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July 27 2012

14:00

The Importance of NextDrop's Customer Cycle, and How to Improve Service

In our last post on PBS Idea Lab, NextDrop, which informs residents in India via cell phone about the availability of piped water, was trying to scale up in a very short period of time. How did we fare?

nextdroplog.png

Well, I think we discovered the first step to winning: Just get good data about yourself. Period. Even if it's ugly. Because after admitting there's something wrong, the second hardest part is wading through the mess and figuring out what exactly that is!

Let me try to lay out everything we discovered about our service.

Customer Side

Goal: Bill everyone possible and make money.

Immediate problem: Billers wasted a lot of time because even when they found houses (which many times proved difficult), a lot of people were getting late messages, weren't getting messages at all, getting them intermittently so they didn't want to pay for the service (no argument there), or just didn't want the service.

Immediate solution: Make a list of areas that have been getting regular messages for the past two weeks, and then call all those people before we actually go out and bill.

Immediate Systems We Put in place

Creation of the "Green List": We look through all of our valvemen data, and using the all-mighty Excel, we figure out which areas received at least four calls within the last two weeks. Our logic here is that since the supply cycle is once every 3-4 days now, if they are getting regular messages, valvemen should call in at least four times in a 2-week span. This system is by no means perfect, but it's a start, and at least gets us to the next level.

Conduct phone surveys: After we see all the areas that are on the Green List, we then call all the customers in that area. We spent two weeks piloting the survey to even figure out what categories/questions we should ask, and we've finally got some classifications the sales team feels good about.

Here are the different categories of NextDrop potential customers:

  • Could Not Contact (people who had phones turned off, didn't answer the call, possibly fake numbers)
  • Satisfied Customers
  • Pay (want to pay for service)
  • Continue
  • 1-month Free Trial (again)
  • Deactivate
  • Unsatisfied Customers
  • Not Getting Messages
  • Wrong Messages

Bill: We just bill the people who are satisfied and want to pay, or who are satisfied but want another free month trial (and have already had one).

our customer cycle

Here's a great flow chart that our sales manager made of our customer cycle (and if any engineers out there think this looks familiar, you're right! It is, in fact, a State Diagram. This is why I love hiring engineers!) And let me say, this may look easy, but it took two weeks to analyze customer behavior to even figure out what states to include and how to go from one state to another state.

customercycle.png

When we finally had data, we discovered some really interesting things about our service:

  • Total number of people called: 1,493
  • Total number of people we could contact: 884 (59%)
  • Total number of deactivated customers: 229
    15% of total customers
    26% of contacted customers
  • Total number of continuing customers: 655
    44% of total customers
    74% of contacted customers
  • Total billable customers: 405
    27% of total customers
    46% of contacted customers
  • Total billed customers: 223
    15% of total customers
    25% of contacted customers
    55% of billable customers
  • Total number of people who paid: 95
    6% of total customers
    23% of billable customers
    43% of billed customers

As you can see, the two major problems we identified were 1) we were unable to contact 41% of the customers we tried to contact, and 2) a majority of the people who we were able to contact were getting incorrect messages (54% of the contacted customers).

troubleshooting problems

And that's where we're at: trying to troubleshoot those two problems. Here are the immediate solutions we're putting in place to increase the people that we contact, and to put customers in the correct valve area.

Instead of taking "Could Not Contact" customers off the billing list, we are going to try to contact them. We're in the process of seeing what percentage of the "Could Not Contact" customers we can actually find and contact when we bill.

We have an intern, Kristine, from UC Berkeley, who will be working with us for the next six months to figure out how to place people in the correct valve area (because that is the critical question now, isn't it?) Kristine's findings are pretty interesting (and definitely deserves its own blog post), but our first prototype is to test a guess and check methodology:

  • First we call customers and find out when was the last time they got water.
  • Then sort through our data and see what areas got water on that date (plus or minus a few hours). This should at least eliminate 50% of the areas.
  • Then, to narrow it down even further, we only consider those areas that are geographically close to the customer. This should narrow it down to within 4-5 areas to check.
  • We subscribe the customer to these areas, and see when he/she gets the correct message. (We will find out through the phone survey.)

That's what we are going to try -- we'll let you know how that goes.

steps toward progress

In any case, I think the tunnel has a light at the end of it, so that's all we can really ask for -- progress!

And, as always, we will keep you updated on our progress, what seems to work, what doesn't, and more importantly, why.

Additionally, and most importantly, we're hiring! We are looking for enthusiastic and passionate individuals who want to be a part of our team. If you love problem solving, and finding creative solutions to problems, we want you!

As always, please feel free to write comments, offer insight, ask questions, or just say hi. Our proverbial door is always open!

A version of this post first appeared on the NextDrop blog.

Anu Sridharan graduated from the University of California, Berkeley in 2010 with a master's degree in civil systems engineering; she received her bachelor's degree from UC Berkeley as well. During her time there, Sridharan researched the optimization of pipe networked systems in emerging economies as well as new business models for the dissemination of water purification technologies for arsenic removal. Sridharan also served as the education and health director for a water and sanitation project in the slums of Mumbai, India, where she piloted a successful volunteer recruitment and community training model.

April 23 2012

13:10

NextDrop: Water Utilities in India Need Good Data

In places like the United States, we have access to more data than we ever know what to do with. We measure everything from what the average historical temperature is on a certain day for a city, to how good a restaurant is, to how much energy we consume. Because of this access, we base many of our critical decisions on this data (or at least that's the hope). Essentially, because we have had access, we know how to use this data.

nextdrop.jpg.jpg

However, this isn't the case everywhere.

Fact: Just because you have access to data, it does not guarantee that you will use it appropriately. Using it appropriately requires behavior change, something that, any person will tell you, is incredibly difficult.

This is the hard part about data -- not the production of it, but the usage. This means that simply providing technology is not a solution. It is technology and the realization of the potential results that will produce meaningful change.

making data-driven decisions

This is similar to the situation water utilities are facing in India. There's no real incentive to get good data, and it makes sense. They have many things to worry about -- mainly, the reduction of non-revenue water. Data is tricky, because the results are more of the intangible kind. You need initial buy-in, and lots of time, in order to build your case for making data-driven decisions.

We know that these data-driven decisions will, in time, reduce non-revenue water, but it will take some time. And unfortunately, in a world that wants sexy solutions along with fast results, this does not come easily.

We're hoping that in the future, other stakeholders will promote the acquisition of quality data, and will push the utilities to make data-driven decisions. From academics, to other government agencies, we see a need from other stakeholders to push this agenda and create this water data market for water utilities.

And when that happens, NextDrop will be there to provide that quality data to the utilities to help them become more efficient.

January 11 2012

15:20

NextDrop's Dashboards Look Great, But Mobile Content Would Be Better

One year ago, when we were just a team of graduate students with a big idea, our teammate Thejo Kote came to Hubli, India and demoed a web-based dashboard to the executive engineer and commissioner here. The dashboard uses Google Maps to show the status of valves and other system components in real time, using information provided via voice or SMS.

dashboard.png

Building that dashboard marked a turning point for NextDrop, which informs residents in India about the availability of piped water in order to help them lead more productive, less stressful lives. It was our first real "pivot," as we moved decisively away from crowdsourcing information from residents, which wasn't working. It was also the way to make progress with the utility, partner with them, and ultimately, win competitions that would enable us to get our company off the ground.

Implementing that dashboard is part of the larger vision of how NextDrop can ultimately revolutionize information flow in water utilities. But based on what we've learned so far, it's not clear that it's the low-hanging fruit in terms of how to make the lives of engineers easier today.

In Hubli, utility engineers have the computers and Internet access you need to follow the days' supply cycle through a live dashboard, but they're not quite there yet in terms of integrating that technology into their day-to-day routines.

But there's a different technology they are using -- everyone in the utility has a mobile phone, and they are incredibly adept at handling calls from hundreds of people each day, as they do things as varied as managing valvemen, dealing with customer complaints, coordinating tanker deliveries, overseeing pipe damage repairs, and interfacing with other engineers.

a day in the life of an engineer

santosh3.jpg

Last week, a team member and I went to the field with Mr. Santosh, one of the two section officers in Hubli's North Zone. While he was showing us the NR Betta Tank, we got to see first-hand the volume of calls he deals with.

Like all the engineers in the utility, Santosh's number is public, so even customers in his area can call him directly with complaints. Here are some notes from my interview with him.

I asked Santosh how many calls he gets, and this was his response:

  • 30 to 40 calls per day from NR Betta Tank, the major reservoir tank he is responsible for, where he checks on the reservoir level and chlorine levels.
  • 15 to 20 calls per day from his valvemen updating him on where they provided water.
  • 20 calls per day from the public inquiring about new connections.
  • 40 calls per day about tanker tuck deliveries.

While we're still learning a lot about the utility, we think the products that will make the lives of utility engineers easier today will have the following qualities:

  • Reduce the volume of calls the engineers get.
  • Provide them information through the mobile phone, the medium they already use.
  • Generate clear electronic records that can be studied over time.

With this in mind, we're launching a daily SMS that will inform utility engineers whether water was delivered to all the areas they're responsible for, and notify them of any exceptions to the set schedule. Beyond that, we're looking at opportunities to help engineers track the status of pipe damage repairs and tanker deliveries.

More news on new utility products soon to follow!

A version of this post first appeared on the NextDrop blog.

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