South Sudan: communicating both ways

South Sudan1

WFP/Hagar Ibrahim

We are back in South Sudan, where, in June, we identified two main areas of opportunity for employing a mobile Vulnerability Analysis and Mapping (mVAM) approach: using it to monitor urban food security and applying it to improve early warning systems.

This time, we are pleased to announce that the project is moving forward, we are collecting more and more numbers and are getting closer to piloting an Interactive Voice Response (IVR) system, which will both boost the capacity of our in-house call center and enable beneficiaries to access information and get answers to their questions.

South Sudan2

WFP/Hagar Ibrahim

The food security situation in urban areas in South Sudan has been deteriorating. According to WFP’s latest urban food security assessment in Bor town, 85% of households are food insecure (of which 44% are severely food insecure, and 41% moderately food insecure). As the urban food security situation needs to be monitored frequently and there is better mobile phone coverage in urban than in rural areas, mVAM is stepping in to collect the data.

Through face-to-face assessments and via our partner agencies on the ground, we have collected over 400 phone numbers and used some of them to conduct food security live call interviews with households in urban centers mostly across Greater Equatoria.

South Sudan map

WFP/Map 1: Number of surveyed households by county, September 2017

However, the context for conducting phone surveys in South Sudan continues to be challenging due to the low mobile phone penetration rates and connectivity problems. We had already reported last time that the main mobile network operators downsized their businesses due to recurrent conflicts. In our most recent round of phone surveys, we found that nearly 40% of the numbers were not reachable. Nevertheless, we were able to talk to over 240 households and ask them about their food consumption, negative coping behaviours, and the food security situation in their communities.

The goal of our latest mission was to provide technical support and assist with capacity building at our in-house call centre. We have configured an interactive voice response (IVR) system, a technology which allows users to access relevant information using the phone keypad and speech recognition. Through the pre-recorded voice response option, the system will be used to answer beneficiaries’ questions relating to, for example, the registration process, food distribution dates, and technical issues, such as lost or damaged vouchers. Users will also be able to record their questions, upon which WFP gets back to them. The IVR system can also initiate calls automatically and direct them to an operator only when a respondent picks up the phone, thereby saving the operators time. This will help address a challenge that mVAM operators in South Sudan have had to grapple with all this time.

The next steps for mVAM in South Sudan will involve deploying and improving the IVR system and expanding our contact information database of potential survey respondents with the help of WFP units and our cooperating partners in the field. Until the next time!

 

World Humanitarian Day 2017

Photo: WFP

Photo: WFP/ Regional Bureau of Cairo

To celebrate World Humanitarian Day 2017, this week we interviewed one of the humanitarians who makes mVAM possible. Hatem works as a data scientist in the Cairo Regional Bureau so we asked him more about his work – remotely monitoring food security in conflict zones in the Middle East.

1. Duty station: Regional Bureau of Cairo (RBC)

2. Job title: Data Scientist (VAM)

3. What does your job entail? My job is mainly focused on the data analysis, aggregation and visualization of the monthly mVAM food security surveys in L3 Countries (Yemen, Syria & Iraq). The process starts with the monthly data collection done by call centres or operators. I follow up with the call centres to make sure that the data they’ve collected is in good format and has minimal or no errors. I also make sure that they are following the sampling guidelines and methodologies designed by our team in headquarters. After that, I perform some data cleaning and validation before storing the data in our database. Then, I run some statistical tests on different variables so that I can understand what significant changes there are in the data compared to previous months. According to the analysis results, trends and statistical changes compared to previous months. According to the analysis results, trends and statistical tests, as well as secondary data/news, me and my team start to gather the most important/significant data and create a brief story that summarizes the food security situation in the country. The bulletin is usually 4-5 pages containing text narratives, charts, images and sometimes maps.  There is also usually a qualitative analysis part based on the open text comments of the respondents. It is usually an interesting yet challenging process to find new ways of visualizing open-ended comments from respondents (usually around 1,000-2,000 comments).

4. How does your work help WFP’s response in conflict zones? The mVAM bulletins provide up-to-date and almost real-time data about people that live in conflict zones who you can’t reach by any other means other than mobile phones. These bulletins inform the programme teams about their needs, the most vulnerable areas and the most vulnerable population groups such as displaced people. This ensures WFP is in a better and more informed position to take any programmatic decision on who is affected by conflict, where they are and how they can assist these people most effectively.

5. What’s the most challenging part of your job? Creating a full story from raw data. As a data scientist I usually face technical difficulties – whether it’s in the data cleaning, storage, or analysis code. However, the most challenging part is usually correlating all the data from mVAM and other sources to represent them in a meaningful and complete story that briefly describes the situation in a specific country.

6. What’s the most rewarding part of your job? Working in the humanitarian sector is very rewarding, even if it is not directly with beneficiaries. Not to mention working with data related to conflict zones where there are rapid changes and up-to-date data is in high demand. The fact that I’m a part of a process that makes other people’s lives better, especially those who are in serious need is, in itself, a huge drive to make me do what I do.

What we found at the market: using Free Basics in Malawi

FreeBasicsAd_Chichewa

We wrote to you back in November about one of our new innovations – our Free Basics website ‘Za Pamsika’ where we’re posting commodity prices using the weekly price data we’re collecting through our mVAM operators on a free website. We said that the project had the potential to reach millions of Malawians – well, a lot has happened since then.

Rather than continuing to willfully upload prices while watching our user statistics go up and down, we went to Malawi to carry out a short ground truth study and get some first hand user feedback.  The aim of the mission was to investigate the best way of using the website and interrogate the assumptions we’d made when designing it.

With this in mind, we tried to answer two big questions:

  1. Who can access our website – what are the potential barriers and how can we work around them?
  2. Do Malawians really want a website where they can find out maize and beans prices?

So we went to rural and urban markets in the Central and Southern regions to speak to the mVAM traders and the consumers in their markets about their mobile phone usage and market activity and to get their feedback on the website.

What kind of answers did we get?

First – access issues. While you don’t need a smartphone to access the website we knew that mobile penetration in Malawi is low. So we were most worried about the prevalence of internet-enabled phones and network coverage. From our study we found out that while we aren’t going to be able to reach everyone in Malawi via a website, we can still communicate with people. Network coverage was a problem in some areas. However, overall we found that most of the traders had internet enabled phones or wanted to buy one. We also found that Malawi’s MNOs have been recently trying to push out better network coverage. All good news for future reach of the website.

Actually the biggest barrier was language and literacy. While English is the national language of Malawi, most of the literate people we spoke to were much more comfortable reading and writing in Chichewa because that’s what they were taught in. While they were very enthusiastic about the website content when it was explained to them, they found the initial design (all in English and text heavy) confusing and difficult to use. Luckily this is an easy change to make so we did a quick redesign of the website and translated it into Chichewa:

malawiblog1

With our new design we headed back into the markets and got much better feedback. Rather than just saying that they liked the website content they could really interact with it and were making comments on the different maize and beans prices.

The second barrier we found was digital literacy. Many of the people we spoke to had internet-enabled phones but either didn’t know how to use them or didn’t even realise that they had the internet on them! Unlike the language change this is not a quick fix. This was particularly prominent amongst the women we spoke to, none of whom were comfortable with mobile internet. We’re therefore going to partner with civil society organisations promoting digital literacy. WFP has a network of partners and farmers on the ground who they reach out to with climate information so we’re going to try and use these focal points to communicate our prices with vulnerable populations and communities who have limited access to information.

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But do Malawians really want a ‘Za Pamsika’ website?

It turned out that maize and beans prices really are something that people want to see on the website. The recent drought was on everyone’s minds and they were really emphasising how much of a difference getting a good price could make. People were also already using their phones to get prices – by calling their friends or other traders in different areas and were quite enthusiastic about the possibility of getting this information for free.

With these learnings in mind and feeling confident with our website redesign and excited to be working closely with the country office, we embarked on our next steps. We now have a new focal point in the Lilongwe office who’s looking after the project and in a much better placed position than us in Rome to reach out to millions of Malawians. By this point over 25,000 people had already visited the Za Pamsika website but we knew our reach could be much further. We therefore started experimenting with ways of advertising the website.

malawiblog2

First – we decided to take out a Facebook ad to try and raise the site profile so we created our own ‘Za Pamsika’ page on Facebook and put out some ads in English and Chichewa. We were pretty excited when they started showing up on Malawian colleagues’ Facebook newsfeeds and within 10 days we’d reached more than 130,000 people and got 650 likes to our Facebook page.

What we didn’t expect was the organic reaction we’d get to our page. Within 3 days we’d not only reached more than 80,000 people with our post, we’d also seen that people started having conversations about maize prices on our advert.  People have also started messaging us about whether we can add their market to our website. We’re also getting comments about what other commodities we should add, for instance more seasonal foods such as groundnuts or soya. Most excitingly we even had someone knock on the door of the sub-office to inquire about the website after seeing our advert!

On a second mission in April we went out to the markets in Lilongwe again armed with our new ‘Za Pamsika’ posters. We were putting them up in the trader’s shops and were pretty quickly swamped with people excited about the website and how it could save them money. But again – everyone was asking us to add more food prices to the site – it seems like Malawians just keep wanting to know more about ‘things you find in the market’!

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So what’s next for Za Pamsika?

We’ve got our new focal point Khataza on board who’s taking charge of the website. First up, taking requests into account, we will be adding other seasonal commodities to the website. We’re going to continue experimenting with our Facebook ads and start using our Facebook page to reach out and engage with people about what they’d like on the page. We’ve also got some new partnerships coming up with civil society organisations who are keen to spread the word about ‘Za Pamsika’ and who we can work with to break down access barriers to this information.

Are millions in Malawi being reached? Not yet – but we’re getting there.

Ain’t no resolution high enough

One of the major challenges we currently face is that while our survey results provide a detailed picture of the food security situation at the regional level, they are only able to provide representative food security estimates at a larger geographic scale, and don’t always tell us where smaller hotspots or pockets of food insecurity are. So we want to find a way to produce the most accurate, up-to-date and granular representations of food insecurity as possible, to help inform our decision making.

Recently some of our team had the great chance to go to Southampton – a peaceful city in the south of the UK – where we loaded up on shortbread and started working on a type of dynamic high-resolution mapping known as Geostatistical Mapping.

The purpose of the trip was to work with and learn from Flowminder/WorldPop. As you might remember, we’ve worked with them in the past to do things like tracking population displacement in Haiti after Hurricane Matthew. They’ve also developed a way to produce high-resolution maps of population demographics and characteristics. We believe these methods can be applied to create high resolution maps of food security indicators.

We collect information at a cluster level (LEFT) - a village, for example. This is relevant at state level (RIGHT)

We collect information at a cluster level (left) – a village, for example. This is relevant at state level (right)

 

As modelling techniques and data processing capability have evolved, and as high resolution satellite imagery has become more available, creating more granular maps than ever before is possible. This is where Flowminder/WorldPop comes into play. Their aim is to provide estimates of population demographics and characteristics for low and middle income countries by integrating census, survey, satellite and GIS datasets, in a flexible machine-learning framework.

So, how does it work? (if you’re not a satistician, skip to the pictures!)

Basically, these high-resolution maps use one or more geolocated data sets, such as rainfall, vegetation or accessibility to markets, and look at the correlation between these secondary sources of geospatial data and something else, say, a particular food security indicator from a household survey in sampled areas (for this reason, high resolution mapping is also referred to as geospatial mapping) . Once we understand the relationship between the two variables in sampled areas, we can make more accurate predictions about the food security situation in non-sampled areas. If available, mobile phone metadata (Call Detail Records) can also be used as an additional covariate, especially in urban areas where the mobile network is dense.

 

How it is now: male literacy rates in Nigeria (shown at cluster level)

How it is now: literacy rates in Nigeria (shown at cluster level)

How we want it to be: high-resolution map of male literacy in Nigeria

How we want it to be: high-resolution map of literacy in Nigeria

 

 

 

 

 

Looking at the example above and the difference in coverage, you’ve probably already understood how appealing high-resolution maps are as a tool for better planning. But we don’t want to stop here – we’re young and full of dreams! If you noticed, we spoke at the beginning of this post about dynamic high resolution maps. We just discussed how to get a static map for more detailed spatial information, but the next step is actually to update this map each time we have new data. This is a great opportunity, because some satellite imagery already provides new data every ten days or so. This means that we could have maps representing the situation in near real-time.

To take this step, we have to bring in data that is available on a high-frequency basis, such as  mobile surveys. These can be used to highlight some areas of our map on regular basis, or to assess the accuracy of the map by checking hotspots with a quick mobile survey.

Hearing from those who are #FacingFamine

Photo: WFP/Amadou Baraze

Photo: WFP/Amadou Baraze

 

In early March, Stephen O’Brien, the United Nations’ Emergency Relief Coordinator, reported that 20 million people across four countries face starvation and famine.  The famines looming in Yemen, South Sudan, Somalia and Nigeria represent the largest humanitarian crisis since the UN’s creation. “Without collective and coordinated global efforts,” O’Brien said, “People will simply starve to death, and many more will suffer and die from disease.”

One of the components that complicates these particular emergencies is access to the areas in crisis. Without safe and unimpeded access for humanitarian aid workers, it’s difficult to get a picture of what’s going on in the affected areas, which adds another dimension to an already challenging response. In Northeast Nigeria, the threat of violence made it difficult for WFP’s food security analysts to visit vendors in local markets or speak with people in their homes – all part of their usual food security monitoring routine.

In order to continue gathering information needed to understand the situation in the affected areas, WFP used remote mobile data collection to get a picture of what was happening in the communities they could no longer speak to in person. With an overwhelming amount of responses, we turned to Tableau , who had already helped us create data visualizations for other countries which use mVAM, to help us visualize the results in a way that could be easily understood by everyone.

mVAM hears directly from people in affected communities in the northeast of Nigeria

mVAM hears directly from people in affected communities in the northeast of Nigeria

 

Our latest interactive data visualization of the food security situation in Northeast Nigeria is now online, and the story of how it came to be can be found on Tableau’s blog. Make sure to check out the free response section, where you can hear from 5,500 households on what should be done to improve the food security in their community.

 

24 Hour Hackathon People

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Hackers in action at Hacking Aid (Photo: WFP/Angie Lee)

 

As some of you might have already guessed, we at mVAM LOVE hackathons. Last weekend, we had a chance to participate in another one: “Hacking Aid” which was organized by the Dutch Ministry of Foreign Affairs and PwC, together with UNHCR, OCHA and Leiden University’s Center of Innovation. This event brought together more than 70 participants from all walks of life – students, aid workers, programmers, developers, linguists, teachers, professionals from the private sector and government. A common thread linked them all: they were brimming with ideas to find digital solutions to some of the pressing challenges the humanitarian community currently faces.

The overall theme of this particular hackathon centered on finding ways to make humanitarian aid more efficient and transparent. Specifically, we looked at solutions that would enable self-reporting by affected populations, so that people in need would be able to report where, when and what type of help is needed.

In order to come up with specific challenges that could be addressed with practical solutions, we had a rapid prototyping session (a.k.a. think hard and quick) to define a problem around collecting and reporting data. This was followed by an open-mic stage, where we pitched our challenges to the hackers.

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Wael explains what we need (Photo: WFP/Angie Lee)

After working away for nearly 24 hours straight, the twelve teams submitted their final outputs for evaluation by an independent jury panel. The winner of the Hacking Aid award, Team Dream Catchers, developed an app to register complaints and feedback, even offline, from refugees in camps. The second winner, Team Seeing Hunger, proposed a solution to WFP’s challenge: a chatbot tool to pick up and verify self-initiated feedback or reports coming through social media.

A special mention went to Team Botcast, which won the Tech Award for the technically most impressive prototype with their chatbot for Dabanga radio station in Darfur. The chatbot facilitates the process of handling requests for assistance and protection. Team Transformers took home the Innovation Award for their app “Noci”, which uses audiovisual techniques to enable those whom are not able to read or write to report on their needs. 

The winners of the hackathon will have a change to travel to Geneva, where they will pitch their ideas to the board of UNHCR, and will receive support from PwC and Leiden University as they develop a prototype. All prototypes will be available open source.

We noticed that many teams proposed chatbot-based solutions to the challenges we pitched, which is exciting for us as it suggests this is a promising area for technological development. mVAM is already exploring how chatbots could be used to help WFP’s work and we hope to find ways to collaborate further with the teams from the hackathon and other partners to vet other ideas in the area of two-way communications.

How many pizzas does it take to build a chatbot?

Hackers are hungry Photo: WFP/Pia Facultad

Hackers are hungry
Photo: WFP/Pia Facultad

This week, government, business, academia and civil society leaders will gather at Davos to discuss solutions to the world’s biggest challenges – including how new technologies can be leveraged to solve some of the most serious problems we face. At mVAM, we continue to explore how some of these technologies could be used to help eliminate chronic hunger, malnutrition and food insecurity – most recently looking at how chatbots could help collect important information during a humanitarian response.

Last week, our collaborators at Nielsen – one of the early supporters of mVAM – organized a 24-hour hackathon at the Nielsen Tech Hub in New York City. As part of ongoing efforts through Nielsen Cares, the hackathon aimed to develop an open-source humanitarian chatbot that can collect real-time information about food security. This came at the right time for WFP – we’d developed and tested a prototype of the chatbot with InSTEDD, and Nielsen’s technology and development input helped bring in important new capabilities. Ultimately, our goal is to field-test a chatbot in Haiti in the next few months to help us track food security conditions as people recover from the impacts of Hurricane Matthew.

The event was open to the public. A diverse group of students, volunteer hackers, and Nielsen staff showed up to take on the challenge, despite the wintry weather. InSTEDD’s Director of Platform Engineering, Nicolás di Tada also participated.

Much more than a chatbot

What the hackers built is much more that a chatbot: it is a bona-fide chat-based data collection and reporting system. Rather than attempt to outdo each other (as is the case in most hackathons), the teams split up to build the different components of the system. The different teams, made up of perfect strangers, communicated during the hackathon through Slack. After 24 hours, most components were fully coded up, but there were still bugs with the orchestrator and the gateway that additional post-hackathon work will resolve.

The architecture of the system, as defined by Nielsen, includes:

  • a management interface that allows an analyst to set up a questionnaire, including and skip logic, and validation rules that prompt the user when they enter a wrong answer. The interface was built using the Angular 2 JavaScript framework;
  • a gateway that is able to interact with respondents through Facebook Messenger and potentially other chat applications. The Facebook gateway was built on top of the AWS Lambda service;
  • a natural language processing engine that analyzes text on the fly. It allows the chatbot to ‘interpret’ a user’s answers. For now, the NLP engine processes English language text, although the engine includes a translation service and, by default, translates all languages to English for more advanced NLP tasks. The engine was built using the AWS Lambda service and leverages IBM Watson’s AlchemyLanguage service for text processing.;
  • a set of ‘backend APIs’ that manage respondent and survey data, route respondents from each response to the next question, and provide data to user interfaces .  The APIs were built using the Django framework for python and deploys on the AWS Elastic Beanstalk service;
  • an ‘orchestration layer’ that maintains survey status and routes messages between the end user and the various backend services. The orchestration service is built on top of the AWS Lambda service; and
  • a “reporting and data visualization engine”. Data vizzes were built using Highcharts, a JavaScript-based application. This allows an analyst to instantly see the results of the chatbot surveys.

 

chatbot

 

Leveraging cloud services from the Amazon Web Services product catalog, the teams were able to build a scalable, cost effective platform that can be deployed quickly to multiple locations globally.

Remember the humans

We also received tips from a chatbot specialist, Alec Lazarescu from Chatbots Magazine. He encouraged us to ‘onboard’ users with an initial message that gives people a clear idea of what the chatbot is for. He told us to avoid ‘dead ends’ and allow users to speak to a human being in case they get stuck.

We’re very grateful to Nielsen for their support and to all the participants for their energy and creativity. The next steps involve WFP and InSTEDD accessing the code and work on ironing out the kinks. We expect challenges with the natural language processing in Haitian Creole, a language that is probably under-researched. Making the different parts of the chatbot work together seamlessly also appears to be an area we will still have to work on.  And, of course, the final test will be to see whether our target group – people living in Haiti – find the chatbot engaging.

Crowdsourcing food prices in remote areas: a bridge too far?

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Photo: WFP

We have success using crowdsourcing to collect food prices by SMS in the refugee camps of Kenya. The experiences made us curious about trying out other methods that could help us deliver data quickly and efficiently from the remote and hard to reach geographies  where WFP works.

We found out about a startup that specializes in crowdsourced data collection. Anonymous ‘contributors’ would carry out simple data collection tasks through a dedicated smartphone app, the sales pitch went. Intrigued, we decided to pilot this system to monitor food prices in a drought-affected area of Southern Africa. We were hoping to use the data to complement the information traditional information systems produce.  What did we learn?

The anonymous ‘citizen reporter’ is a myth. The company we worked with had to go through local organizations, such as NGOs, to find people able to collect the data for us. This a far cry from the vision of sourcing data from an anonymous crowd. There is more to finding contributors than putting out some ads on social media and magically reaching masses of people. Our contributors were not really anonymous and were easily identified by traders. In the end, the activity looked a lot like traditional tablet-based data collection. The World Bank also found the same thing when they contracted a private company for crowdsourcing. You can find more on their experience in here.

Getting started is labor intensive. It’s going to be a learning process for both your organization and the company, and this will mean investing significant staff time. On our side, since we were unfamiliar with the methodology, there were a lot of iterations as we attempted to specify commodity types and data types. This is perhaps surprising because we at WFP have been collecting food prices for a long time. It turns out we needed to revisit the commodity lists, specify unit measures — a process that required patience. On the company’s side, they had limited experience in the geographies of the pilot which could lead to an overestimation of what was possible and how quickly.

Expect long ramp up times.  The ramp up to the data volumes we wanted took months, because that time was needed to set up the system and recruit the local contributors. Our roll out was planned this way. Do not expect an army of anonymous contributors to materialize out of thin air.

It’s still hard to reach remote places. The crowdsourcing model is no silver bullet when it comes to reaching the remote places we were interested in monitoring. It proved hard or even impossible to source enough data from the more remote markets when using a crowdsourcing service. This is perhaps because of low smartphone penetration in remote locations, the high cost of sending a contributor to such places, or to poor connectivity. In contrast, collecting data from larger urban areas was much easier.

High costs are a barrier to handover in resource-poor environments. It became clear that the cost of the activity was higher than lower-tech alternatives. WFP works to enable handover of information systems to national authorities or other local partners. For the moment, the cost of app-based crowdsourcing is perhaps out of the financial reach of our local partners.

After some trial and error, we were able to obtain good quality data through crowdsourcing that was helpful to our field offices. However, ultimately we returned to our mVAM strategy – using phone calls to traders to collect food prices each week. Although our approach can’t cover as many commodities as the company’s crowdsourcing activity provided, it has its own advantages. It’s lower tech – there is no fancy app to download. There is no far away company to deal with. Above all, it’s an approach we can hand over to our local partners.

mVAM recognized for innovation in the 2016 ‘Nominet 100’

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We’re pleased to announce that mVAM has been recognised as one of 2016’s 100 most inspiring social innovations using digital technology to drive social change around the world. The competition, the NT100, is run by the Nominet Trust, the UK’s leading tech for good funder.

The 2016 NT100 was selected from 700 projects reviewed by Nominet Trust and a panel of partner organisations including: Big Lottery Fund, Cancer Research UK, Comic Relief, Nominet, Oxfam, Telefonica O2 and Skoll Centre for Social Entrepreneurship.

mVAM has been recognised for its contribution to humanitarian interventions by leveraging mobile technology to provide frequent, lower cost food security data.

If you want to find out more about other NT100 projects check out their Social Tech Guide, a comprehensive collection of inspiring ways tech pioneers are changing lives, communities and our world for the better.

Our 5 hacks for mobile surveys for 2015

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An mVAM respondent in Mugunga III camp, DRC.

  1. Gender matters. Design and run your survey in a way that promotes women’s participation. With mobile surveys, it’s hard to get as many women to respond as men. Make sure you’re calling at the right time and that you provide incentives. We also recommend having women operators. For more of our thinking on gender in mobile surveys, check out our blog entry on gender issues in West Africa.
  1. Validate mobile data against face-to-face data. Your mobile survey results may differ significantly. In many contexts, cell phone penetration has not reached the most vulnerable groups. In DRC, we had to provide phones to Internally Displaced Persons (IDPs) and access to electricity- to learn more check out our video and our blog entry. But it’s not always possible to distribute phones so it’s important to check your results against other data sources. Also, people get tired of answering their phones all the time so attrition and low response rates will affect your results.
  1. Mind the mode!  Your results will differ according to whether the survey is done through SMS, IVR, or live calls by an operator. Live calls have the highest response rates, but you have to be ready to pay. For simpler data, we have found that SMS is effective and cheap. Just remember- the context matters. SMS is working well with nationwide surveys, even in countries where literacy rates are not that high- check out our recent results in Malawi. However, SMS can be a problem in communities where literacy rates are very low or familiarity with technology is low as we found in DRC IDP camps. For Interactive Voice Response (IVR) that use voice-recorded questions, the jury is still out on its usefulness as a survey tool.  IVR didn’t work as well as SMS in Liberia, Sierra Leone, and Guinea during the Ebola crisis (HPN June 2015). But IVR has potential as a communication tool to push out information to people. Check out our entry on our two-way communication system where we use IVR to send distribution and market price information to IDPs in DRC.
  1. Keep the survey user friendly and brief. Always keep your survey short and simple. Stay below 10 minutes for voice calls, or people will hang up. If you are texting people, we don’t recommend much longer than 10 questions. Go back to the drawing board if respondents have trouble with some of your questions. With mobile surveys, you don’t have the luxury of explaining everything as with in person interviews. It might take a few rounds to get it right. When we want food prices, we’ve found we need to tweak food items and units of measurement in Kenya and DRC to best capture what people buy in local markets. Again, short and sweet should be the mobile survey mantra.
  1. Upgrade your information management systems. There is nothing as frustrating as collecting a lot of great data – without being able to manage it all! Standardize, standardize, standardize! Standardize questions, answer choices, variable names, and encoding throughout questionnaires. Automate data processing wherever possible. Also, you’ll be collecting phone numbers. This is sensitive information so make sure you have the correct confidentiality measures in place. Check out our Do’s and Don’ts of Phone Number Collection and Storage and our script for anonymizing phone numbers. Finally, share your data so others can use it! We’re posting our data in an online databank.