Naughty or Nice?

If you’re guilty of committing these marketing no-nos, you may well be receiving a lump of coal from your customers this year.

Making constant demands of your customers

All too often, marketers act like petulant children, making incessant demands of their customers without providing any real service or value in return. “Buy now!” “Call today!” “Read this email!” “Share this on Facebook!” All your customers hear is, “Me! Me! Me! Give me what I want right now!” And what’s their reaction to such self-interested yapping? At best, it's a collective yawn; at worst, a complete tune-out. So what should you do instead? Fame Foundry friend Gary Vaynerchuk suggests hitting your customers in the face. Wait…let us explain. You see, about once a week, Vaynerchuk poses this question on Twitter to his one million followers: “Is there anything I can do for you?” And he does mean it literally. For example, when one of his followers in Canada wrote “Just ran out of Tabasco,” Vaynerchuk overnighted eight bottles. Image via Warren Weeks When another in Minnesota responded with a request for a cheeseburger, he opened the door the next day to find a delicious cheeseburger hand-delivered from one of his favorite restaurants. So what’s in all of this concierge-like servitude for Gary? It’s part of an approach that the always-colorful Vaynerchuk calls “jab, jab, jab, right hook” (which is also the name of his latest book) According to Vaynerchuk, a jab is anything of value — a joke, an idea, an introduction, and yes, even a meal. After he delivers a few jabs, he can then justifiably hit you with a right hook: a request to buy something. In other words, “jab, jab, jab, right hook” means “give, give, give, ask.” Note the emphasis on giving. You must give first and give generously before you ever ask for anything in return from your customers and prospects. As he explains in the book, “Your story needs to move people’s spirits and build their goodwill, so that when you finally do ask them to buy from you, they feel like you’ve given them so much it would be almost rude to refuse.” It’s a philosophy as simple as it is effective: put your customers first, and they’ll return the favor. As Vaynerchuk says, “If you’re in business, first and foremost, you have to be nice. Show your customers that you care.”

Insulting our intelligence

It’s 2013. We’ve all seen more than our fair share of advertising. We all have the Internet. So stop insulting our intelligence with your “candid interviews” and “medical experts.” After all, how many mornings have you found yourself leisurely chatting about the joys of breakfast cereal with an unseen interviewer? And, Post Foods, you really cannot be serious with this! Nobody’s buying it, and nobody wants to buy products from companies that don’t respect our ability to discern fact from fiction. If you want to engage with us, authenticity is the only way to get (and hold) our attention.

Playing to dirty motivations

Image via Amusing Planet This one is something of a corollary to insulting our intelligence. We all know that sex sells. We all want to be thinner, richer and more attractive. But we’re also savvy enough to recognize when we’re being manipulated by marketers. Take this ad for the Dodge Big Finish Event, which ends with a keeping-up-with-the-Jonses challenge: “Let’s see the neighbors compete with that!” Is that really the best selling point you have, Dodge? Similarly, this spot implies that the secret to unlocking popularity, confidence, masculinity and sex appeal is the keys to an Audi. If you really want me to drop upwards of $80k on a car, you’re going to need to do better than that. Educate your customers. Show them how your products and services will make their lives better, easier, more efficient or even more fun in a real way. Not in an aspirational, wink-wink, don’t-we-all-want-to-be-Kardashians way.

Committing a blatant money-grabbing maneuver

Contrary to the unforgettable line uttered by the infamous Gordon Gekko in the 1987 film Wall Street, greed is not good. While customers don’t begrudge any company the need to turn a profit, when they smell a blatant money-grabbing maneuver, they’ll quickly blow the whistle. For several years now, major retailers have been attempting to get a jump on Black Friday spending by opening on Thanksgiving day – a move that has been viewed by many as a morally questionable practice of allowing consumerism to encroach on one of our nation’s most revered holidays. This year, however, Kmart took a giant leap over the line of good judgment when they announced that they would open at 6 a.m. on Thanksgiving day and remain open for 41 consecutive hours. The public backlash was swift and sharp. Within hours of the announcement, hundreds of Kmart customers took to social media and threatened to boycott the store if it didn't reverse its decision so that its employees could spend Thanksgiving with their families. People called the decision "heartless," "greedy," "shameful" and "disgusting." Image via The Huffington Post "Shame on you, Kmart. I will never set foot in any of your stores again," wrote one now former customer on the company’s Facebook page. "I have family members that work in retail, and because of greedy retailers like you will not be able to spend the day with us." Another added: “Maybe Kmart should have shown they are thankful for their loyal employees and let them be with their families on Thanksgiving. I realize you are a corporation, and your goal is to make money...but sometimes you need to show and prove that people are important, too." So what lessons can you take away from Kmart’s Thanksgiving PR travesty? In your quest to own your market, always proceed with caution. Today’s consumers are not only smart but selective; they shop with their heads and their hearts. They want to deal with companies that demonstrate their dedication to serving the best interests of both their customers and their employees. They won’t trust their business to those whose only master is the all-mighty dollar, so make sure you always err on the side of ethics and in everything that you do, prove that it’s you who exists to serve the needs of your customers, not the other way around.

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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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