Contrast, color and spatial separation improve attention and retention.

Photo by Quentin Keller: quentin-keller-55752
Photo courtesy: Quentin Keller @kt1klr

According to Human-Oriented Technology Lab and a study conducted by  & website designers have only 50 milli seconds, less than 1 second, to get the attention of a viewer.   They conducted three studies and found each time that visual appeal ratings  could be assessed within 50 ms, suggesting that web designers have about 50 ms to make a good first impression.

Axel Buchner at the Heinrich-Heine University, Institute for Experiential Psychology, in Düsseldorf, Germany found that dark text on a light background consistently outperformed retention over light text on dark background. The higher the contract the better people could retain the information they were presented with.

Higher contrast improves readability.
Higher contrast improves readability.

Mr. Buchner also found that this is true regardless of the color combination used.  However a study by Mariam Adawiah Dzulkifli and Muhammad Faiz Mustafar found that different colors do influence people’s emotional state. Greene, Bell, and Boyer (), demonstrated in their studies that warm types of colors such as yellow, red and orange have  a greater effect on attention compared to the cool type of colors like brown and gray.

Photo Courtesy :  Peter Lambert @peterjlambert
Photo Courtesy : Peter Lambert @peterjlambert

Lloyd-Jones and Nakabayashi (31), carried out a study on the effects of color on object identification and memorization, and found out that there were differences in memory performance in object-color spatial integration and object spatial separation.

London Transport, photo courtesy Matt Obee, www.mattobee.com

Two hundred and thirteen undergraduate students from the University of Kent were involved in the study. All participants were non color-blind.  The study found colored object with non-colored background have better memory retention and yielded faster respond time compared to colored object with colored background.  Full Article

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Why I like old brands

Pinald Aftershave
Pinald Aftershave

I like brands that endure.

I wear Brooks Brothers (1818), Orvis (1856) and LL Bean (1912) clothing.

My aftershave is Pinald (1810).

I drive a BMW (1912 again).

And I live in an old town in New England that was settled in 1649 in a house built in 1927. The house we lived in before this one was built in 1770.

All of this is not a coincidence. I like to think that the brands I favor have stood the test of time. These brands deliver quality and value even as they have adapted to changing market conditions, competitive threats and new ways of doing business.

There’s another reason I have stuck with these brands for years well beyond the fact that I am an intensely loyal person. There are emotional and often irrational reasons I like brands in the first place. It’s pretty clear that I have not picked my brands because they were the lowest cost or that they deliver the highest status. I’ve selected them because they work for me functionally and passionately. The $7 after shave that I buy at the drug store has little in common with my 5 series BMW. On the surface, there is little commonality. Beneath the surface these purchases are very much the same. They all are interesting. I feel good buying them. And I enjoy using them.

When it comes to building brands, I want to create brands that will last. This includes offering both practical benefits and emotional connections to our audiences. I want my brands to be logical, distinctive, interesting and at the same time familiar.

There’s something irrationally rational about brands. That’s the puzzle we marketers try to solve every day.

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Effective Machine Learning has Human Agreement

Effective Machine Learning needs buy in from the chosen humans. Photo by Anders Jilden
Effective Machine Learning needs buy in from  chosen humans. Photo by Anders Jilden

Machine Learning is quickly becoming one of the fastest growing technologies and when it works, it is amazingly effective.  Google, Oracle, IBM, Salesforce, Palantir, and Iris AI are some of the leaders in machine learning who pride themselves in assisting clients in ways that were unthinkable only 10 years ago.   Now computing power, thanks to cloud computing, is at an all-time high.  Processing power due to distributed computing and parallel processing allow more algorithms run simultaneously, speeding up the time it takes to get results.

Machine learning allows software programs to learn on their own without custom programming.  One of the most impressive examples to me is the Google translation machine.   Apparently the program decided to write an intermediary step, translating text into machine language that first identified the meaning of the paragraph. It then translated it into the various languages.  The result is amazing!  The machine figured out a solution that had stumped the programmers. Now the machines are ready to learn and ready to take on amazingly challenging tasks that before were humanly impossible.

For this new era to work  humans need buy-in. Processing power is no longer the problem.  Human willingness to take these evolutionary steps will be the next challenge.   Companies who are user friendly always have been more successful.

When I led the media department of DDB,   I challenged Dottie Hall and Vern Rayburn of Microsoft.  I suggested that if they could create a calculating program like that wouldn’t make Corporate Managers, namely men, feel like they are taking on clerical duties, Microsoft would become an amazing successful company.  Dottie Hall and Vern Rayburn pushed this initiative through. It really worked. American productivity sky rocketed and started a new era of tech induced productivity around the world.   MIcrosoft changed the world, because we wanted people to feel good about using the personal computer.  We wanted them to feel smarter.  Machine Learning will have to overcome this same hurdle.

Database managers, Big Data experts, SEO managers, everyone who is used to Oracle, IBM, PeopleSoft, and Unix based programs will have to start opening themselves up to these changes.  For Machine Learning to work human behavior and willingness to adapt to change will be once again become the biggest challenge.  AI is the only way we will benefit from Big Data.  It will be so exciting to participate in this next evolutionary step.

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Only 24% of people use cash in the US to pay most of their bills.

photo by Grace Strok

The IoT so practical.  Credit and Debit Cards are still popular. Since the 2008 recession and a number of hacking incidents that affected  personal data of 100 million credit and debit cards at big retailers have changed the way people feel about using their cards. This is improving the chance of the latest trend of using mobile wallets.  Either way only 24% of people use cash any more.

“A mobile wallet can turn a smartphone into an access device to connect with a payments network,”  says George Peabody, senior director at Glenbrook Partners, a payments research and consulting firm in Menlo Park, California. “The phone is the most convenient tool (for this purpose),” Peabody  continues, “It’s always within reach, and it’s providing the user interface to existing payment vehicles like prepaid or credit cards.”

Apple Pay, Android Pay, and Samsung Pay  are quickly gaining acceptance, and it’s very likely that within the next few years, they will be the dominant forms of payment across the board.  One reason for their rapid growth is that they work faster than Chip Cards.  Square accepts EMV based chip cards which are safer than cards with merely a magnetic strip and since chip cards often take a long time to work at the cash counter more retailers opt for the contactless version of payment via smart phone.

The security and the convenience supports this trend to contactless payments.  To learn more about how we have assisted financial institutions and tech companies gain customers at below typical acquisition rates contact us now, we look forward to working with you.

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