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.

---

Left in the Dark? The Pitfalls of Taco Bell’s #OnlyInTheApp Social Media Stunt

Taco Bell recently unveiled a new mobile app, available for both iOS and for Android, that “gives consumers complete access to every Taco Bell ingredient to create what they want, when they want it – all in the palm of their hand.” The app allows customers in drive-thru or in their dining room to order and pay for items directly on their mobile device. The app itself may be a great idea, but the marketing push behind this new addition from Taco Bell is certainly raising some eyebrows. The company decided to “go dark” on their social media platforms and on their website, replacing their normal content with a black background and a large message that says that “the new way to Taco Bell isn’t on the Internet, it’s #onlyintheapp”, using a hashtag that they have created for this campaign. While I can appreciate the company’s desire to focus heavily on marketing this new feature, doing so at the detriment of all their other messaging and marketing channels is short-sighed. In this article, we will take a look at the possible benefits of this “all in” approach and why Taco Bell may have done this, as well as the pitfalls of this type of campaign and putting all your eggs, or in this case all your tacos, in one basket.

Information on demand

We live in world where immediate access to information is now expected. Have a question? You can whip out your phone or open the web browser on your desktop computer and hop over to Google for the answer. The same holds true for the services we use or products we buy, including menu items and locations or contact information for restaurants. Having worked on websites for restaurants in the past, I can tell you from experience that this information, menu and locations/contact, are some of the most heavily requested pages on those sites. Taco Bell’s current marketing approach, and their decision to “take down” their normal website in favor of a marketing message and nothing BUT a marketing message, is not a customer-friendly decision because it runs contrary to the information on demand culture that our customers have come to expect from websites. Now, to be fair, Taco Bell did not actually take down their entire site. If you run a search engine query for “Taco Bell Menu”, you can find those pages still live on the Web, but you have to work for it! Their current homepage, which is where their visitors will likely go, includes no links to the other pages of the site. If a customer needs menu information, or if they are looking for something like a location’s address or phone number, they will have to go out of their way to dig that information out. That is asking a lot of a person and few customers will go to those lengths.

What they want versus what you want

Taco Bell’s current campaign is a perfect example of placing a company’s needs before their customers’ needs. The marketing message that now dominates Taco Bell’s media properties is what they want people to know about. There is nothing wrong with promoting a new service or product, but by removing easy access to the rest of the information their customers may want, they are ignoring their needs in place of their own. What if a customer comes to the site to find nutrition information, only to be greeted by a message to download this new app. Is that a good customer experience? Perhaps they do not have an iPhone or Android device. This message is lost on them and they are at a dead end. This is a lost opportunity. Saying that this information is “not on the Internet” and instead forcing them to download an app is like saying “we don’t care how you want to access this information, we want you to download an app and we won’t give you that information unless you do so.” That may sound harsh, but that is absolutely how this decision comes across. Yes, there is value in putting a marketing campaign front and center in big way like this. Taco Bell’s new app is certainly being talked about, but most of the chatter I am hearing is not about the app itself or how great or convenient it is, it is about the company’s decision to market it in this way, with the rest of their messaging and information absolutely non-existent. A better approach would have been to market this new app in a big way with a bold, prominent placement across all their media channels, but to also include easy links to the normal website and social media content. With that approach, they could still ensure that their message comes across loud and clear, which is what they want, but they would not be ignoring what their customers want because that information would still be easily accessible.

Ignoring the conversation

Another interesting (and not in a good way) aspect of Taco Bell’s “going dark” campaign is what they are doing on social media. Their Facebook page currently includes only 1 post with a message similar to their website about the new app. The Taco Bell logos and everything else have been removed. What this page does have are comments – 1,194 of them as of this writing. If you read through those comments, you will find people complaining about the removal of the website content, the lack of delivery services, and many random slams on Taco Bell in general. Bottom line, there is a lot of negativity on this page, but Taco Bell is nowhere to be found in those comments. Their “going dark” campaign also includes them removing themselves from the conversation. This is not how social media works. Social media is all about engagement and conversations. If you put something out there, especially something like a new service like this, you should be prepared to answer customers’ questions and have those conversations. Taco Bell has yet to do this. Instead, they have “gone dark” and are nowhere to be found.

A better approach

When you have an important message to convey to your audience, you want that message front and center. There may be the temptation to take the same route that Taco Bell did and remove all your other content in favor of that message. Yes, people that visit your site will see it because that is all that there is to see, but is that the end goal? No, you do not want customers to only see your message, you want them to see your message and take action. Preferably, you want them to take the action that this campaign is focused on, but if they cannot do that, you do not want them to hit a dead end. In the case of Taco Bell, someone without a mobile device that can download the app, or someone with no interest in downloading that app, has hit that aforementioned dead-end. There is nowhere else for them to go other than away from Taco Bell. That is a lost opportunity. For your own marketing campaigns, you want to ensure that if you put a message front and center, you also make other paths available for people who that message may be lost on. Bottom line, you do not focus on one message or campaign at the expense of everything else you have to say and offer – and you never take yourself out of the conversation! When customers are talking about your company and what you are doing, that is a golden opportunity to respond and start a conversation. If you instead decide to “go dark”, you miss that opportunity completely.

In closing

I expect that this campaign is a temporary one for Taco Bell. Soon enough, their website and social media will be back to normal, but in the meantime, all I see in this marketing push are missed opportunities and ill-informed decisions. When planning your own campaigns and messages, speak to your marketing team or agency and always ask yourself whether your plans focus too heavily on what you want instead of what your customers need. The key to a successful campaign is finding a way to address both of these needs and tie together your company’s goals and those of your customers.

---