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Roboflow, a Des Moines, Iowa-based startup developing tools for building computer vision models, today announced it has raised $20 million in a series A round led by Craft Ventures. This brings the company’s total raised to date to $22.2 million, and CEO Joseph Nelson says the money will be put toward ongoing product development and hiring efforts.

The global computer vision industry was estimated to be worth $11.32 billion in 2020, according to Grand View Research. But while the technology has obvious enterprise applications — computer vision algorithms can be trained to perform tasks like spotting gas leaks, counting pills, and monitoring workplaces to enforce social distancing — companies can face barriers to leveraging it in production. Teams are often faced with rebuilding software infrastructure and attracting the necessary machine learning expertise.

Founded in 2019, Roboflow is the brainchild of Brad Dwyer and Nelson, who see computer vision as a foundational technology that can enable developers to solve problems resulting from devices’ inability to see the world around us. The platform provides a framework for developers to build computer vision into their products, enabling them to upload images and videos to train custom or prebuilt computer vision models.

Above: Roboflow’s dev interface.

Image Credit: Roboflow

“Computer vision is one of those generational technologies that, like the personal computer or mobile phone itself, will be adopted by every industry. Software is limited by its ability to receive structured information as input, and that structure is typically provided by a person,” Nelson said in a statement. “What computer vision does is enable every part of the world around us to become programmable, unleashing a Cambrian explosion of applications. That’s why computer vision needs to be a part of the toolkit of every developer, not only expert machine learning teams.”

Dev-focused platform

With Roboflow, customers can annotate images while assessing the quality of datasets to prepare them for training. (Most computer vision algorithms require labels that essentially “teach” the algorithm to classify objects, places, and people.) The platform lets developers experiment to generate new training data and see what configurations lead to improved model performance. Once training finishes, Roboflow can deploy the model to the cloud, edge, or browser and monitor the model for edge cases and degradation over time.

“The promise of Roboflow reminds me of the early days of Stripe,” Roboflow investor Lachy Groom said in a press release. “Like payments, computer vision is a critical piece of infrastructure that needs to be made broadly available to developers. Consider how FaceID aims to unlock phones seamlessly, or how mobile check deposit alleviates the need to wait in line at a bank, simultaneously allowing bankers to focus on customer service.”

Roboflow competes with CrowdAI and Chooch, among others, in the growing computer vision development tools market. But Roboflow claims to have over 50,000 users, including engineers in half of the Fortune 100 companies, plus startups, universities, and hardware companies. Clients include Pfizer, Walmart, Amgen, and Cardinal.

“These [computer vision] examples are the tip of the iceberg. Every industry will be rewritten, and for that to happen, all developers — not just machine learning experts — need to have tools that make vision accessible,” Groom continued.

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This article is part of a Gaming Insights series paid for by Facebook.

Gaming is a highly competitive industry. At Facebook, we see that games advertisers are often competing for the same audience, using similar creative strategies.

Whether they’re producing many ads to fight fatigue, optimizing successful concepts, or copying the competition, these popular creative strategies might lead to some success but are reactive in nature. It also makes it hard to stand out from the crowd, with many ads looking the same. In fact, one study showed that 56% of gamers say nearly all or many of the ads they see are repetitive[1].

By moving towards a more proactive, structured approach to ad creative you can understand what works and — more importantly — why. You can gather unique data to formulate distinct points of view that can be scaled within your organization and ultimately help drive long-term, sustainable growth.

Expand beyond executional strategies

But before I get into our new framework for experimenting with ad creative, I want to explain why it’s more important than ever. The changing ads ecosystem means that businesses will find it harder to deliver personalized ads and accurately measure campaign performance.

Reduced signals also lessen the ability to target specific audiences and optimize ad creative based on their behavior. Therefore games advertisers need to evolve their strategic approach, placing learning at its core, to develop creative that’s more broadly applicable to audiences.

Creative Prototyping: Experiment to drive success

Developed by Facebook’s Creative Shop and Marketing Science teams, Creative Prototyping is a way to intentionally experiment, uncover new creative territories and drive success. At a tactical level, it involves conducting structured experiments using the Ask, Make, Learn, Adapt framework.

Some of you might recognize this from The Big Catch Playbook, where we showed advertisers how to use the Creative Prototyping framework to test motivation-led ad creative.

This approach has an immediate impact on media efficiency: A meta-analysis of creative experiments on Facebook platforms found winning assets developed through testing and learning had a measurably lower average cost per ad recall, cost per action intent, and cost per action compared with the alternative assets.[2]

Let’s take a deeper look at the different stages of the framework.

1. Ask: Develop a learning agenda and craft creative hypotheses

The first step is to create a learning agenda, ie. decide what questions to ask. Your agenda should prioritize long-run structural creative learnings over short-run, campaign-specific ones.

Once you have a defined agenda of what you want to learn, you can prioritize the items and build out hypotheses to test.

2. Make: Design the creative assets based on your hypotheses

Once you have your hypotheses, it’s time to build the assets. There are three key rules to follow when creating your assets including isolating your creative variables and finding the balance between distinction and similarity.

Remember, you’re not designing final campaign assets, but prototypes to help you learn how to build the final campaign.

3. Learn: Design your creative testing methodology and analyze results

Now you need to design a test that will either prove or reject the hypotheses. Tests should be consistent, executed across all groups and should consider historical learnings, among other best practices.

There are multiple test methodologies, depending on the hypotheses but once you’ve executed, you’ll want to explore primary and secondary learnings to iterate.

4. Adapt: Determine future implementations of learnings

At this stage, you’ll decide whether you need to do another round of Creative Prototyping, or if you’re ready to implement your learnings into business-as-usual campaigns.

You should create a “Creative Prototyping log” to record all learnings. This should become your central repository for insights and best practices. Remember, the goal is to build a learning muscle that’ll help drive success over time.

Case Study: How Babil Games elevated the impact and creativity of their campaign through Creative Prototyping

Best known for its titles Nida Harb and the Strike of Nations series, Babil Games wanted to take a more structured approach to testing different ad creative to help them make more informed decisions about their advertising and player acquisition. Working closely with Creative Shop, Babil Games used Creative Prototyping to test the best ways to create compelling Facebook ads.

For the “Ask” part of the process, they developed a learning agenda for Strike of Nations, narrowing down their questions and hypotheses according to position, concept, and execution.

Following this, they landed on a hypothesis that sought to explore the effect on performance of placing their brand logo into their ads. To test this. they developed four unique creative approaches (“Make”).

While each creative looked slightly different, they all had a similar composition in terms of the gameplay, aspect ratio, length, and end cards. The only variable that changed was how they integrated their brand logo, thus allowing them to effectively attribute performance changes to its presence.

Once the test was run, and they were in the “Learn” phase, they saw that the card and logo creative had a 24% lower CPI and 27% lower cost per registration than no logo (primary metric). It also drove higher registration volume and purchases (secondary KPI).

Therefore, Babil validated their hypothesis that incorporating the brand logo was good for their performance, and understood more about the best creative strategy to do this.

But they didn’t stop there. As part of the “Adapt” stage, they tested a new agenda item to understand how integrating gameplay into CGI footage impacted performance. They saw that incorporating gameplay into their creative outperformed pure CGI, thus validating their hypothesis and providing them with an additional learning to incorporate into their business-as-usual creative strategy.

Key learnings

As highlighted above, the advertising ecosystem is changing and ad creative has never been more important in driving effective campaign performance. By taking a proactive and structured approach to creative development game advertisers can get ahead of the competition and ultimately drive success.

Dig deeper: Learn more about the Creative Prototyping framework, including step-by-step instructions, best practices, and case studies in our new playbook, available to download for free here.

[1] Source: Online survey responses of 728 established mobile gamers in US, from “Mobile Gaming Behavior Post COVID-19” by Interpret (Facebook IQ-commissioned online survey of 13,246 mobile gamers ages 18+ across BR, CA, DE, FR, JP, KR, UK, US, VN Jul–Oct 2020).

[2] Source: Facebook Internal Data, December 2019

Lewis Tutssel is Head of Gaming, EMEA Creative Shop at Facebook.

VB Lab Insights content is created in collaboration with a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected].