no image

Using Customer Data to Fine-Tune Product Design and Marketing Strategy >

emilyhosoya
•06/29/12
• 0 Comments

By Emily Hosoya

Since starting our work with CARD Bank in the Philippines, we’ve realized the best savings products are designed by the customers themselves. Although we’d love to sit down with each of CARD’s 500,000+ savings customers to discuss their needs, there is never enough time or resources to do so. Instead, with the help of our senior data analyst, Jacobo Menajovsky, we’ve created a process to use specific customer information to address our business questions and drive CARD’s product design and marketing strategy.

It can get overwhelming to sort through data without a clear approach. Over the past year, we developed a process to sift through customer information to cluster customers into manageable segments. This process allows us to better learn about their needs and analyze their savings habits.

Along with our Progress out of Poverty Index , a tool that uses country-specific indicators to predict a given household’s likelihood of poverty, we looked at CARD’s demographic and financial data to cluster customer types. In addition to poverty level, the most predictive variables we found in the clustering process included family size, education level and employment status.

Simplifying large amounts of customer data allows us to predict product successes among different customer groups. By forecasting behavior by group, CARD can effectively make small changes to individual products, such as with minimum balance amounts and delivery channels, and even larger changes to its offerings portfolio as a whole.

Just as important to designing better savings products, the bank can also fine-tune their marketing and cross-selling strategies. Having a clear distinction between customer types enables us to tailor savings solutions to appropriately meet customer needs.

It’s easy to get bogged down with vast amounts of customer information, but you have to start somewhere. As an iterative process, we suggest using sophisticated data models like the PPI to capture poverty levels, statistical software to process the data, a dashboard for analyzing data trends, and most importantly, clear research questions to answer from the data.

For more information on our approach, and how you can use data to help answer important business questions, see our case study titled Information as Power: Implementing Data Analytics at CARD Bank.

Emily Hosoya is interning with the Microsavings Initiative, a project at Grameen Foundation developing savings programs with microfinance institutions.

0 Comments

Comments