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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Flex Your Expertise

As one charged with growing a business, there is undoubtedly always one question lingering in your mind: where's my next lead going to come from? Would you believe that sharing just a few minutes of your time and your expertise each day could inject new life into your sales pipeline? The answer is Answers – that is, LinkedIn's Answers forum. Designed as a platform for professionals to exchange expertise, this lesser-known feature of the LinkedIn network can be found under the "More" menu options in the top navigation bar. If you already have a LinkedIn profile, using the Answers forum to attract new leads is easy as 1-2-3:

Step 1: Do your homework.

As with any social media network, it's important to get a feel for the lay of the land before you dive right in, so invest time up-front familiarizing yourself with the LinkedIn Answers forum community. A good place to start is by perusing the leaderboards of top experts in the categories that relate to your business. Click the "see all my answers" links next to these members’ names, and look for commonalities among the answers that were chosen as best. You might even want to test the waters a bit by asking a good question or two so that you can gage the type of responses you receive. Also, if it's been a while since you established your LinkedIn profile, now's the time to dust it off and give it a polish. When you become active on the forums, it's likely that many new people will click through to your profile to qualify your expertise. Make sure you deliver a strong first impression by reviewing each area of your profile to confirm that it is complete and up-to-date. Sharpen the language you use to describe your experience and specialities so that it is precise and powerful. If you're active on Twitter or you have a blog, you can even add applications that will automatically publish your latest tweets and links to your most recent posts on your profile.

Step 2: Turn on your radar.

Once you feel comfortable with the inner workings of the Answers forum, it's time to start looking for opportunities to participate. Questions posted on the forum are divided into categories, from administration and business operations to sustainability and technology, and these broad topics are further subdivided into more specific areas of interest. Don’t try to keep tabs on every question asked. Instead, focus your efforts on the categories that are most closely related to your areas of expertise and where your prospective customers would most likely be active. A great way to streamline this process is by subscribing to the RSS feeds for your chosen categories and setting up a folder in your reader of choice where they can be collected in one location. Rather than having to waste time every day clicking around the forums to browse each category of interest to you, questions will be delivered directly to you in real time. All that's left for you to do is make a quick, efficient scan of your feeds folder once or twice a day to find a few open discussion threads where you can contribute helpful, practical advice.

Step 3: Share your expertise.

When you identify a question that presents an opportunity for you to add something meaningful to the conversation, simply click through to the corresponding discussion forum page, hit the yellow "Answer" button, compose your response and submit. There's no need for a hard-hitting sales pitch. Just talk about what you know with ease and authority. As is true throughout the social media universe, be authentic and allow your personality to shine through in your answer. While you'll want to be conscientious of your spelling and language and maintain a professional demeanor, your response should come across as friendly counsel from a trusted advisor, not an encyclopedia entry. Imagine that the person who asked the question is standing right in front of you, and keep your answer conversational in tone and free of off-putting technical jargon. Be generous with your expertise, and don’t fret about giving away inside secrets. By demonstrating your depth of knowledge and familiarity with the subject at-hand, you'll boost your chances of someone seeking you out to continue the conversation one-on-one. The more personalized you can make your response, the better. Take a few extra moments to review the profile of the person who posted the question. Use the information available to you to custom-tailor your response to their needs. You'll score extra points if you can give a specific example that is relevant and useful to them in their industry or job position. You can also supplement your answer by including links to other web content that might be helpful to anyone who would like to explore the subject further. If you publish a blog, this is an excellent opportunity to link to posts that offer more information about the topic in question. While there is an option to reply privately to the LinkedIn member that asked the question, it will serve you better to post all your answers publicly. By making your response available to anyone who might be interested in the same topic, you'll maximize your exposure to prospective customers and increase the likelihood of reaping good leads from your efforts.

Elevate your profile

As you continue to contribute to the community, your efforts will begin to compound themselves. As an active participant in the Answers forum, you'll elevate your visibility on LinkedIn and gain exposure to members who might otherwise have never had occasion to encounter your profile. Over time, if you consistently provide authentic advice that is sound and not self-serving, you'll develop a reputation as an expert in your field. People will reach out to you based on the depth of your knowledge and the generosity of time and attention that you demonstrate in your answers. Moreover, when one of your answers is selected as best by the member who posted the question, you'll receive special recognition on your profile as an expert in that category. This is a great form of peer validation – an invaluable commodity in any online community. The more best answers you earn, the higher you’ll appear on the leaderboard of experts in your category, giving you an added boost of credibility. Do yourself a favor and check out the LinkedIn Answers forum today. With over 75 million LinkedIn members worldwide, you won't run short of potential leads anytime soon.

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