Big-data-based food startup Hampton Creek raises $90M

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Credit: Hampton Creek

Hampton Creek, a San Francisco startup that uses advanced data analysis to develop eco-conscious, egg-free food products, has raised a $90 million series C round of venture from a collection of big-name investors. Horizons Ventures and Khosla Ventures led the round, with participation from Salesforce.com founder and CEO Marc Benioff, Facebook co-founder Eduardo Saverin, and DeepMind founders Mustafa Suleyman and Demis Hassabis, among others.

Hampton Creek has now raised $120 million since launching in 2011. Its products Just Mayo and Just Cookies are sold in grocery stores ranging from Walmart to Whole Foods. It previously sold a general-purpose egg substitute called Beyond Eggs.

According to co-founder and CEO Joshua Tetrick, Hampton Creek is based around a simple premise. “Ninety-nine point nine-nine percent of the food we eat is totally shitty for our bodies and for the environment,” he said, and the only way to change this is to make healthy, sustainable food that people actually want to eat.

Josh Tetrick, the CEO of Hampton Creek Foods, image courtesy of Gigaom, Katie Fehrenbacher

Joshua Tetrick

However, he added, being labeled as health food or natural food could actually be the kiss of death for what Hampton Creek is trying to accomplish. It needs to be on the shelf next to everything else if it wants consumers to really give it a chance. The hope is they’ll keep buying it because they like it, not just what it stands for.

“People are buying [unhealthy food] because it tastes good and it’s affordable,” Tetrick explained. “Those are the magnetic drivers.”

It’s a lofty goal that has required the company to invest heavily in its technology platform and machine learning team, because it will need to discover a lot of non-obvious insights about what ingredients might work in what products. Essentially, it’s looking for plants that Tetrick calls “functionally powerful” —  they can replace traditional ingredients with fewer chemicals, less fat, less sugar, a smaller carbon footprint and so on, while still tasting good and retaining, for example, a high amount of protein.

The person in charge of the company’s data science efforts is Dan Zigmond, the former chief data scientist for YouTube and [company]Google[/company] Maps. He’ll be speaking in more detail about Hampton Creek’s data-analysis techniques and vision, which entails everything from R models on single machines to deep learning models on whole clusters, at our Structure Data conference March 18–19 in New York.

HC Flywheel

So far, Hampton Creek has screened about 6,000 plants and hopes to screen hundreds of thousands, Tetrick said. When it finds good candidates — something that could act as a better binding agent or emulsifier than eggs, for example — the company starts thinking about what types of products might benefit from it. It’s already working on pastas as well as a scrambled egg substitute, but won’t bring anything to market until it tastes better, lasts longer and is more affordable than the standard options, he added.

“We consider ourselves a technology company that is doing some pioneering things in food,” Tetrick said, but he might consider reversing that characterization.

Yes, Hampton Creek will have to work like mad and perhaps develop some new techniques in order to analyze all the data it plans to, but that’s just a means to an end. Ultimately, beating food and consumer industry giants on taste, cost and distribution — or even getting them to take serious notice — will mean excelling at the business of food.

4 Comments

Peter Fretty

Great example of the type of success people can find when they take the time to learn about big data and realize the potential value add it affords. Of course, understanding is just the start of building the culture for continued analytical success.

Peter Fretty, IDG blogger working on behalf of SAS

thubten2001

Food packing is a low margin business. Competing on price will be unrealistic. Price is exactly what the industrial food giants have mastered. Tesla does not compete on price.

VN

(Too?) Ambitious. We (human civilization) do not know enough about internal workings in foods, so approaching it explicitly (i.e. through assumptions) will be misleading. Plus, taste experience is highly subjective. So I suppose there will be enough stuff to spend the $90M on (in addition to fighting brick and mortar). Machine learning is the way to go, but you have to pick the right level or analysis. At Flavourspace, we do exactly that (with almost zero budget), at the level of ingredients that you CAN make conclusions about.
Noble vision. Good luck to the guys. Exciting times.

forrestmaready

Anyone interested in this space might check out Next Glass (nextglass.co). We run wine and beer through our Mass Spectrometers to map their chemistry, then use machine learning to predict taste and find chemically similar products. Cheers.

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