marketingtechoutlook

4 Ways Computer Vision is Useful for Consumer Packaged Goods Companies

By Danielle Sauvé, Director, Customer Insights and Experience, Product Identification, Danaher Corporation

Danielle Sauvé, Director, Customer Insights and Experience, Product Identification, Danaher Corporation

A woman visits a store and sees something interesting on the shelf. She wants to know more about the item, so she pulls out her smartphone, opens the Amazon Flow app and points the phone’s camera at the product. Within seconds, her phone is filled with information. She now has the option to read about the product and either buy it at the store or add it to her Amazon cart on the app.

"We must capitalize on the quality, efficiency, and recognition that computer vision brings to brands"

This same technology used to create this point-and-learn experience is disrupting and improving consumer packaged goods (CPG) companies’ brand packaging process. The technology, known as computer vision, is increasing quality, driving efficiency, cutting waste and saving money at the same time that it is improving the consumer experience, driving purchases and capturing revenue opportunities for brands.

Computer vision, a computer’s ability to identify information from images, video, and the real world, may be newer technology, but it’s already incredibly useful across many industries. Think about your ability to unlock a smartphone with your face, Facebook’s ability to automatically recognize and tag people’s faces in photos and an autonomous vehicle's ability to recognize and stop at a stop sign. All these capabilities leverage computer vision, which by 2019 is expected to be a $17.7 billion market in the consumer sector alone, according to Statista.

Here are four ways computer vision can add value to your brand packaging workflow:

1. Identify colors and find inspiration anywhere

Did you realize that computer vision can identify colors through your cell phone camera? Before the packaging process even begins, you can capture colors to inspire your package’s color palette. The Pantone Studio app uses computer vision on your camera to recognize the closest Pantone color that's represented out on a hike in the woods, on your urban expedition or on your fashion hunt. Simply take a picture with your camera, open it in the Studio app, pick the object you want to identify and then the app will automatically show you the appropriate Pantone number. From there, you can look at the inspiring color on different materials, build a palette and share it with other people on your team. It’s easy to see that computer vision technology amplified with a color application becomes a powerful tool for designers, very early on in the creative process.

2. Enable proofreading perfection and maximize efficiency

As you can imagine, computer vision makes packaging proofreaders’ jobs easier and slashes the number of errors that make it to market. Proofreaders put hours of effort into hunting for inconsistencies. With computer vision, they’ll be able to save many hours and packaging companies will be spared many headaches. A computer vision program can tell you, ”I scanned this, and this is what I found. These are the possible parts that could have changed.”

There’s often a pamphlet inserted into the packages of pharmaceutical products that has instructions and warnings printed in multiple languages. This presents a problem for proofreaders; even if they proof this pamphlet with great care, it’s really a greater waste of their time and talent. With computer vision, the pamphlet is scanned in seconds, showing proofers deviations from the original text. Even if proofreaders can’t read Mandarin, they can compare deviations flagged by computer vision.

3. Ensure high quality print

Companies can also use computer vision to ensure quality from the first stages of package printing. Computer vision can scan the package pixel-by-pixel, treating the entire surface—logo, typeface, background color and everything else on the package—like individual images. It examines these images, hunting for flaws. If a piece of copy is a skew, the color drifts out of specification or an extra line is added to a brand’s logo, the human eye may not consistently notice these errors, due to fatigue or distraction. Computer vision is uniquely able to compare the package to the digital prototype and spot these errors within seconds. When error detection is combined with process control, operators can adjust in real-time, creating higher quality prints and less waste on each print run.

4. Create an easier consumer purchase experience at the point of inspiration

Beyond higher quality, efficiency and the ability to see errors that humans cannot, computer vision allows apps to recognize products by their packaging, enabling consumers to see a product anywhere and immediately purchase it. Consumer-facing apps, like Amazon Flow, can identify products on a physical retail shelf, on your friend’s kitchen counter, at the office or wherever your day takes you. Then it presents you with options for purchasing that product, conveniently delivered to you by Amazon.

Consider the shopping habits of the woman I mentioned earlier; she’s looking for more product information and she’s looking at products with mobile apps like Amazon Flow as if retail shops are showroom floors. She is the average modern customer.

If a package has a logo, text, and colors that are easily recognized by computer vision apps, that product is more likely to end up in her cart or on her shopping list. Brand leaders need to produce packaging that is not only easily identifiable to the consumer, but also identifiable by computer vision to strengthen their position of preference.

Ten years ago, did you ever think you’d see consumers—from 10-year-olds to 100-year-olds—pointing cameras at retail products to find out more information? Computer vision may be an emerging technology, but it’s growing quickly and is already useful to CPG brand leaders. We must capitalize on the quality, efficiency, and recognition that computer vision brings to brands. Without taking steps toward adopting computer vision while it’s nascent, you risk being left behind when it’s ubiquitous.