Yesterday I described how I came to start my company Quant Price. Today is the story of what happened after that decision.

I first did the Founder Institute program that helps entrepreneurs create companies. They introduced many concepts that are hard for first time founders to grasp. Things of HUGE importance ranging from talking to potential customers to validate the idea to seeing how buying decisions are made.

There are many things that can make a startup fail. Each of these factors are risks. A successful startup has avoided each of these pitfalls in succession.

There is a step by step process to developing the idea and de-risking it. The founder Institute program helped me think through them.

One shortfall of the program was that it has a cookie cutter view to creating startups. It requires that everyone fit into a mold. This may not serve every startup well. I’d probably have taken it to completion if I were not “terminated” for having a hard time getting a sufficient number of video interviews.

Though to be rational, I’ve to admit that it’s also possible that I just have a very bad idea or am not really cut out for this. I may only know very late because “I’m drinking my own Kool-Aid”.

The risks are huge. There are a lot of things that matter to a startup being a success. There has to be value in the product. There has to be a way to reach a big chunk of the market. There has to be a way to convert all those gains into money. People have to believe in the dream and be willing to contribute for free for the duration that the cash flow doesn’t exist.  It takes a lot to keep a startup together.

It was very hard to find my first customer. I didn’t even have my marketing material when I went to interview Katherine Krug the founder of Getbetterback.

Partially because I didn’t realize that I was supposed to bullshit my way through such meetings and, frankly, partially because I didn’t know how to bullshit about it if I had to.

Fortunately, Katherine is one of those people who tests things like prices. I think she was lucky to not have a mentor who had a strong opinion about pricing. (I still can’t believe that people don’t test their prices.)

Katherine first did a price test with Optimizely. Unfortunately, Optimizely is not really geared to do price tests. Additionally, putting Optimizely code on your web page makes it load a lot slower for the end consumer. That itself reduces conversion rates.

It made me wonder if people don’t know or just don’t care about the impact this has on the browsing experience.

Optimizely targets conversion rates, so once Katherine was done with the test it told her how many people had converted to buy at different prices. Obviously at the higher price, fewer people converted.

But the real question was which price lead to better margins?

And was the test significant?

That is when she realized she needed Quant Price.

I salvaged the Optimizely results to guess what the next prices for the next test should be.

We used our pricing engine and did an A/B test. It turned out that seemingly identical prices $49 and $59 were over 26% different from a revenue perspective.

We also realized that changes to the web design and the holiday season could change the optimal price. So much so that at some point $69 was 22% better than $59 because of the Christmas buying spree.

Hidrate, my second client, was easier to find after we put up a video interview with Katherine.

With Hidrate, we seemed to run into some bad luck. The $59 price for them was identical to $49 from a profit perspective. But as luck would have it, they were running out of inventory. Now, at the $49 price they were burning through 45% more inventories to make the same amount of money as at $59.

Once we told them about it, they raise their price to $59 to save inventory. They kept their customers happier for longer AND made money to buy more inventories without losing the company to investors.

Here are details from our first two clients,

• Test your prices
• Prices Change
• Inventory And Pricing

We did all this by using technology developed for finance. Quants in the financial industry are usually tasked with pricing securities worth trillions of dollars based on market data. There are multiple factors that affect the “fair” price of a security.

I learned how this valuable task of pricing can be automated incorporating available data.

Many companies now quote prices and sell exclusively online. Computers can be used to make pricing decisions based on price sensitivity of individual customers, business specific parameters, such as inventory availability and market conditions and the current season.

Airlines and Amazon already do this on a massive scale and are very profitable as a result.

SaaS companies and large-to-medium scale retail operations could be next in line.

Fortunately, our market is easily defined. We are looking to help companies with more than 300 distinct customers. This is desirable because with more data we can get statistical significance on more complex models.

We are looking to help SaaS companies that want better pricing models. For them, we have a solution that reduces churn and increases revenue at the same time !

To justify building a company-specific solution, we need to be able to service a pool of revenue of over 10M$/year.

Quant Price’s free app Qbot is available to smaller companies in the Shopify app store.

In order to make our technology more accessible we packaged it into APIs and apps, that help us expose this functionality to more companies.

Two final points.

• I’m happy to pay a bounty for referrals. Contact me at info@quantprice.com for details.

• We are looking for talented people  who can participate in the development of our product.

And if you have any questions or comments you can respond here or use the email address above.

After I got my Masters in Computer Science and Optimization from USC,  I worked for banks for a while as a quant (people who do quantitative or mathematical stuff such as build complex models to evaluate financial securities, risk and reward).

Contrary to what most people would tell you about working for a bank, I found the work very interesting—not surprising, given my math background.

But much as much as I enjoyed my work, I was bitten by the entrepreneurial bug and wanted to do something original.

I’m an engineer at heart and I like inventing things that makes life better or improve what people are already doing.

Innovation seems to happen overwhelmingly in small to medium companies and then big companies usually buy the promising smaller ones.

Doing my own thing would also gave me the ability to try out new things at an amazing pace that makes the process of discovery a lot faster.

Initially I had the bright idea of trying to beat the banks at what they do best: price securities.

I invented a way of making models that could learn more from purchase data about pricing than conventional models do and put those models to work trying to outguess Wall Street computers.

Eventually, I realized that even though my algorithms were smart, the networks that were affordable to me were too slow to use this information for the strategy I developed.

Even though I could predict prices, I couldn’t get ahead of computers that were closer to the exchange to make the profitable trades.

I had to make a pivot or bet even bigger and buy access to the necessary networks.

I paused to think for a while and it occurred to me that the world is full of things that need to be priced.

So, why stick to the business of securities where so many quants and fast computers were concentrated in solving a problem with a lot of history?

Why not solve a Main Street problem?

I began looking for a niche where there was a significant problem that I could solve.

About that time I heard a story on the NPR money podcast.

Two sets of people were asked to guess the weight of a cow shown in a picture.
They first asked a bunch of experts what the weight might be and each gave a different answer.
They also gathered answers from a crowd of non-experts on a website.

The median value in the crowd of non-experts was much closer to the true weight of the cow than the group of experts!

That led me to think about how the wisdom of crowds could be used to help small companies make better decisions.

Of course, I wanted to monetize the information in the buying decisions that people make.

How about learning the perfect offer to make to shoppers at an online store?

 I’d recently built complex pricing models to value financial securities, so I knew I could do this.

I quickly noticed that small businesses were leaving a lot of profit on the table. They were essentially using rules-of-thumb, instead of measuring price sensitivity and making optimal offers for peak profit just like the airlines, Airbnb and Amazon are already doing.

How much of an advantage can optimization bring?

For example, the average retail store runs with a 5% net margin.
What  if the store could raise the sale price (after any discounts) of the average item by 1% without affecting demand?

That would raise their margin a staggering 20%!
And that is what happens with just a 1% change.

Imagine what can happen with a larger price change.

In our experiments with stores on Shopify, we noticed that they were so far off the optimal price that they were making less than 70% of what they could be making if they could tune into the crowd.

That is beyond a big deal!

So I began working on an app with the vision of using optimization to create better offer management strategies.

Over the last year I created the company, Quant Price. You can try our first app for free on Shopify.

Join me tomorrow for a closer look at what Quant Price does.