The Timelines They Are a-Changin': 7 Tips for Making the Most of the New Facebook Pages

Like it or not, it’s here: the new timeline for Facebook business Pages. And much like any of the wide-sweeping changes that Facebook has introduced in the past, the faster you can get acclimated, the sooner you can start using these new features to your advantage to engage more effectively with your customers. So without further ado, let’s dive right in, shall we?

1. Put your best foot forward.

The first thing that you – and your fans – will notice is the new cover photo. This is your very own Facebook billboard, your opportunity to grab new visitors and entice them to delve deeper into your Page. This is prime real estate, so make sure you use it well. A great photo can make a memorable first impression that helps you attract and keep potential customers; a bad one can turn them away before they ever interact with the rest of your content. Get creative with this space. Feature one truly stunning image that captures the essence of your brand. Showcase your products or services. Give fans a peek behind the scenes of your company. No matter your approach, just make sure you play by the rules: No mention of prices, sales or discounts. No contact information, such as your phone number or website address. And no calls to action, whether it’s to like your Page or buy your product. You’ll also notice that your profile photo persists in the new format. This spot is best used for your logo icon, as it will also be the thumbnail image that appears alongside your posts in your fans’ news feeds.

2. Deliver a call to action.

So Facebook says you can’t include a call to action in your cover photo. You’ll also discover that you can no longer set a default landing tab for your Page. What’s going to here? And what are you to do about it? Relax. You still have plenty of opportunity to capture attention and motivate your fans to take action. Just below your cover photo, you’ll see four featured app slots. Photos will always take the first slot on the left, but you can customize the other three to your liking. These slots are the perfect place to feature a promotional video, link to your company blog or even a store (or donation) app. Much like the cover photo, these app slots will get a lot of eyeballs, so make sure you design them well.

3. Tell your story.

Once you get past your cover photo and apps, you’re into the timeline itself. In some ways, the timeline isn’t so revolutionary. Your most recent update appears at the top and the rest follow in reverse chronological order as you move down the Page, just as they always have. On the right side of the Page, you’ll see the timeline represented in years, starting with the year that your business was established. Here’s where you can really start to have some fun with the new format. Add important events – the date your company was founded, the launch of a new product, awards you’ve won – as milestones in your timeline to create a sense of stepping back in time. The more visually engaging you can make these milestone posts, the better. There are also new ways to filter what’s shown on the Page: Highlights, Friend Activity, Posts by Page and Post by Others. The trickiest part of these new filters is that posts by your fans aren’t as visible. So if interaction with your fans is important to you (and it should be), then you’ll need to be very intentional about engaging with them.

4. Get visual.

Another difference that will quickly become apparent the more you study the new timeline format is that photos and videos are given much greater prominence than any other type of content. Jumping on the bandwagon that has made platforms like Pinterest and Instagram so popular, the timeline focuses on visual storytelling. This is actually a plus, since as we’ve covered previously, one great photo truly can do the work of a thousand words in conveying what it is that your brand stands for. Data from Facebook supports this theory as well, indicating that “posts including a photo album or picture can generate 2X more engagement than other post types.” Use this new format to your favor by posting photo- and video-based content as often as possible.

5. Stick to it.

In order to help extend the longevity of your content, you now have the ability to pin a post to the top of your Page (it will appear just below your status update compose box). While this won’t affect how your update appears in your fans’ news feeds, it will keep this story at the top of your own Page for up to seven days. Pinning a post to the top of your Page is a great way to call attention to a special event, marketing offer or other especially fantastic content. Alternatively, you can also highlight a story so that it spans all the way across both columns of your timeline, making it easier to find as a user scrolls down.

6. Keep an eye on your inbox.

Another major change being introduced in this latest iteration is that Pages now have the ability to receive direct messages from fans. While these messages can only be initiated by a fan, this new feature serves a very useful purpose. Fans can now use Facebook as a way to ask a question or discuss a customer service issue in depth without doing so in the public forum. It’s a win-win for both you and your customers. Just be sure to keep a close watch on your inbox and respond to any messages you receive in a very timely manner. After all, if someone has reached out to you, you want to make sure they feel that their effort is both appreciated and taken seriously.

7. Don’t over-think it.

Finally, even though these changes are significant, there’s no need to feel overwhelmed or intimidated. The fact of the matter is that the majority of your fans will continue to consume the content you’re publishing in the same way they always have: via their personal news feeds. So while a well-designed Page timeline is important – particularly when it comes to winning over new fans – it’s not necessarily going to reinvent the wheel of the interactions you have day in and day out with your existing fans. In an ideal world, they would reward all the time and effort you put into your timeline by congregating on your Page and interacting with it directly. In reality, that’s probably not going happen on a consistent basis. The best approach is to strike a balance between the two. Be innovative in trying out new ideas and approaches on your timeline, but keep the content of your daily posts steady and strong, since you never know just when or where someone will come across it. After all, just as with most things in life, the more things change on Facebook, the more they stay the same.

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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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