as necessary until you find cohesive data “lumps” Present findings to management Refine Marketing Mix Review product featuresRevise training / incentives Develop promotional strategy

24. Recommendations for action Begin segmentation using the data we have Research fellows will be producing reports based on social performance data for all countries Marketing personnel take this data and use it to perform simple target group differentiation Can be done with coordination with research fellows (adding to social performance report), or as an effort later this year Milan supports your effort, through tool development & data analysis