Deconstructing the Viral Hashtag

Pick your platform — whether it’s print advertising, video, Instagram, Pinterest, Facebook or Twitter – hashtags have overtaken them all. Handy, catchy and often irreverent, hashtags are quickly becoming a mainstay of modern marketing. Like every aspect of marketing, though, some brands are more adept than others at creating hashtags that drive awareness and engage the public. As a marketing tool, hashtags can be a bit of an enigma. Many times the best ones are spontaneous and random. They have an incredibly short lifespan, and there’s no way to control them once they’re out there. So what exactly makes one hashtag succeed wildly while another one plummets into instant obscurity? Why are some hashtags so memorable and others imminently forgettable? The short answer: The most effective hashtags appeal to a wide range of people because they key into the human psyche in a powerful way. Let’s deconstruct a few of the most viral and memorable hashtag campaigns of the past few years and uncover the secrets of their success:

The appeal of social currency: #firstworldproblems

The nonprofit organization Water is Life (http://www.waterislife.com) was looking for a way to raise money to supply clean water to villages in developing countries. And they hit on an innovative way to do it, with a video and print campaign that capitalized on the popularity of the existing hashtag #firstworldproblems by juxtaposing the perceived problems of Americans against the all-too-real struggles experienced by those in less fortunate nations around the world. In the documentary-style video, complaints from actual #firstworldproblems tweets – including “I hate it when I go to the bathroom and forget my phone” and “My son got the wrong toy in his Happy Meal” – are read aloud by impoverished Haitian families. The stark contrast in conditions really struck a chord with viewers, who discovered newfound perspective on just how lucky they are to have such superficial issues. The campaign took off, with the video drawing a million views in just four days and response that generated enough donations to supply clean water to impoverished communities for a million days, according to Water is Life. The psychological appeal of this campaign and its signature hashtag is an interesting one. It leverages the natural human desire to accumulate social currency — to appear generous or to “look good” to our peers — to drive people to perform acts of generosity they might otherwise not consider. By poking a bit of gentle fun at the public while educating them to issues of real concern, Water is Life created a tidal wave of awareness and action.

The appeal of emotion: #SFBatKid

There’s no doubt about it: people share content that inspires strong emotions, and the BatKid story was one of the most emotionally appealing stories of 2013. The campaign, which was spearheaded by the Make-A-Wish Foundation, reached out to the city of San Francisco to help make a little boy’s dream come true. And five-year-old Miles Scott, who is currently in remission from life-threatening leukemia, got his dream — and then some. Powered by the Twitter handle #SFBatKid, the Make-A-Wish Foundation originally hoped to attract 200 volunteers in their effort to help Miles become Batman for a day. But the story and the hashtag caught fire, and the foundation was inundated by responses from as far away as Norway and Australia. About 10,000 San Franciscans turned out to watch as BatKid saved them from several dastardly villains. Even the mayor got into the act, giving Miles the key to the city as thousands of spectators watched from the steps of City Hall. It’s important to note that although this could have been framed as a sad story, it wasn’t. Sadness evokes a low-energy response that rarely results in action. Instead, the uplifting #SFBatKid campaign inspired a whole city to act by appealing to the most positive human emotion: hope.

The appeal of humor: #tweetfromtheseat

Who knew toilet paper could be so funny? In a cleverly designed campaign, Charmin managed to get people laughing in and out of the bathroom by transforming a taboo behavior into an ongoing conversation with their playful #tweetfromtheseat. With a little wink and some witty repartee, Charmin’s #tweetfromtheseat campaign incorporates current events and other brands into their Twitter feed — and the public loves it. As a result, they’ve turned a mundane household product into something special and built a huge, highly engaged following at the same time. It’s truly an inspired campaign, with an appeal that’s simple but nevertheless effective. After all, people do love potty humor.

The dual appeal of practical value and social proof: #40dollars

Talk about the power of a tweet: the Obama White House used the hashtag #40dollars to great effect to lobby for an extension on the payroll tax cut. The campaign began in February 2012 when the President tweeted “Taxes will go up for 160 million Americans by the end of this month if Congress doesn’t act. What does #40dollars a paycheck mean to you?” People all over the country responded — and a Congress infamous for not passing legislation was convinced to extend the tax cuts for the rest of the year. The #40dollars hashtag worked on two different levels: It inspired a response from Twitter users because it asked them to consider something of practical and personal value — what would an extra $40 mean to them? And it inspired action from Congress because humans are wired to respond to social proof. In other words, people embrace an idea or an action more easily when they see others agreeing with it.

#thebottomline

Even hashtags that seem to be completely spontaneous — and lucky at that — are often the result of some serious planning. (Check out Oreo’s #dunkinthedark Super Bowl tweet for a great example.) If your business is looking to leverage the power of hashtags to create a lasting impression, you can use these notable success stories as your model. Spend some time strategizing. Think about what type of appeal will work best for your brand, and make a deliberate effort to tap into it. Be thoughtful, be creative and be daring! If you can hit the right notes, you’ll not only be the brand of the moment but the one that people will be talking about for weeks, months and even years to come.

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Intelligent Design: Transform Your Website into a Sales Engine with Machine Learning

Machine learning: The phrase evokes images of computers playing chess or IBM’s Watson destroying two legendary Jeopardy! champions in a three-day tournament. The truth is, though, machine learning is no longer a novelty; it’s now an integral part of our daily lives. Every time you receive a product recommendation from Amazon, your email server weeds out spam before it reaches your inbox or you enjoy a playlist on Pandora, you’re seeing machine learning in action. In a nutshell, machine learning is the science of training computers to recognize data patterns and make adjustments automatically when those patterns change. While on the surface this may not sound very exciting, nothing could be further from the truth. In fact, machine learning is the key to transforming your website into a lean, mean selling machine.

Understanding machine learning in 100 words or less

Machine learning uses algorithms to build models from data; as more data is collected, the algorithms are “trained” to adapt to changes. There are two ways in which machine learning can be implemented: supervised and unsupervised. Supervised learning algorithms are used to create models that establish relationships between types of data — the relationship between purchase data and user clickstream data, for example. Unsupervised learning uses algorithms to gain insights into customer behaviors and preferences by looking for patterns within the data. Both of these methodologies are designed to make marketing and e-commerce more exact, more personal and more profitable.

Putting machine learning to work

Netflix, Pandora and Amazon are all familiar examples of machine learning in action. All three use recommender systems powered by complex algorithms. These systems collect data about your browsing activities, past selections and any ratings or reviews you may have provided. Then they segment you into clusters with other customers who have demonstrated similar interests or behaviors and use this data to suggest items that might appeal to you based on the browsing and purchasing habits of these other customers. You see this on Netflix as the category titled “Because you watched...” and on Amazon as “Customers who viewed this also viewed...” To gain a deeper understanding of how these algorithms work, let’s take a closer look at Amazon. To Amazon, you are a very long row of numbers in a massive table of data. Your row represents everything you’ve looked at, clicked on, purchased (or, equally as important, not purchased) or reviewed on the site. The other rows in this gargantuan table encompass the same thing for the millions of other customers who shop on Amazon. With every click, visit and purchase, more data is added to your row, which allows Amazon to constantly mold and shape the products it recommends to you and the special offers you receive based on an ever-evolving stream of information about you that is being collected and stored. Another innovative example is True Fit, a retail software start-up that is on a mission to apply data analytics to increase customer confidence in online clothing purchases while decreasing the number of returns for e-railers. Well-known fashion retailers, including Nordstrom, Macy’s and Guess, have implemented True Fit’s algorithms on their e-commerce sites. When customers shop on these sites, they’re asked to create a profile that includes their height, weight and perhaps most importantly, the size and brand of their favorite piece of clothing. Using that data, True Fit is able to recommend the correct size for a specific brand and article of clothing. Even more importantly, as customers continue to use the True Fit system, it learns more about their personal style and preferences and steers them toward purchases they’ll be more likely to keep and enjoy rather than return.

How machine learning drives smarter marketing

You don’t need the resources of major e-commerce giants like Amazon or Netflix to take advantage of machine learning to to improve your e-commerce site and your online marketing efforts. By enhancing your existing site with systems that allow you to create a virtual marketing intelligence brain, you can create a more personalized – and therefore higher quality – shopping experience for your customers. By establishing this type of marketing intelligence ecosystem, you can mine the data provided by customers every time they visit your site to answer vital questions that will help you fine-tune your site and your online marketing strategy – questions like these:

• How likely is a given website visitor to convert?
• What behaviors characterize customers who are likely to buy?
• What behaviors characterize customers who are likely not to buy?
• How can new visitors be identified as high-potential long-term customers?
• Which type of web traffic has the most value?
• Which products or services appeal most to a given segment of customers?
• Given the contents of a particular customer’s shopping cart, which additional products are high-potential recommendations?
• How can website visits be optimized to provide the best possible experience for each individual customer?

Making it personal

The final question in the list above is one that deserves special notice because of the staggering potential for using machine learning to create a more personalized shopping experience – one of the key drivers for increasing online sales. Not only can the data collected via such marketing intelligence ecosystems be used to drive recommender systems, it can also be used to create personalized advertising based on market segments — or even individual profiles — that can be distributed across a variety of desktop, mobile and social platforms. This type of advertising can be tailored to any number of personal preferences and demographic information, including age, marital status, location, lifestyle choices, typical purchases, brand preferences and so on. Ads can be focused to such a granular level that they reflect specific colors a given customer prefers, and their individual purchase drivers, such as status or cost-effectiveness. Another exciting aspect of machine learning-based personalization is the development of individual customer profiles. You can even combine online and offline customer data to create a more complete picture of a given user. Types of data included in this profile might include online and in-store purchases, membership and activity in rewards programs, product ratings and clothing sizes. Just imagine how much more powerful your marketing efforts could be if you were armed with this level of information. One of the most important aspects of a successful marketing intelligence ecosystem is how data mined from customer activities is combined with sound business rules in order to make smart recommendations that are well received by customers and that do not compromise their trust in your brand. For example, most people who walk into a supermarket like bananas and will often buy some. So shouldn’t the recommender simply recommend bananas to every customer? No – because it wouldn’t help the customer, and it wouldn’t increase banana sales. So a smart supermarket recommender would always include a rule to exclude recommending bananas. At the other end of the spectrum, the recommender shouldn’t push high-margin items just because it’s beneficial to the seller’s bottom line. It’s like going to a restaurant where the server steers you toward a particular high-dollar entree. Is it really his favorite? Or did the chef urge the staff to push the dish because it comes with a side order of premium mark-up? To build trust, the best recommender systems strive for some degree of transparency by giving customers clues as to why a particular item was recommended and letting them adjust their profile if they don’t like the recommendations they’re receiving.

Science fact, not fiction

Machine learning can give your business a serious competitive edge by opening the door new opportunities in the marketplace. It can help you personalize and improve your customer experience dramatically and thereby drive sales and revenues. Creatives and developers alike are rapidly pioneering new and innovative ways for marketers to use machine learning — and the future of marketing built on these ideas has seemingly endless possibilities.

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