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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Bad Romance: 10 Phrases Never to Say to Your Customers

Your brand’s image isn’t defined by your logo. Your message isn’t controlled by your latest carefully crafted ad campaign. Your reputation isn’t wrapped up in your website.

In today’s age of social media, each and every one of your customers holds a megaphone, and he who holds the megaphone controls the message.

As a result, every single interaction your customers have with your brand shapes not only the way they perceive your company and its values but how they share and spread that perception to others in their circles.

So in this world where every word-of-mouth message has the potential to be amplified hundreds or even thousands of times over, what can you do to protect your brand’s good name?

Of course, when times are good and everyone’s happy, it’s not too hard to provide a positive experience. But what about when things go wrong? What do you do when you’ve got a disappointed, frustrated, irate or even irrational customer on your hands? That’s when your problem-solving savvy is really put to the test.

In situations like these, it’s critical to ensure that every employee at every level of your company is thoroughly trained in tactics for transforming a conflict or complaint into a positive experience.

Here are 10 phrases you must absolutely, positively never ever say to a customer when your brand’s reputation is on the line:

That's our policy...

You care about your policies, but you know who doesn’t? Your customers. All they care about is getting their problem fixed.

Using policy as the justification for why you can't achieve this goal will fall flat every time. Of course you can’t let your customer walk all over you and it is sometimes necessary to draw a line, but you have little to risk by finding ways to work within those policies to offer an innovative solution.

There's nothing I can do...

This statement will make any customer’s blood boil. When you say “There’s nothing I can do,” what they hear is “You and your business don’t matter to us.” You might as well throw in the towel and walk away.

There's always something that can be done, even if it requires a little creativity and compromise. Make sure to give your employees leeway to improvise when necessary. A minor concession costs nothing compared to the impact of an angry customer blasting you on Twitter.

What you need to do is...

When customers call, they’re looking to you to fix their problem, not to be told how to fix it themselves.

Of course, there are some situations where it’s appropriate and even necessary to walk a customer through the steps required to solve a problem. Just be sure to choose your words carefully. The difference between saying “What you need to do” and “Let’s try this” is conveying the sense that you’re on their side and that you’re committed to working together hand-in-hand until their issue has been resolved.

I'm new at this...

Again, nobody cares. You might as well tell a customer that they should hang up, call back and hope to have better luck in reaching someone more competent.

Rather than exposing your weakness, simply bring in someone with more expertise and experience to help navigate this sticky situation. Your customer will appreciate the hands-on team approach, and you’ll benefit from learning from a pro.

No...

Ouch! Never, ever drop the "no" bomb. If you don’t make a concerted effort to solve the problem at hand, you can kiss that customer – and every other existing or prospective customer who will listen to their tale of woe – goodbye.

I don't believe...

I'm not sure...

We don't...

Take your pick, but these are all thinly veiled ways of saying “no,” which we’ve already established is the not the right answer. Save your breath and spend your time and energy looking for ways to be helpful and find viable alternatives.

There’s no one else here you can talk to...

Unless you are literally a one-man operation, customers know you're lying when they hear this statement. There's always someone higher up that can step in.

Telling someone there's nobody else they can talk to is tantamount to saying, "You’re not worth our time." This isn’t going to diffuse their anger and will likely send them packing, irate enough to leave horrible reviews all over the Web.

I don't know how to help you with that...

This phrase is problematic even when the best of intentions are meant by the employee offering support.

Sometimes a customer will ask tough questions. Instead of saying you don't know what to do, take a proactive role in connecting them with the correct resource within the company who can handle their problem.

That's not my job...

Wrong. No matter who you are or what your job title is, keeping your customers happy is your number one responsibility. Just do it.

I hate my job...

This company sucks...

These types of statements tend to surface when both the customer and the employee are in a state of emotional upset. It’s the equivalent of telling a customer that they're not going to find any help here.

Bad-mouthing the company is never the right answer, no matter what the situation. It can do immeasurable damage, and in reality, even the angriest customer doesn’t really want commiseration.

They don't care about your opinion. They’ve taken the time to give you a chance to make it right, and they want a solution.

If you find yourself reaching a breaking point where you feel the need to make statements like this, take a step back and clear your head. You may not feel that you’re getting the support you need from your company, but customers don't need to hear this. If necessary, ask if you can put them on hold momentarily while you "search for the solution." Then take some deep breaths, get help and do whatever you need to do to rejoin the conversation with a positive attitude.

{Nothing...}

Silence is the absolute worst thing a customer can hear from you.

If it’s necessary to put a caller on hold, don't keep them waiting long. Even better, get their information and tell them you’ll call them back (and then make sure you do – promptly!).

If they’re asking tough questions or getting frustrated, responding with silence will only add fuel to their fire. But that doesn’t mean you must have the right answer on the tip of your tongue, either.

Starting with genuine empathy is a great way to diffuse the situation. Even a simple, "Yes, sir, I can see why you are upset. Let me see what I can do to help you out," will buy you some time to organize your thoughts and collect your composure so that you can confidently work toward a mutually agreeable solution.

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