On May 26th we invited some representatives from the press to the launch of the next phase of our Mobenzi pilot project in KwaNyuswa (Valley of a thousand hills, KZN).

During our two week trial run in December 2009, agents had completed Mobenzi tasks using our company owned phones, under supervision and together at a central location. Since May 26th however, a group of agents have been working independently as private contractors to Mobenzi.

These are some of the major factors that make the launch of this phase of the pilot a significant step forward.

1. Agents are now working in their own time, requesting batches of tasks whenever they have a few minutes spare.
2. They complete tasks while at home, travelling on public transport or even between lectures at college.
3. Most of the agents are using their own mobile phones after having installed the Mobenzi application from a link we sent to them.
4. With each task that agent’s complete, associated credit is built up in their account. Once credit reaches a certain thresh-hold, funds are disbursed electronically to their phones using FNB’s SendMoney platform. Although some agents had to borrow our company phones, many have already earned enough income from Mobenzi to purchase their own, brand new compatible Nokia phones.

These changes in the way the pilot is being run are allowing us to test the scalability of the concept. We can now manage recruitment of new agents, assignment of tasks, monitoring of quality and disbursement of funds all from our central office.

With this platform in place, it is only the demand from businesses for the services of our agents that will slow the growth of Mobenzi.

Traditionally, when it comes to gathering data via SMS, researchers have two rather ungainly options if they wish to collect anything more than the simplest one word response:

1. Require that respondents structure their feedback in some way (e.g. comma delimited) or;
2. Solicit answers one SMS at a time, with responses still needing to be confined to a reasonably limited vocabulary.

But what if you could ask a question (or several questions) in an advert, SMS or display and allow respondents to answer in free text – but still be able to analyse the data using quantitative techniques? That’s precisely what SMS Insight offers: the ability to derive structured results from free text responses. Here’s how:

1. Pose the question

Your message can be communicated via SMS, on TV or radio, in print ads or outdoor displays, on product packaging etc.

e.g. An advert asks: “What would you change about our company? SMS your name with your ideas to 35xxx”

2. Solicit feedback in plain text

Respondents simply reply via SMS – in their own words – with no fixed formats, keywords or codes required.

e.g. “I wish your staff would be more friendly. Niki”

3. Use human intelligence to crunch the responses

Our panel of mobile workers use their innate human ability to structure, classify and quantify each response by answering simple questions based on your exact analysis requirements.

e.g. “Is the response about our products, prices or service?”; “What is the respondent’s name and if possible, their gender?”

4. Integrate and act on the results

What was once raw, unusable SMS data is transformed into rich, structured information that can be used to inform business strategy, evaluate performance and identify opportunities.

e.g. “14% Products, 12% Price, 74% Service”

Interested in conducting research using SMS Insight? Please get in touch with us to discuss your requirements.

About Mobenzi

Mobenzi is a software service that empowers people to be rewarded for completing simple tasks on their mobile phones. These tasks involve certain types of problems that are difficult for a computer to solve without assistance from a real person – even someone without expert knowledge of the problem.

Find out more about how Mobenzi works

Purpose of the pilot project

For two weeks we equipped pilot participants with the Mobenzi software application installed on standard mobile phones to assess whether they could effectively complete simple business tasks using only their phones.

These were some of the guiding questions we were attempting to answer during the pilot.

• Is the concept easy to understand?
• Is the technology easy to use?
• What types of tasks are feasible?
• What types of people are most suitable for doing Mobenzi tasks?
• What is the best way to present a given task to an agent?
• How long does it take to complete different types of tasks?
• What quality should be expected in the results of completed tasks?
• What issues are involved that may affect attrition rates (fatigue, boredom etc)?
• Could the service grow through viral expansion (Can participants teach each other)?
• Based on other findings, what are the financial implications with regard to agent remuneration and the cost of the service to organisations?

Project location and venue

We ran the pilot project from the Light Providers community centre in KwaNyuswa. The area lies on the outskirts of urban development, west of the Inanda Dam, about 40 minutes outside of Durban in KwaZulu Natal, South Africa. It is one of the largest of the various tribal authorities that make up the Valley of a Thousand Hills.

Due to the gross unemployment rates in the region, and our close proximity to the area (Only 14km from our office), we selected KwaNyuswa as the location for our pilot project.

Format of the pilot

We started the first week of the pilot with 5 participants who would later act as mentors when 20 new recruits joined them for the second week. We spent the first week testing out various types of human intelligence tasks and discussing issues surrounding understanding the use of the mobile application as well as the various types of tasks themselves.

During the second week we had more participants to help work through large sets of tasks. We assigned participants various types of tasks and recorded completion times and responses for all participants so that we could crunch the data to assess what factors affect quality and efficiency.

We focused on text-based human intelligence tasks

We decided to focus on “Text to Form” tasks for the pilot project. These types of tasks involve extracting structured data from free-text.

Some examples of this type of task include:

• Categorising SMS survey responses into reportable data.
• Sentiment Rating of “Tweets” (Messages on Twitter).
• Classifying text based job and product advertisements.

For all of these tasks, we displayed a short instruction for the task, followed by the content (such as an SMS or a tweet) and then a series of questions about the content (Such as whether the SMS included a person’s name). The participant worked through each task one step at a time.

Find out about other types of human intelligence tasks

Results of the first phase of our pilot project

One of the critical factors affecting the feasibility of Mobenzi is whether or not the mobile application is easy to use for people who have had little exposure to the internet and other software applications. A quote from the summary of the first day of the pilot shows how easily the participants understood both the concept of doing work on their phones as well as how to use the application itself:

Without any instruction, most of the participants had the application open and simply started completing tasks. Although I had high expectations, I still thought there would be many questions and a fairly slow start. But within half an hour of me arriving at the venue, the participants had their heads down and were completing tasks. A few questions popped up during the day, but none that the other participants couldn’t answer themselves.

Using the software to complete tasks came very naturally and required almost zero training. From the participant comments, it is also clear that there would be a huge demand for Mobenzi tasks. I believe we could easily find thousands of Mobenzi agents who already own compatible phones within just half an hour’s drive of our offices in Hillcrest, let alone the rest of South Africa and the world.

We have not yet done much analysis on the quality or efficiency of the completed tasks, but initial assessments are very positive. Over the next few weeks we will be crunching the data to help answer some more of the questions we outlined at the start of the pilot.

The results so far have exceeded our expectations and at this stage I would guess that our biggest challenge in moving forward will be to generate a sufficient supply of tasks to keep Mobenzi agents busy.

Scaling up the pilot in April 2010

This pilot was a short 2 week project to get an early feel for what to expect. In April next year we will scale our efforts up and take on a much larger group of participants to pilot the concept further. Until then we will be tweaking the software and preparing the systems to handle the logistics of a much larger project.

We are very open to suggestions if you have any ideas for types of tasks or even real world data that we could get Mobenzi agents to process during our pilot later this year.