Using machine learning, DecisionNext’s SaaS platform aggregates hundreds of data point, quantifies market risk through probabilistic forecasting, and increases forecast accuracy.
Commodities are risky assets, and the higher the risk you are willing to take, the greater your reward may be. This makes simple decisions about when to buy and sell incredibly high-stakes, stressful endeavors, and many companies have suffered severe financial losses for a single misstep. Agricultural products like soybeans, wheat, rice, cocoa, coffee, corn, and sugar, as well as livestock and meat, are all traded on the commodities market but forecasting each assets’ future outlook is fickle business and as tricky as trying to predict the weather on a specific day several months from now.
But what if there was a way for technology to help commodities buyers and traders peer into a digital crystal ball to have a much better idea about what the market was going to do several weeks or even months from now?
According to San Francisco software startup DecisionNext, that fantasy is a whole lot closer to becoming a reality. The startup has a SaaS platform that provides users with forward-looking views of commodity markets that they can translate into actionable business decisions. And it recently closed a $7 million Series A led by Dalus Capital, a Latin America-focused investment firm.
“Dalus Capital has mostly commodity company owners among its LPs, so as an investor they provide us with an interesting network that we were not yet able to access and it’s within Central and South America, which are very commodity-rich locations,” Mike Neal, co-founder, and CEO of DecisionNext, told AFN. Neal is a firm believer that the traditional era of commodity business is coming to a close.
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Powered by proprietary machine learning algorithms, the platform aggregates hundreds of data points, quantifies market risk through probabilistic forecasting, and increases forecast accuracy. The interactive and transparent platform provides real-time validation of forecasting models and backtesting of market decisions with the flexibility to simulate market scenarios and test market hypotheses. By quantifying the risk, businesses can make more informed decisions about purchasing, pricing, and sales strategies for their commodities.
“For example, if we are trying to figure out the price of chuck rolls in the beef industry in the United States six months from now, we will simulate prices of chuck rolls along with the rest of the beef carcass to produce a distribution of likely prices six months from now. The reason it is useful is that the whole industry makes forecasts or even guesses about where prices are going over a time horizon that is usually at least six months out, sometimes longer, and makes decisions based on those forecasts,” Neal explains.
Although a number of commodities trading consultancy firms exist to help companies make the right moves, Neal sees them more as partners instead of competitors considering that the SaaS platform can be marketed to them as well. They can then offer their customers even better advice backed by hard data. The advantage of peddling a SaaS product is the wide breadth of potential customers that it brings. For DecisionNext, anyone who wants to make a better decision about commodities markets is a potential customer.
A Massive Global Industry Means a Massive Scaling Opportunity
For now, the company is focusing on providing commodities forecasting for food and agriculture commodities as well as natural resources. This is hardly a limitation considering the sheer size and magnitude of both divisions. Cocoa alone is a $50 billion global industry, for example,
But convincing target customers that a software program can save them a lot of money while helping them steer clear of losses can be an uphill battle. And as Neal points out, your product doesn’t mean much if people aren’t willing to pay for it.
“The founding team and I have started four companies and one thing we have learned over time, and something that informed how we built this company, is the importance of opening the black box so users can see what is happening and why the software is providing the recommendations it’s providing. This helps a lot with user adoption because if they can understand it, they can trust it. If it’s just a black box, they tend not to trust it,” he explains.
The new dose of capital will help DecisionNext build out its sales team so that it can expand on its existing markets in North America, Asia Pacific, and Europe.
“We intend to grow the company significantly, not to just give returns to investors,” says Neal. “As a company, we are very much on a mission to change the way that the hard and soft commodity industries make decisions and to improve performance significantly in these markets. Our experience is that if you can improve performance and raise the water level in the industry, it’s a good thing for everybody.”
With such significant gains to be won by achieving better predictability, several startups have also offered tech-driven solutions for the commodities world.
Fintech for Agriculture
London-based Stable recently raised a $6 million seed round to help farmers, traders, and insurers manage food price risk with fintech. Its platform acts as a marketplace for farmers to buy affordable volatility insurance for their products. Having access to this type of insurance not only helps farmers secure their income but also helps them de-risk their businesses, making other types of financing, like business loans, more accessible. Price volatility is a significant risk for producers, buyers, and insurers of agricultural products. Prices can swing by as much as 20% or 30% per year, making it difficult for farmers to manage and plan their businesses and for commodities investors to gauge investment decisions.
Singapore-headquartered agrifood business Olam International has partnered with sensor startup Consumer Physics to help improve its cocoa production by capturing data on the exact amount of cocoa within a cocoa bean pod. The company plans to use Consumer Physics’ SCiO hand-held, connected smart sensors to analyze the full moisture range in cocoa beans on the farm in a non-invasive way in under 60 seconds.
Decision support software developer FarmLogs has launched what it describes as a first-of-its-kind grain marketing service called AutoHedge for corn and soybean growers that takes the challenges out of learning how to trade commodities on top of running a farming operation.