The New Rules of Customer Engagement

The rise of the culture of the Web has revolutionized the nature of how businesses and customers relate to each other. Whereas companies could once shape and direct the perception of their brand through carefully crafted ad campaigns, direct mail and press releases, today the flow of information runs fast and free, and customers now own the conversation. And because whoever owns the platform dictates the culture, the world is a more human, less institutional place to do business. Today’s customers want to feel good about the products they buy, where they came from and how they were made. Moreover, they want to like the company behind the product – that is, genuinely like it, not just press a Facebook button. We are entering an age of emotive economics. Corporations have been forced to adapt to this new, people-centric culture by connecting with customers on their own turf in more casual, conversational ways. There’s little room or respect for old-school corporatism’s pomp and protocol. And those businesses that cling to traditional behaviors in the online community will earn its scorn very quickly. Those companies that have embraced this new landscape and its risks have enjoyed great reward. For this reason, the human face, rather than steal and glass, has become the façade of big business. Yet with style must also come substance. It is no longer acceptable to be about profit alone. Companies are expected to contribute to the world in ways that make their customers’ lives better. Consumers are demanding that business be done on their terms. It should come as no surprise, then, that human values – transparency, respect, conscientiousness, kindness, trust, generosity and the like – are the keys to engaging with customers in this brave new world of business. So how can you use this new dynamic to your advantage to grow your business? You must embrace the new rules of engagement, understanding that when you put people first, profit will follow:

1. Don’t treat customers like they’re stupid.

When Netflix bungled its attempt to change its pricing structure last year, the backlash in the online community was severe, with many customers threatening to cancel their accounts immediately. It was clear that customers viewed Netflix as a value-priced alternative to more expensive traditional paid media channels, and therefore many felt betrayed by the company’s sudden doubling of their fees. While the price increase itself wasn’t really so unjustifiable, what made it unpalatable for customers was the company’s lack of transparency in explaining the business drivers behind the rate hike – the rising operational costs of maintaining a physical DVD business and the growing licensing fees for content streaming. Instead, Netflix spun the move as giving customers a “choice,” offering the option to subscribe to a DVD-only service at its lowest price ever. Here's an excerpt from their ill-advised news release: To his credit, CEO Reed Hastings acknowledged the poor handling and chalked up the misstep to “overconfidence,” which still sounds like PR spin, but hey, we’re making progress. The lesson? If a change in product or policy may have negative consequences for your customers – even if only in perception – acknowledge them, express empathy over the inconvenience or added cost, explain the reasons if possible and then point out the benefits or offer alternative options. People understand that businesses must evolve and that profit is still part of the equation. Don’t assume that your customers are beyond reason or treat them like they’re too stupid to detect what’s really going on.

2. Over-serve your customers.

It’s always good business to go the extra mile for customers, but never more so than when trying to recover from a mistake. How you handle disappointment is what determines whether your customers write you off and tell everyone about it or trust you more and spread the good word about you. When Google decided to phase out Wave due to poor user adoption rates, they didn’t just shutter the windows and bury the technology as if it never existed. Instead, they gave users months to move their data off the service, converted Wave into an open-source project and gave the Wave community the tools to get involved:

3. Be humble and listen to your customers.

Building on the success of its breakthrough app Wunderlist, Berlin-based software firm 6Wunderkinder recently launched Wunderkit, a project collaboration and management tool designed around the social connections of today’s work environment. Wunderkit was made available in a “freemium” model, offering standard features to all users for free with additional functionality available for a small fee to power users. As the company analyzed feedback from its beta release of Wunderkit, they realized the community’s dissatisfaction with project collaboration between users – one of the platform’s central features – only being available to premium users. What follows is a fantastic example of how to demonstrate that you’ve heard your customers loud and clear, understand their feelings and are acting to resolve the issue:

The customer is king, and the king is here to stay.

Never underestimate the commitment required to engage with customers effectively in today’s marketplace. Human relationships are not managed well with rigid rules and policies but instead must be governed by human values like compromise, sensitivity and transparency. It takes much more than a Twitter account and Facebook page to win the hearts of customers. You must develop a culture that is focused on and driven by the customer through and through. Be purposeful in growing such a culture. Establish your own rules of engagement. Make sure every employee is encouraged to embrace and exhibit those values. Empower them to do whatever it takes to take care of your customers. The journey may be a bit rocky at times, but if you walk the line faithfully, you’ll earn something from your customers that can never bought with advertising dollars – trust, respect and even passion for your brand and what it stands for.

---

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.

---