Keep it Social

Human communication is complex. The words that you use, the tone and volume of your voice as well as your body language and facial expressions all play a role in how your message is received by those that you are communicating with. A poorly chosen phrase or a simple misstep in your body language can steer a conversation into unexpected, and unintended, territory. In an age when so much of our communication has now become digital, the challenges have become even greater. Many of the social cues present in face-to-face interactions are all but impossible to convey. Body language and facial expressions are a non‐factor, and tone is as hard to express as it is easy to misinterpret. As a result, when communicating online, achieving clear understanding of meaning and intention comes down to the words that you use and how you use them.

Social communication

In today’s Digital Age, social media plays a pivotal role in the way companies communicate with their customers. But with the proliferation of social platforms – from standard bearers like Facebook, Twitter, LinkedIn and Google+ to niche sites like Pinterest, Instagram, Flickr and Foursquare – it’s easy to become overwhelmed by the task of managing your brand’s social presence. As a result, it’s tempting to find ways to automate this communication in order to save time and resources. This is exactly the solution that I discussed recently with a vendor who was promoting a platform that would automatically broadcast updates to dozens of social media sites each time a blog post, news release or similar content was published to our website. Rather than spending the time to post this content to each of our social profiles individually, this tool would do it all for us in one quick shot. While this may sound like a dream come true, the problem is that it is a blunt instrument-style approach to communication: every profile gets exactly the same update at the same time. But the reality is that not all social media sites are the same, and neither are the audiences that use them. The way you communicate with connections on LinkedIn should differ from how you do so on Facebook. Similarly, the content you’d publish on photo-sharing sites like Instagram, Pinterest or Flickr is completely different from the updates you’d post to a micro‐blogging site like Twitter. Each site has a syntax specific to that particular social media platform, and ignoring that syntax greatly compromises the effectiveness your communication. You absolutely have something to lose – the opportunity to connect with your audience in a meaningful way. Some might argue that since the posts are automated, you’ve got nothing to lose by trying this approach, but that is incorrect. You absolutely have something to lose – the opportunity to connect with your audience in a meaningful way. And that lost opportunity could cost you dearly if the tailored messages of your competitors reach your potential customers where your robotic, automated communications miss the mark or, even worse, alienate your followers. In the end, while automation will save you time, it does so by taking away your ability to customize your message for specific audiences and platforms.

Be social, be specific

Stepping back from social media for a moment, think about human communication in general. We change the way that we speak and the messages that we send depending on who our audience is. You speak to your friends differently than you speak to your family. You communicate with co‐workers and peers differently than with clients and customers. Effective interpersonal communication requires an understanding of how best to convey your desired message to those you are speaking with. This is not something you could ever automate; it requires a human touch. When it comes to communicating via social media, the medium and the methods may be different, but the basic underlying principal remains the same: to be effective, your message must be tailored to the audience that will receive it. Although the channels themselves may be digital, you can’t eliminate the human element. For an example of how different messages should be tailored to different platforms – and why not every update is right for every social media profile you manage – let’s take a look at how my company shares our news and announcements. When we acquire a new certification or receive recognition that’s worthy of a press release, we promote that accomplishment on sites like Linkedin, Twitter and Facebook where followers naturally expect to see updates about what’s going on with our company. In each case, we use the specific syntax and conventions of that site – such as hashtags on Twitter – to make sure those updates are in a format that audiences are familiar with and can easily find. We do not, however, share content like this on sites like Flickr or dribbble because those platforms are visual in nature, and these particular announcements have no meaningful visual component to them. If instead we are publishing an update about a new website project that we are launching for a client, we will again post that announcement to Linkedin, Twitter and Facebook, but we will also add updates to social media sites that are more visual in nature because, for this update, we do have good image-based content (i.e., a screenshot of the new design) that can accompany the post. Each time we post an update to social media, we consider the nature of the content to decide which sites are most appropriate for those updates. Additionally, each social media post that we make uses the specific syntax of that social media platform.

Forget trying to do it all; focus on doing it right

The concept of automating your social media communication is only an attractive option if you are trying to publish content to so many social media sites that doing so has become unmanageable drain on your time. If this is the case, the solution isn’t to find a way to automate the work; it’s to streamline your activities to include only those sites that are a good fit for your needs. Trying to use every single social media site available to post as much content as possible is not a sound strategy. Why? Because social media platforms are overrun with self-promotional content that is irrelevant to audiences, and users of these platforms are quickly becoming conditioned to tune out this static. Sending automated updates to dozens of sites at once, without ever considering whether or not those updates are appropriate for those sites, just adds to this problem. Is that how you want your company’s news and announcements to be perceived – as part of the useless glut of social media updates? So if taking the time to individually update dozens of social media profiles for your company is not the answer, and automating those updates is also a no‐go, then how can you use social media to effectively communicate your organization’s message? The first step is to speak with a professional team that can help you establish an appropriate social media strategy – one that suits your brand and fits into your overall marketing plan. That team can help you identify which social media sites your audience is actually using and what types of updates you should send to each platform. They can also help you develop a rhythm for social media updates – one that you will be comfortable executing on a regular basis. By identifying the right sites for your organization and understanding how to use those sites effectively, you can capitalize on the power of social media to grow your brand and your business.

Case in point: KLR

KLR is a large accounting and business consulting firm headquartered in New England. In developing their social media strategy, they realized that while their target audience does likely use Facebook (after all, who doesn’t at this point?), they do not use that platform to search for the types of high‐end accounting and business planning services that the firm offers. As a result, promoting their services to that audience on that platform would be inappropriate, and their content would fall on deaf ears. Instead, KLR uses sites like LinkedIn and Twitter, where they have built a network of business connections that recognize them as thought leaders in their industry, to promote their services. Does this mean they turned away from Facebook altogether? Not at all; rather, they determined a more effective use for the platform: communicating with current and prospective employees, including interns whom they were looking to attract to the firm. Recognizing that college-age students would absolutely be using Facebook to research potential employers and positions, KLR decided to use their Facebook profile to showcase their company culture and their standing as a “Best Place to Work” for eight years running. By evaluating different social media sites, which segments of their audience (if any) are using those sites, and how they can most effectively convey their messages across that landscape, KLR has made the most out of the time they spend managing their social media presence.

A final word

Social media can be invaluable in its role as an open line of communication between your company and its customers. However, it can also be one of the surest ways to waste time and resources if you don’t have the right strategy in place. Make sure you’re getting the most from your efforts by contacting a digital marketing specialist to discuss your company’s needs. Together, you’ll be able to define your company’s voice and bring a human touch to your social media strategy.

---

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.

---