Mar 15, 2014

TulaLens: the Constant Editor 

We are dedicated to reshaping our approach until our end users, the communities in developing countries that we are partnering with, use and believe in the product we develop.

Over the past two months, TulaLens has been conducting market research in Chennai, India. Here's a quick overview. We're in the process of surveying 100 people living in the slums of Chennai to better understand their needs and behaviors. For now, we are only focusing on products they use, not services. Of those surveyed, 48% are males; 52% are females. Their average monthly household income is 12,453 rupees = $208 USD, and average household size is 4 people. 77% of the people we spoke with said they could read. In addition to the market research, we've had the amazing opportunity to participate in the Ashoka Feedback Labs competition. During this time, we've had several light bulb moments that have allowed us to start the editing process. We'd like to share these with you.

1) What types of products do we want to rate?

We initially thought that we would be able to go directly to the poor and garner their feedback on products they use to improve their lives. When we asked them to rate a product they use, they often mentioned daily household products such as vegetables, shampoo, and soap. Sometimes they mentioned products such as cigarettes. We want to focus on products that could potentially have the most impact on the lives of the poor. This approach has not been useful in collecting feedback on those types of products.

2) How do we identify and categorize the products by the organizations that developed them?

In order for people to be able to retrieve rating information on products in their area, we need to classify all products that are rated. Collecting the bar code on a product would allow us to do this. However, only 8% of the people we interviewed were able to provide bar code information to us. Interviewees provided us feedback on a product, but 78% did not know what organization developed this product.

3) Will low-income communities find this system useful?

We initially envisioned an SMS-based system as the best approach. Well, we were very wrong! 50% of the people we interviewed said that the SMS-based system we proposed would help to improve their lives. 27% said that such a system would be useful, but they did not know how to read or they did not feel comfortable using SMS. 16% said they did not have a cell. 7% said such a system would not be useful. Our aim is to develop a system that the poor can use despite literacy level, language, etc. There are other mobile technologies that may be more suitable in achieving this aim. We have ruled out other technologies because only 10% of the people we spoke to said they owned a computer, and 8% said they had internet access. On the other hand, 78% owned a mobile phone.

4) Do the poor truly need a way to provide feedback and get high-quality information?

Absolutely! 65% of the people we spoke to said they currently have no way to provide feedback on any products they use. 10% said they provided feedback through shops, another 10% said they provided feedback through customer service centers, and the remaining people said they provided feedback that did not result in any change.

70% of the poor currently get information on the products they use through TV, 30% through friends, and 30% through newspapers. This means that the majority of the people we spoke with are purchasing products based on advertisements rather than high-quality information.

5) How do we get the poor to use TulaLens?

Heather Franzese at Good World Solutions provided us with excellent feedback during the Ashoka competition. Her organization uses an interactive voice response technology to survey workers in developing countries. They then use this information to pressure companies to improve working conditions. Good World Solutions found that pushing information and asking for feedback was less successful than marketing the technology and allowing people to initiate feedback on their own.

Based on these key findings, we'd like to share the following lessons learned:

Lesson #1: Partner with organizations. TulaLens can provide the technology and associated services for organizations to understand their users needs. If we partner with organizations - NGOs, social enterprises, companies - that share the TulaLens vision, we will be able to resolve several of the challenges I've illustrated above. Partnering will allow us to focus on products that have a large impact on the lives of the poor, to easily classify products without a bar code, and to help transform the way that organizations approach poverty alleviation. Our aim is for these organizations to make the user-centered approach a part of their DNA.

Lesson #2: Use a mobile technology that even the most marginalized can access. We are still exploring technology options such as interactive voice response, and smart-phone based applications that community leaders can use to collect and provide feedback. We'd love to hear more about the successes and challenges your organization has faced when using different technologies.

Lesson #3: It's time to start a pilot. Market research has been helpful in refining our idea. However, we're now ready for the next step. End users may tell us that they're interested in such a system, but learning from actual behaviors is always more useful.

If you have any feedback on TulaLens, we're very eager to hear. Please feel free to start a discussion below!!