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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Should There Be an App for That?

Getting into your customers’ pockets is no easy task.

With the tablet wars heating up and smartphone sales skyrocketing, at some point, the thought is bound to cross your mind: Is it time to create an app? The lure of being with your customers everywhere they go and being readily accessible with the tap of a finger is certainly hard to resist. And with over 350,000 iOS apps in Apple's App Store and over 200,0000 apps in Google's Android Market, it’s clear that many companies have eagerly climbed aboard the app development bandwagon. Thanks to a proliferation of DIY app templates, the barriers to entry in the app marketplace are not as steep as they once were. But you can't simply judge the merits of creating an app as you would would any other marketing tactic. Instead, you should look at your potential app as a product in and of itself. You wouldn't put time and money into developing a product without a reasonable amount of confidence that a market exists for it. Same goes for an app. You can build it. You can get it into the app store. But if it's not something people want, your efforts will be for naught.

Your app must meet these eight basic criteria, or else it's not worth the investment:

1. It must be designed around business growth objectives.

To justify the necessary investment, your app must be aimed at promoting the growth of your business, whether it does so by making it easier for your customers to buy from you or keeping your brand at the forefront of their awareness. Sure, plenty of big names have created vanity apps that don’t serve a business growth function, but that’s not a luxury the average company can afford. For example, Mercedes offers an iPad game called SLS AMG HD that allows users to put their driving skills to the test through a series of tunnel challenges. Sure, it’s a slick-looking gimmick. But is it actually doing anything to improve the company’s bottom line? Does anyone who is inclined to buy a Mercedes really need an iPad racing game to tip them over the edge? An app in and of itself is not a marketing campaign. If your goal is to boost the visibility of your brand, creating an app is a very indirect and costly means to reach your desired end. Remember that you'll be competing with hundreds of thousands of other apps to be discovered by smartphone users. Your chances of creating something so new, so different and so out-of-the box that it will go viral and jump to the top of the download charts are slim to none. What's more, even the number of downloads your app gets is no guarantee of ongoing exposure to your customers. According to a recent study, as many as twenty-six percent of apps are opened only once after download. With odds like that, if your sole purpose is elevating the visibility of your brand, there are no shortage of other tactics – from SEO to pay-per-click advertising to social media-based PR campaigns – that will likely deliver a better ROI.

2. Its utility must be customer-driven.

No matter what, your app development process shouldn't be an exercise in ego-stroking. Forget what you think is cool or cutting-edge and look at your app through your customers' eyes. For your app to be successful, it needs to offer something people want, whether that's in the form of utility, convenience, content or all of the above. For the most part, your customers use their mobile devices for one of two purposes: productivity or entertainment. If you want to create an app that entertains, be prepared to bring the big guns because you're competing in a space with the heavy-hitters, from Facebook, Twitter and YouTube to a slew of professional game developers. The productivity space is easier to enter, but conquering it is still no easy feat. A useful productivity-oriented app must make it easy for your customers to accomplish the types of tasks they commonly perform while on-the-go. If your app is content-driven, it needs to be encyclopedia-worthy to warrant a spot on the reference shelf of your customer’s mobile device. It must be comprehensive and updated frequently, and its interface must be ultra-searchable and scannable. Whole Foods offers a great content-based app. Users can search its extensive library of recipes by keyword, input ingredients they have on-hand to get suggestions and create shopping lists on-the-fly. Search results are even classified by dietary preference, such as gluten-free or low-fat. This type of utility aligns squarely with Whole Foods’ target market. Undoubtedly, their app is the go-to resource for many health-conscious, time-strapped working parents who leave the office with no idea of what they’re going to make for dinner when they get home.

3. It must offer an optimal balance of usefulness and simplicity.

Your app should not attempt to be all things to all potential users, or it will be doomed to failure. Likewise, it also shouldn't simply be a mirror of your website's features. If that's your plan, you'll be better served by optimizing your existing site for mobile browsers. Generally speaking, the more features you try to cram into an app, the less intuitive it becomes to use. And as hard as it may be to imagine, app users are even less patient and more fickle than Web surfers. If your app is difficult to figure out or frustrating to use, they'll wipe you from their phone with nary a second thought. Therefore, it’s critical that your app’s purpose is clearly defined and that its functionality is streamlined. The FedEx Mobile for iPhone app is a perfect example of this balance of utility and simplicity. FedEx is a massive global corporation that offers a broad array of services to a highly diverse customer base. Its app, however, is limited to four main functions: obtaining a quote, creating a shipping label, tracking a package and finding a location – exactly the type of time-sensitive features you need at the ready when you’re trying to get that all-important document out in tonight’s shipment or awaiting an important delivery.

4. It must be mobility-oriented.

There are some tasks people like to do on their phones and some they do not. Any task that involves too many steps and is not urgent in nature is not going to be something your customers would choose to do on their phone rather than just waiting until they are in front of their computer screen again.

5. It must take advantage of the mobile device’s unique features.

Mobile devices have a number of built-in features that even many laptops don’t necessarily have, such as GPS, the ability to deliver push notifications, a camera and video camera. If you don’t plan to tie the utility of your app to any of these functions, it’s hard to justify creating a dedicated mobile app rather than just enhancing your current site to provide an optimal experience for mobile users. Amazon's Price Check is a great example of an app that takes advantage of the phone's camera function to allow users to scan barcodes and compare prices on-the-go. GPS is a little more tricky to use wisely. A lot of branded apps – from Gap's StyleMixer to USPS Mobile – use GPS to allow users to find their closest brick-and-mortar location. As an auxiliary feature it's a nice convenience for users who are already plugged into the app, but this type of function is not enough to justify the existence of an app in and of itself, as there are plenty of other mapping and searching apps that can deliver the same information with broader utility. Also, you should weigh the pros and cons of integrating push notifications carefully. If you can provide legitimate value to your customers with timely alerts, that's fine. But if you cross the line into intrusiveness, you're just asking to get deleted.

6. It must be applicable to a broad customer base.

The Chipotle Mobile Ordering app lets users place a completely customized order and pay directly from their phone in advance of arriving at the store. For a national chain with thousands of time-starved customers who have little patience for waiting in line, the app represents the perfect marriage of utility and marketing savvy. But apps like this require a certain scale to make sense. If you're a mom-and-pop bakery, you could theoretically develop an app that would let your customers design their own cupcakes and place their order right from their phone. While it would surely be a fun gimmick, it doesn't fulfill a broad-based need, and the app's ability to generate additional revenue would probably never offset the cost to create and maintain it.

7. It must be well-designed and thoroughly tested before launch.

The app marketplace is no place for experimentation. You have to get it right out of the gate. If you go live prematurely with an app that's underdeveloped, lacking utility or plagued with bugs, your bad reviews will haunt you for a long time. What's more, disenchanted users won't be likely to give you another shot even if you come out with something better later.

8. It must continue to evolve.

If you’re going to get into the app development game, you must be prepared to be in it for the long-haul. Once you launch your app, your job is only just beginning. App developers have raised the bar of user expectation for updates. You must monitor your feedback and respond with interface tweaks that provide a better experience. You should also add to and improve upon the functionality of your app regularly and keep it fresh with current data. If users see an app that has gone stale on the shelf, they're not going to be inclined to download it.

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