13 Sure-Fire Ways to Bring Customers In Your Door Today

For clothing boutiques, restaurants, coffee shops, florists, salons, bakeries and every other kind of business that thrives on foot traffic, one of the most invaluable benefits of social media is the immediate access it offers to customers.

With a little creativity and flexibility, you can use Facebook and Twitter to bring more customers through your doors every day. Elicit a direct response without taking the hit for printing and postage to send a mass mailing, and broadcast frequent, time-sensitive updates without spamming your customers’ e-mail inboxes.

Just remember, in a real-time world, timing is everything.

What’s for lunch?

It’s 11:00 a.m., and your customers are feeling the mid-morning lull. As breakfast fades into a distant memory, hunger begins to creep in.

It’s the perfect time to post an update with your daily specials. After an hour of dreaming about your rich, creamy pumpkin soup, it will be hard to think of another option.

I saw it on Twitter

Offer limited-time exclusive sales and discounts for fans and followers who mention your tweet or repeat a special promotional code at the check-out counter.

The new fish bowl

Everyone knows the fish bowl on the bar where you drop your business card for a chance to win a free lunch.

Create your own virtual fish bowl by picking up the tab for a randomly selected follower who retweets your daily specials or likes your latest update on Facebook.

What’s hot now?

Driving by a Krispy Kreme store, it’s all but impossible to resist the sirens’ song of the “Hot Now” sign.

The same concept applies in the virtual world. Got a batch of warm peach pies fresh out of the oven, ready and waiting for a scoop of vanilla ice cream? Post a tantalizing tweet and reap the benefits of the power of suggestion.

Be their social director

When mid-afternoon Friday rolls around, water cooler talk turns to weekend plans. Capture the after-work crowd by posting your happy hour special or open mic night.

Never find yourself under the weather

Don’t let the rain dampen your sales. When skies are grey and temperatures are falling, your customers might not be inclined to venture out into the elements. But what chilly cube-dweller could resist the allure of a perfectly brewed espresso?

No such thing as a slow night

Is business unusually slow on Saturday evening? Turn your night around by tweeting “Hungry? No wait tonight @CornerCafe.”

Got a few gaps in your appointment book? Fill those empty slots by offering a one-day 2-for-1 special.

What’s new and what’s now?

Post a photo and tell us why your hot new arrival is this season’s must-have.

Even customers who stopped in just last week will be tempted to come back and make sure they’re the first to be seen sporting the latest trend.

Answer a question before it’s asked

Your customers may not be thinking about their holiday plans just yet, but you are definitely thinking about your holiday bookings.

Create a sense of urgency by sending out a tweet like “Jingle bells will soon be ringing! Book your party now while reservations are still available!” to spur them to action today.

While you're in the neighborhood

Is there a special event happening that will bring your customers to your area? Jump on the bandwagon by offering a promotional tie-in or themed refreshments that will entice them to stop by.

The race is on

Want a quick traffic boost? Offer a special freebie to the first few fans through the door who repeat a secret phrase.

Where everybody knows your name

Everyone likes to feel they’re part of the club. If you regularly post updates with photos of guests or shout-outs to loyal customers, others will be drawn in by the desire to get in on the action.

Don’t forget your manners

As nice as it is to hear the cash register ring, don’t use your social media megaphone exclusively for self-promotion.

To earn the lasting loyalty of your fans and followers, be sure to provide helpful, useful news, information and links, too, and keep them coming back for more.

 

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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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