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Tuesday 20 October 2015

Investor red flags- always logical?

A few of my interns recently did a brain-storming session with me. They were all enthusiastic to do something of their own and had many questions. Many of them I met at several accelerator competitions and hackathons.

I am not an authority or a VC. So far, I have invested in 9 companies globally- all in healthcare or education. One thing that I have come across in almost every transaction is when seed funded companies go for Series A- Investor raise red-flags about

1. Recurring revenue
2. Team
3. Valuation

One such situation came in today when I was asked How do you anticipate growth of 15%-20% YOY when in a force of 200 employees you have only 5 sales guys and 2 marketing people. What are you relying on? Most start-ups find it hard to answer such a question.

When you cannot rely on sales and marketing to be your driver of growth, you rely on your customer to be your driver of growth. Hence when the service and the product keep evolving imparting better and sustained value- one needs to rely less on marketing. I think it actually builds a much deeper foundation when you don’t rely on a very large sales and marketing team to get your brand out.

This MIT guy asked me- How would you measure the value of a company? Especially, a company that you started a month ago – how do you determine start-up valuation?

My answer was simple Valuation need not show the true value of the company. Actually what it defines is about investor share in the company.  At the time of investment, valuation is the core determinant of return for investors. In other words, the return to investors is based on the increase in the valuation of shares they receive in exchange for their capital. Understanding valuation is critical to successful investing. Unfortunately, valuation is the most misunderstood part of the investment process and often leads to contentious negotiations that get the entrepreneur investor relationship off on the wrong foot.Valuation depends on how much money you need to say run 3 pilots and have 8-12 months of runway. As an Investor, I expect growth in 18 months.

Valuation matters to entrepreneurs because it determines the share of the company they have to give away to an investor in exchange for money.  At the early stage the value of the company is close to zero, but the valuation has to be a lot higher than that. Why? Let’s say you are looking for a seed investment of around $100, 000 in exchange for about 10% of your company. Typical deal. Your pre-money valuation will be $ 1 million. This however, does not mean that your company is worth $1 million now. You probably could not sell it for that amount. Valuation at the early stages is a lot about the growth potential, as opposed to the present value.

The biggest determinant of your startup’s value are the market forces of the industry & sector in which it plays, which include the balance (or imbalance) between demand and supply of money, the recency and size of recent exits, the willingness for an investor to pay a premium to get into a deal, and the level of desperation of the entrepreneur looking for money.

The follow-up questions was- Does valuation also depend on who you are taking money from? - I laughed it out. The answer to that is a big "YES" - we all know that.

We have an acquisition offer from a large hospital chain- Tim said- they are interested in Acqui-Hire- Should we take it? How to decide between getting acquired and acqui-hire.

Well. It depends on how your company is doing? Acqui-Hire is offered when you have a great team together but product isn't going anywhere. Acquisition is the best move when we realise that the product has traction, but the company does not generate enough revenue to qualify for an IPO.

And last but not least- What does an Angel like me look for returns?

Some angels target 5x to 10x ROI (cash-on-cash return on their investment) in four to eight years, which yields an internal rate of return of between 25 and 75 percent. (In the accompanying table, the target numbers assume that divergence of between 3x and 5x times is factored in.) Other angels simply target 30x ROI without divergence. The two approaches are effectively equivalent:

If you assume 4x divergence (the midpoint between the expected range of 3x to 5x) and multiply that by a return of 7.5x (midway between the 5x and 10x range), then you get 30x, which factors in divergence. These rules of thumb are not sacrosanct; they reflect two common approaches.

Divergence:
Divergence is the difference between the growth rate of the company’s valuation and the
valuation of the shares investors receive due to dilution by subsequent investors and other factors. Even in successful ventures, divergence, in fact, tends to be between 3x and 5x.
A simple example may help make the point: An investor funds at a $4-million post-money valuation and receives shares valued at $2 each. The company is sold in five years for $60 million, which is a 15x increase in company valuation. Due to dilution, however, the value of the investor’s shares will almost certainly not have increased 15x to $30 per share. They might instead have increased only 3x to $6 per share. In this example, the increased valuation of 15x divided by the increase in the investor’s share value of 3x demonstrates a 5x divergence.

A startup company’s value, as I mentioned earlier, is largely dictated by the market forces in the industry in which it operates. Specifically, the current value is dictated by the market forces in play TODAY and TODAY’S perception of what the future will bring.

Effectively this means, on the downside, that if your company is operating in a space where the market for your industry is depressed and the outlook for the future isn’t any good either (regardless of what you are doing), then clearly what an investor is willing to pay for the company’s equity is going to be substantially reduced in spite of whatever successes the company is currently having (or will have) unless the investor is either privy to information about a potential market shift in the future, or is just willing to take the risk that the company will be able to shift the market.

Therefore, when an early stage investor is trying to determine whether to make an investment in a company (and as a result what the appropriate valuation should be), what he basically does is gauge what the likely exit size will be for a company of your type and within the industry in which it plays, and then judges how much equity his fund should have in the company to reach his return on investment goal, relative to the amount of money he put into the company throughout the company’s lifetime.

Comments welcome.



Credits:
http://www.angelcapitalassociation.org/
http://seedcamp.com/resources/how-does-an-early-stage-investor-value-a-startup/
Ewing Marion Kauffman F Foundation
http://www.forbes.com/sites/mariannehudson/2015/03/06/the-art-of-valuing-a-startup/
http://articles.bplans.com/30-questions-angel-investors-will-ask-you/



Monday 5 October 2015

Dr. Ruchi Dass on Role of Analytics in Healthcare

Quick Snapshot of our work in the field of Analytics- shot at the Healthcare IT Summit, Mumbai, India. What do we do there with Predictive analytics tools- serious work!!

Predictive analytics (PA) uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. That information can include data from past treatment outcomes as well as the latest medical research published in peer-reviewed journals and databases.

In medicine, predictions can range from responses to medications to hospital readmission rates. Examples are predicting infections from methods of suturing; understanding Patient’s patterns,  bias and compliance;  determining the likelihood of disease, helping a physician with a diagnosis, and even predicting future wellness.

The statistical methods are called learning models because they can grow in precision with additional cases. There are two major ways in which PA differs from traditional statistics (and from evidence-based medicine):

First, predictions are made for individuals and not for groups
Second PA does not rely upon a normal (bell-shaped) curve.

Prediction modelling uses techniques such as artificial intelligence to create a prediction profile (algorithm) from past individuals. The model is then "deployed" so that a new individual can get a prediction instantly for whatever the need is, whether a bank loan or an accurate diagnosis.


1. Help increase the accuracy of diagnoses.

Physicians can use predictive algorithms to help them make more accurate diagnoses. For example, when patients come to the ER with chest pain, it is often difficult to know whether the patient should be hospitalized. If the doctors were able to answers questions about the patient and his condition into a system with a tested and accurate predictive algorithm that would assess the likelihood that the patient could be sent home safely, then their own clinical judgments would be aided. The prediction would not replace their judgments but rather would assist.


2. Helps preventive medicine and public health.

With early intervention, many diseases can be prevented or ameliorated. Predictive analytics, particularly within the realm of genomics, will allow primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can make lifestyle changes to avoid. As lifestyles change, population disease patterns may dramatically change with resulting savings in medical costs. With Predictive Analytics, our future medications would be more personalised because predictive analytics methods will be able to sort out what works for people with "similar subtypes and molecular pathways."

3. Provides Physicians with intuitive insights planning treatment methodology for individual patients.

Evidence-based medicine (EBM) is a step in the right direction and provides more help than simple hunches for physicians. However, what works best for the middle of a normal distribution of people may not work best for an individual patient seeking treatment. PA can help doctors decide the exact treatments for those individuals. It is wasteful and potentially dangerous to give treatments that are not needed or that won't work specifically for an individual. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including the doctor's time.

4. Provide employers and providers with predictions concerning insurance product costs.

Employers providing healthcare benefits for employees can input characteristics of their workforce into a predictive analytic algorithm to obtain predictions of future medical costs. Predictions can be based upon the company's own data or the company may work with insurance providers who also have their own databases in order to generate the prediction algorithms. Companies and hospitals, working with insurance providers, can synchronize databases and actuarial tables to build models and subsequent health plans. Employers might also use predictive analytics to determine which providers may give them the most effective products for their particular needs. Built into the models would be the specific business characteristics. For example, if it is discovered that the average employee visits a primary care physician six times a year, those metrics can be included in the model.

5. Helps researchers develop with even lesser data that can become more accurate over time.

In huge population studies, even very small differences can be "statistically significant." Researchers understand that randomly assigned case control studies are superior to observational studies, but often it is simply not feasible to carry out such a design. From huge observational studies, the small but statistically significant differences are often not clinically significant.
For example, small to moderate alcohol consumption by women can result in higher levels of certain cancers. Many news programs and newspapers loudly and erroneously warned women not to drink even one alcoholic drink per day.

In contrast with predictive analytics, initial models in can be generated with smaller numbers of cases and then the accuracy of such may be improved over time with increased cases.

6. Helps Pharmaceutical companies to plan and best meet the needs of medications for the masses.

There will be incentives for the pharmaceutical industry to develop medications for ever smaller groups. Old medications, dropped because they were not used by the masses, may be brought back because drug companies will find it economically feasible to do so. In other words, previous big bulk medications are certain to be used less if they are found not to help many of those who were prescribed them. Less used medications will be economically lucrative to revive and develop as research is able to predict those who might benefit from them. For example, if 25,000 people need to be treated with a medication "shotgun-style" in order to save 10 people, then much waste has occurred. All medications have unwanted side effects. The shotgun-style delivery method can expose patients to those risks unnecessarily if the medication is not needed for them.

7. Leads to better Patient outcomes.

There will be many benefits in quality of life to patients as the use of predictive analytics increase. Potentially individuals will receive treatments that will work for them, be prescribed medications that work for them and not be given unnecessary medications just because that medication works for the majority of people. The patient role will change as patients become more informed consumers who work with their physicians collaboratively to achieve better outcomes. Patients will become aware of possible personal health risks sooner due to alerts from their genome analysis, from predictive models relayed by their physicians, from the increasing use of apps and medical devices (i.e., wearable devices and monitoring systems), and due to better accuracy of what information is needed for accurate predictions.

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