Going the Distance: Four Ways to Build a Better Customer Loyalty Program for Your Brand

It’s easy enough for a customer to join your loyalty program, especially when you’re offering an incentive such as discounts. All your customer has to do is give out some basic information, and voila! They’re in the fold, a brand new loyalty member with your company. From there, it’s happily ever after. You offer the perks; they stand solidly by you, bringing you their continued business. Simple. Or is it? In reality, just how many of those customers are act ively participating in your loyalty program? Do you know? Sure, loyalty program memberships are on the rise according to market research company eMarketer, having jumped 25 percent in the space of just two years. However, that figure may be a bit misleading. The truth is that, while loyalty program sign-ups may be more numerous, active participation in such programs is actually in decline. At the time of the study, the average US household had memberships in 29 loyalty programs; yet consumers were only active in 12 of those. That’s just 41 percent. And even that meager figure represents a drop of 2 percentage points per year over each of the preceding four years, according to a study by loyalty-marketing research company COLLOQUY.

When discounts just aren’t enough

So what’s a brand to do? How can you make your loyalty program worth your customer’s while—as well as your own? After all, gaining a new loyalty member doesn’t mean much if your customer isn’t actively participating in your program. Consider this: Does your customer loyalty program offer members anything different from what your competitors are offering? Chances are your program includes discounts. That’s a given. And what customer doesn’t appreciate a good discount? But when every other company out there is providing this staple benefit in comparable amounts, it becomes less and less likely that customers will remain loyal to any one particular brand. Frankly, it’s all too easy for customers to get lost in a sea of loyalty member discounts. They’re everywhere. In fact, just under half of internet users perceive that all rewards programs are alike, according to a 2015 eMarketer survey. The key to success, then, is to differentiate your business from the crowd. If you can offer your customers something unique and valuable beyond the usual discount, chances are they’ll be more likely to stick with your brand. Here’s some inspiration from companies who get it.

Virgin: Reward more purchases with more benefits.

That’s not to say you need to get rid of discounts entirely. In fact, nothing could be further from the truth. Customers still love a good discount. The goal is to be creative in terms of the loyalty perks you offer. Take the Virgin Atlantic Flying Club, for example. As part of its loyalty program, the airline allows members to earn miles and tier points. Members are inducted at the Club Red tier, from which they can move up to Club Silver and then Club Gold. Here, it’s not just a discount. It’s status. And people respond to feeling important, elite. Still, even where the rewards themselves are concerned, Virgin is motivating loyalty customers with some pretty attractive offers. At the Club Red tier, members earn flight miles and receive discounts on rental cars, airport parking, hotels and holiday flights. But as members rise in tiers, they get even more. At the Club Silver tier, members earn 50 percent more points on flights, access to expedited check-in, and priority standby seating. And once they reach the top, Club Gold members receive double miles, priority boarding and access to exclusive clubhouses where they can get a drink or a massage before their flight. Now that’s some serious incentive to keep coming back for more. Discounts are still part of the equation – but they are designed with innovation and personal value in mind, elevating them to more than just savings.

Amazon Prime: Pay upfront and become a VIP.

What if your customers only had to pay a one-time upfront fee to get a year’s worth of substantial benefits? It may not sound like the smartest business idea at first glance. But take a closer look. Amazon Prime users pay a nominal $99 a year to gain free, two-day shipping on millions of products with no minimum purchase. And that’s just one benefit of going Prime. It’s true that Amazon loses $1-2 billion a year on Prime. This comes as no surprise given the incredible value the program offers. But get this: Amazon makes up for its losses in markedly higher transaction frequency. Specifically, Prime members spend an average of $1,500 a year on Amazon.com, compared with $625 spent by non-Prime users, a ccording to a 2015 report from Consumer Intelligence Research Partners.

Patagonia: Cater to customer values.

Sometimes, the draw for consumers isn’t saving money or getting a great deal. The eco-friendly outdoor clothing company Patagonia figured this out back in 2011, when it partnered with eBay to launch its Common Threads Initiative: a program that allows customers to resell their used Patagonia clothing via the company’s website. Why is this program important to customers? And how does it benefit Patagonia? The company’s brand embraces environmental and social responsibility, so it was only fitting that they create a platform for essentially recycling old clothing rather than merely throwing it away. The Common Threads Initiative helps Patagonia build a memorable brand and fierce loyalty by offering its customers a cause that aligns with deep personal values. OK, so their customers get to make a little money, too. Everybody wins.

American Airlines: Gamify your loyalty program.

If you’re going to offer your customers a loyalty program, why not make it f un? After all, engagement is key to building a strong relationship with your customer. And what better way to achieve that goal than making a game of it. American Airlines had this very thing in mind when it created its AAdvantage Passport Challenge following its merger with USAirways. The goal: find a new way to engage customers as big changes were underway. Using a custom Facebook application, American Airlines created a virtual passport to increase brand awareness while offering members a chance to earn bonus points. Customers earned these rewards through a variety of game-like activities, from answering trivia questions to tracking travel through a personalized dashboard. In the end, participants earned more than 70 percent more stamps than expected – and the airline saw a ROI of more than 500 percent. The takeaway: people like games.

Stand out from the crowd.

Your approach to your customer loyalty program should align with your overall marketing approach. Effective branding is about standing out, not blending it. Being memorable is key. To this end, keep in mind that loyalty programs are no longer a novelty. That means that yesterday’s strategies won’t work moving forward, so look for ways to rise above the noise, setting yourself apart from the cloying drone of countless other cookie-cutter programs.

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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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