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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Walk the Line: Balancing the Resources and Rewards of Social Media

For those charged with growing a business in today’s marketplace, social media can present a bit of a quagmire. With all of the hype around social media and the proliferation of social networks, it’s easy to get sucked in to the vortex, spending countless hours obsessing over follower counts, scouring the Web for interesting content to share and seeking out opportunities to cultivate relationships with key influencers. However, no business – no matter how large or small – has unlimited resources to dedicate to social media. You must find a healthy balance between the time and energy you invest and the rewards you stand to gain from your participation. As with any marketing endeavor, success starts with a plan. When determining how to direct your social media efforts, you take into account three key elements:

• Your target market
• Social media sites and the capabilities of each
• Your short- and long-term business growth goals

By carefully weighing each of these factors, you can create a robust social media plan that is specifically tailored to your business and your target audience.

Know your customers.

At the heart of the question of how much time to spend on social media marketing lies a fundamental understanding of your customer. Without an intimate understanding of who you're marketing to, you cannot determine the best methods of reaching them. This will also help you determine in which social media sites to invest the most time and energy. More than likely, many of your customers are spending time on at least one social media platform. Statistics favor of this theory: 30 percent of people across the globe are online, and these users spend 22 percent of the time they’re online on social media. But be careful not to make assumptions based solely on the age of your customers. After all, it's users over the age of 55 who are currently driving growth in social networking via the mobile Web. One of the best ways to learn exactly how and where to engage with your customers is to do some good old-fashioned research. Ask them to fill out a survey and provide them with a reward that’s desirable enough to motivate them to respond.

Where are your customers connecting?

This is another important piece of the puzzle that will help you fine-tune your social media investment. If your customers spend a lot of time on Twitter and LinkedIn but not as much on Facebook, then you can divide your time and efforts proportionately. The trick is knowing how to find out where your customers spend their time. Fortunately, each social media site provides some basic research tools that will help you make this determination:

• Twitter: Use the "Advanced Search" tool to search by keywords and by zip codes to find potential customers, and see how much activity you can identify from these users.
• Facebook: Facebook’s research tools are somewhat limited, but you can check your competitors’ Pages to see what types of posts are the most popular based on the number of “likes” and comments they receive.
• LinkedIn: Use the "People Search" feature to identify key individuals as well as relevant groups that may have a lot of traction with your market.
• Google+: Use Google Analytics to determine the amount of traffic or leads you are getting from your posts.
• Klout: Use this service to see how your followers are responding to your social media activity. Klout can track most major social sites, including YouTube, Flickr and Instagram.

Absolute minimum effort

At an absolute minimum, you should establish a page on each of the big four social media sites: Facebook, Twitter, LinkedIn and Google+. This accomplishes a number of things. First, by listing your address and basic information on social media sites, you’ll help search engines like Google find your website and list your company’s information properly. Also, keep in mind that customers use all sorts of tools to find you, not just Google. If they happen to search for you on their favorite social site, you want to make sure they’ll find you there. The basic information you should have on your each of your profiles includes:

• Company name
• Company logo
• Website URL
• Customer service phone number
• Brief description of your company

This puts you on the social media map, as it were. You can certainly begin engaging potential and current customers after this stage, but even if you do nothing else, this will at least make you accessible. Then, based on the level of engagement of your target market on each site, you can determine how much more you want to do with each account.

Developing campaigns

Finally, once you've determined that you should do some level of effort of social media marketing, you know which sites are best for your market, and you've developed some basic profiles on each site, it's time to formulate a campaign. Just as with any marketing campaign, you must start by identifying specific, measurable goals you want to accomplish. By doing so, you can then determine how many resources can and should be invested in the process to achieve your desired outcome. For example, you may want to reach a goal of 1,000 “likes” on Facebook in the next three months. This is doable for a company on just about any budget, and you'll know pretty quickly if you need to put more effort into getting these “likes.” If you only have 50 after the first week, then you need to step it up. Some companies frame their desired return from their social media activity in terms of dollars and cents. This is not a bad strategy for the long term, but if you’re just starting out, it can actually be deceiving. Why? Because the work of establishing your brand on any social media network is a time-intensive process. It will take a concerted long-term effort to grow your following to the point where you can achieve significant levels of engagement and have a reasonable understanding of the relationship between your participation and the company’s sales performance. For that reason, in the beginning, it’s often more productive to focus on activity-based goals – such as achieving a specified number of followers on Twitter – rather than on more traditional ROI metrics. So take a step back, determine what sites your customers use to connect, focus your efforts on these sites and set some reasonable, time-based goals for yourself. Then, as you begin to gain traction on a particular social media site and establish a foundational understanding of how well it works for engaging customers and driving profitable traffic, then establish some ROI goals for your top engagement-level accounts.

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