How To Make Money From Predictions: Almanis And Selling Off Of Free Crowdsourcing Services

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The launch of a private client prediction service demonstrates how a start-up can take a public or open-source product and convert it into a viable moneymaking business model. Slowvoice, the start-up in question launched Almanis , a crowd-sourced prediction website, late last year. (See the previous article From Guessing…

The launch of a private client prediction service demonstrates how a start-up can take a public or open-source product and convert it into a viable moneymaking business model.

Slowvoice, the start-up in question launched Almanis, a crowd-sourced prediction website, late last year. (See the previous article From Guessing The Weight Of A Cow To Ruling The Stock Market to learn more about Almanis and the crowd-sourced prediction process.)

It now intends to launch Percypt, its private-client version, in the near future. Percypt will do largely the same things as demonstrated in the Almanis model. It will take a variety of predictions from staff at a company, weight them correctly and provide an analysed prediction of the most likely outcome – a process that will enable companies to improve efficiency.

Slowvoice has already proved that its prediction process works, scoring 96.4% accuracy on the 28 questions now closed on the Almanis website. The next steps are to show how companies and investors can benefit from the predictions and then show potential customers why they should pay for the private-client service.

The first part has already been achieved. Almanis executive chairman, Mike Halsall, provided two concrete examples. The start-up asked the general population whether it thought the iron ore spot price would be below USD $44 per metric tonne by March 2016. Almanis predicted with a 99% level of certainty that it would indeed be below $44 per tonne. This would have allowed any commodity traders paying attention to know whether they should close out their position, says Halsall.

Similar predictions for brent crude could have a wide-ranging impact for not only commodity and oil traders but the wider economy as well.

The same can be seen for future questions, he adds. For example, a big debate in the UK at the moment is whether a Labour government would keep Trident – the submarine-based nuclear missile deterence system. Currently Almanis is hovering at 40% “Yes they will vote against it”.

In other words, it’s less than likely to happen. This is an important indicator for a variety of businesses related with the Trident programme. “If it was going the other way – if I’m involved in trident – in 3-4 years I’m going to get shut down,” says Halsall. “It’s a question that directly impacts my business.”

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He also pointed out the potential for wider general investment. For example another of the questions asked was whether Lending Club, a crowd-funding site, would exceed £2.5bn in loan issuances in the fourth quarter of this year. The crowd is currently thinking that this has a 73% chance of occurring.

“If I were a business and looking for money or to buy up debt then this tells me that demand is growing at such a high rate,” he adds. “You’re likely to make an investment on better terms than from a high street lender no matter which you were looking for.”

The next step then is to convince companies that this same ability to predict using numbers of opinions instead of relying on the opinion of a few experts. In essence companies can set up markets to improve the performance of integral parts of the business that could perform more efficiently if future knowledge was known now.

For example, banks could use Percypt to calculate risk grades on client exposures. Currently banks use a combination of analytics and human judgement to assess degrees of risk for credit ratings. But this can lead to problems such as group-think, cognitive dissonance and self-reinforcing optimism (often described as emperor’s new clothes syndrome).

“Where judgemental deliberation is required there is the risk of bias. While this can take many forms the consequence is that the credit rating is incorrect,” says a spokesperson for Slowvoice.

The Percypt prediction system would then enable the bank to identify differing views on risk between the anonymous crowd and the traditional assessment system – thereby improving the bank’s credit risk assessment and thus the overall business, says Karl Mattingly, chief executive officer (CEO) and co-founder of Slowvoice.

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But in order to convince risk-adverse banks to try a new system, Slowvoice must first demonstrate how well it works. And that is where the open Almanis platform comes in, he adds.

With only two months of predictions, it’s still too early to tell whether Almanis’s sterling accuracy rate remains so high. But if it does, Slowvoice could very well be onto a winner that will be the best possible selling tool for its money-making model.

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