Discussing the Value Map

In my previous post I created a value map for the Cambridge Center for Families. A department of the Cambridge city government that was created with the mandate of educating and connecting parents in the Cambridge Area. My value map focused on the existing services that are provided for new parents. The other corresponding customer profile for this map would be a new parent. The Cambridge Center for Families has resources for all parents, but new parents are a specific sub-set of that group.

I decided to focus on the Cambridge Center for Families because it is a service that I had direct experience as a new parent in Cambridge, and I had the intuitive feeling that I had been served well by them. Creating the value map, I realized that while I appreciated the services provided by the center, it didn’t provide, or at least didn’t focus on the areas that are the biggest pain points for new parents. Approximating a customer profile, my wife and I’s biggest pain points as new parents and in my son’s first two years were lack of sleep, lack of access to child care, nursing difficulty, lack of peers to connect with and feeling lost regarding the best way to parent on a broad variety of issues.

The activities provided by CCFF addressed some of these issues. They provided education resources on parenting, and linked to further resources. They provided some group activities for parents to connect. But they didn’t address the biggest pain points, and those they did, the solution was at best incomplete. Leaving aside the elephant of sleep deprivation in new parents, child-care is a problem that could be addressed in some degree by the Cambridge center for families leveraging it’s existing capacity. They already serve as a network for parents, and as a source for directing resources. After two years of being a parent, I have found that most child-care I have used has come from my personal network. By leveraging their own parent network CCFF could help to alleviate one of the biggest pain points for new families. The value mapping process helped to identify this potential new service, and diagnose an area where a good organization could be even better.

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Value Map

I created a Value Map for the Cambridge Center for Families(CCFF). This is a service that was pretty helpful when my son was born and my wife and I felt pretty lost. They are the first organization that I think of when I think of a government sponsored program that has directly benefited me. As much as I appreciate their work, I feel that they could do even more. The other side of this is a customer profile. For me I think that CCFF has a pretty good alignment with what we needed, but that they could expand their services to more pain points for new parents. The value map framework can be found here.

Screenshot 2016-02-22 14.15.46

Learning Faster

Post created for Technology, Policy, and Public Service Innovation Class.

I did some work at medical device company that sold a number of urology products. One of their products was a non invasive testing machine. Its target market was doctors in a certain specialty with private practices. The device sold for a couple of thousand dollars and it had a disposable that was used every time the doctor used the machine to test a patient. We were pricing the disposable at around $50. This product had been around for a few years, and there were a few doctors who had it, but they rarely ordered new disposables.

The company had just received capital from investors who wanted the company to use it to figure out the right sales model for the product.

My role, although I was not the only one assigned to work on this, was to help the company figure out how to make this product as profitable as possible.

We had a couple of hypotheses as we started to consider a new sales strategy. The most important was that the potential of the disposable revenue stream dwarfed that of the sale of machines. A practice that used the machine twice a week for two years would provide us with as much revenue as the initial sale of the machine!

What we needed to learn was, how do we sell as many disposables as possible? We did test a number of strategies in attempt to figure out this question, but if we had applied a more rigorous approach to hypothesis testing we could have figured it out much faster.

Our first attempt to increase disposable sales was to rent out the machines and give economic incentives for practices that used more disposables. This turned out to have little effect on the problem, which we could have learned much faster with A/B testing. Rather than offering a rental program to all of our clients we should have segmented our population to be able to compare disposable use for those who owned and rented the machine. With this approach we could have more quickly moved to the next potential strategy.

Through this strategy and others we finally realized that the practices we were selling to had almost no change in their use of our product given any potential economic incentive. Doctors often said to us in sales meetings that they are very sensitive to the price of products and their ability to get insurance reimbursement for particular tests or procedures. But their behavior indicated that they were, ironically given their mercenary rhetoric, mostly acting in a way that was best for their patients, independent of the price of delivering service.

Had we learned this earlier we could have much more quickly pivoted to a strategy that highlighted the clinical benefit of our device, which was a non-invasive alternative to an existing test, and abandoned strategies that focused on pricing. Had we been A/B testing our economic strategies from the beginning, we would have much more quickly discovered our client’s motivations.

Learning faster

Post created for Technology, Policy, and Public Service Innovation Class.

I did some work at medical device company that sold a number of urology products. One of their products was a non invasive testing machine. Its target market was doctors in a certain specialty with private practices. The device sold for a couple of thousand dollars and it had a disposable that was used every time the doctor used the machine to test a patient. We were pricing the disposable at around $50. This product had been around for a few years, and there were a few doctors who had it, but they rarely ordered new disposables.

The company had just received capital from investors who wanted the company to use it to figure out the right sales model for the product.

My role, although I was not the only one assigned to work on this, was to help the company figure out how to make this product as profitable as possible.

We had a couple of hypotheses as we started to consider a new sales strategy. The most important was that the potential of the disposable revenue stream dwarfed that of the sale of machines. A practice that used the machine twice a week for two years would provide us with as much revenue as the initial sale of the machine!

What we needed to learn was, how do we sell as many disposables as possible? We did test a number of strategies in attempt to figure out this question, but if we had applied a more rigorous approach to hypothesis testing we could have figured it out much faster.

Our first attempt to increase disposable sales was to rent out the machines and give economic incentives for practices that used more disposables. This turned out to have little effect on the problem, which we could have learned much faster with A/B testing. Rather than offering a rental program to all of our clients we should have segmented our population to be able to compare disposable use for those who owned and rented the machine. With this approach we could have more quickly moved to the next potential strategy.

Through this strategy and others we finally realized that the practices we were selling to had almost no change in their use of our product given any potential economic incentive. Doctors often said to us in sales meetings that they are very sensitive to the price of products and their ability to get insurance reimbursement for particular tests or procedures. But their behavior indicated that they were, ironically given their mercenary rhetoric, mostly acting in a way that was best for their patients, independent of the price of delivering service.

Had we learned this earlier we could have much more quickly pivoted to a strategy that highlighted the clinical benefit of our device, which was a non-invasive alternative to an existing test, and abandoned strategies that focused on pricing. Had we been A/B testing our economic strategies from the beginning, we would have much more quickly discovered our client’s motivations.