Business Toolbox: How to Standardize Your E-mail Signature

E-mail is the workhorse of communication for your business. It’s likely to be your first point of personal contact with prospective customers as well as your go-to vehicle for conducting day-to-day conversations with existing clients. As a result, your e-mail signature is an important – but all too often overlooked – extension of your brand. Just as you wouldn’t mail a letter or a proposal printed on any old paper stock, you should give equal consideration to creating and implementing a standardized corporate e-mail signature. However, this is where things get a little tricky. While your e-mail signature serves as your electronic business card, it doesn’t play by the same rules of branding that govern your stationery, website design or even participation in social media networks. Here are five common misconceptions that can lead you astray when crafting your signature:

1. If I’m going to represent my brand, I must include my logo.

According to conventional thinking, your company’s brand and logo are one and the same. However, as counterintuitive as it might seem, it is best not to include your logo in your e-mail signature. Why? Because it is difficult to control how images are interpreted and displayed by different e-mail clients. Most e-mail applications either store images as attachments or block them, resulting in a broken image. Therefore, if you construct your signature around a logo, and that image frequently is not displayed, it compromises the consistency and professionalism that you are trying to achieve. The best, most universally replicable alternative is to integrate your corporate colors in your signature, albeit with restraint. For example, you might choose to display your company name in one of your corporate colors, which will make it the most prominent element while also employing one of the primary elements of your visual brand.

2. Personality, personality, personality...it’s all about personality, right?

In marketing, yes. On Facebook, Twitter and LinkedIn, you’ll never get anywhere without personality. However, when it comes to e-mail, make sure your messages are friendly and personable, but keep your signature strictly professional. The one and only purpose of an e-mail signature is to let the recipient know who sent the message and provide a way for them to get in touch with you. You might think it’s fun to include your favorite quotation in every e-mail, but in doing so, you run the risk of unknowingly offending a client or prospect. And never include any non-company-related information in your corporate e-mail signature. Not a link to your personal blog, not the URL of your side-project website, not your Facebook, Twitter or Skype details. That’s only asking for trouble.

3. It’s important to make a lasting impression.

The only impression you want your e-mail signature to make is professionalism. If your clients remember your signature and not the point of your message, there’s a problem. Don’t give into the temptation to experiment with large, bold or multi-colored text. Don’t try to use the typeface from your logo; more often than not, it won’t be displayed properly by the recipient’s e-mail client. Stick with simple, plain, web-safe fonts in the same size as the body of your message, and you can’t go wrong. Returning to the example of mailing a letter or a proposal, there’s a reason you would never print your correspondence on multi-colored florescent paper. Like your letterhead, your signature should reflect the legitimacy and gravity of your business-related communication. It should never compete with your message or in any way distract from the information you need to convey.

4. I need to make sure that my clients can reach me by any and every means necessary.

There’s no question that great customer service is a key competitive edge in today’s marketplace. And it’s understandable why giving your clients your direct office line, 800 number, cell phone, fax number, IM handle, mailing address and LinkedIn profile would seem to convey that you are accessible at their convenience through any number of channels. However, a much better way to serve your clients is to provide the one method of contact through which they can almost always reach you. Most of the time, this will be a phone number (pick one: work or mobile). Then, rather than having to sift through a dozen different means of communication to identify the one they need or play guessing games about which one will connect them to you in the most expedient manner, it will be right there for them to find at a glance. As a rule, there’s no need to include your fax number or your mailing address in your e-mail signature. In the unlikely event that your client needs to send you something by fax or mail, you can either include this information in the body of your message, or they can jump over to your website, where these details should always be readily available.

5. I want to drive traffic to my blog / encourage people to follow me on Twitter / promote a limited-time offer.

These are all great marketing objectives. However, you must always keep in mind that e-mail is, first and foremost, a platform for communication between one human being and another. You wouldn’t wrap up a phone conversation with your client by asking them to be your friend on Facebook, and you wouldn’t conclude a sales meeting by making a blatant plug for your blog. Your e-mails aren’t billboards for your marketing message du jour; always keep it personal and professional. Including your website URL in your signature is a good way to indirectly promote your business, its presence on various social media networks and targeted marketing efforts without cluttering up your e-mail messages. If your customer or prospect clicks through to your site, they should be presented with all of these options – most likely before they ever leave the cover page.

Best practices for a professional e-mail signature

Follow these tried-and-true guidelines to ensure your e-mail signature is polished, professional and customer-friendly:

• Focus on providing only the most essential information about who you are and how you can be reached in an effective and unobtrusive way.
• Limit your signature to four lines (the accepted standard), with a maximum of 72 characters per line to optimize how it is displayed in different e-mail applications. Combine different types of information on one line by using pipes (|) to separate the text.
• Typically, you should include only your name, job title, company, primary method of contact and corporate web address. Don't repeat your e-mail address in your signature.
• Write out the URL for your company website rather than using hyperlinked text.
• Create different signatures for different purposes. For example, you might have one version for e-mails you send to vendors that includes your office line and another for client correspondence that provides your cell number.
• Always add a signature to replies, but include fewer details. For example, whereas your primary e-mail signature would most likely include your name, position, company name, contact information and web address, your reply signature might provide only your name, primary form of contact and web URL.
• Don’t include a legal disclaimer unless required to do so. The best practice is not to transmit confidential information in plain text in e-mails because that information could easily be extracted or forwarded.
• Use a signature delimiter to create visual separation between your signature and the body of your e-mail. The standard protocol recognized by most e-mail clients is two hyphens followed by a space and a line break (-- ).
• Don't use HTML formatting, as it can interfere with how your signature is displayed in some e-mail clients.
• Simple, plain text in the same size as the body of your e-mail is best. Employ bold or colored text very sparingly for emphasis, and use only your corporate colors.
• Don't use an image as your signature, and avoid including images in your signature.
• Be sure to test your signature in as many different e-mail clients as you can (including web-based applications like Gmail). Don't forget to also check how your signature looks when forwarded to ensure that all lines wrap correctly.

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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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