Our first pilot project for Mobenzi ended on December 4^th 2009 and on the final afternoon we assigned a survey to the participants’ phones to find out information about them as well as their thoughts on the pilot.

Although we had 25 participants in the pilot, 2 members of the team were not present on the Friday afternoon. The following statistics are therefore based on the remaining 23 team members who completed the self-administered survey using the Nokia 3120 mobile phones we provided for the pilot.

Age, Gender and Language

The 25 pilot participants were all from the local community of Kwanyuswa. The average age of the team members was 24 and there was an even gender split. Each of the participants had completed grade 12 and could speak fairly good English. Their first language is isiZulu but each of them studied English as a second language.

Employment history

17 of the participants (70%) had never had a full time job at the time of running the pilot. A few participants had part time jobs but were able to make the 5 hour sessions each morning.

Household information

The 17 participants that were willing to answer questions about their households have on average 7 people living permanently at home. 16 homes had stoves (94%), 14 had running water (82%), 14 had a television (82%), only 10 owned a fridge (60%) and none of the households owned a motor vehicle.

Mobile phone usage

19 of the 23 participants (82%) owned their own mobile phone (53% Nokia, 21% Samsung, 16% LG). Most participants (60%) had used MXIT (a mobile instant messaging client) in the month preceding the pilot. 9 team members (40%) had used their phones within the last month to browse the web and download pictures, music or games. The average airtime expenditure per person over the preceding 3 months was R100 per month.

Demand for mobile tasks

If employed full time in another position, the participants expressed on average that they would probably like to do Mobenzi tasks for about 3.5 hours per weekday to subsidise other income. If working only part time in another position, the desired commitment increased to 5.5 hours. Over weekends the average expected commitment was 10 hours (Including Saturday and Sunday). This works out at between 27 and 37 hours per week. 5.5 hours of concentrated work is probably the ceiling for how much time someone could spend doing Mobenzi tasks in a single day.

Everyone agreed that most Mobenzi tasks would be completed at their homes, but most participants also mentioned they would probably complete tasks while on public transport (buses and taxis) and while walking around the local community.

Thoughts on Mobenzi

The major reason the participants noted for what they liked about Mobenzi was that the work was interesting and entertaining. Only one person answered that the work was boring. The biggest challenge the team raised was that some classification tasks were ambiguous and deciding on the most appropriate answer was sometimes very difficult.

Fatigue was a problem for some participants who mentioned that their hands started hurting by the end of the day or they battled to concentrate for so long (We ran the pilot for about 5 hours each day with short breaks every hour and a longer break for lunch).

The participants were generally very excited about Mobenzi. Some of their comments are included in a related article: Feedback from pilot participants about mobile tasks

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.