Focus, Technology and Personalization: A Master Class in Branding from Apple’s Angela Ahrendts

Angela Ahrendts was recognized as a branding and marketing powerhouse well before Apple tapped her as their senior vice president of retail and online sales. Her eight-year track record at Burberry is very nearly legendary — and with good reason. When Ahrendts came to Burberry in 2006, growth at the venerable company had nearly come to a standstill, but within just a few years, she had re-established the brand as a force to be reckoned with in the luxury market. Through a combination of savvy use of technology and some hard-nosed business moves, she rebuilt the Burberry label brick-by-brick, and by the time she left for Apple, had nearly tripled the company’s annual revenue. And while your company’s marketing budget and resources surely are a drop in the bucket compared to Apple’s or Burberry’s, that doesn’t mean we can’t take a page from her syllabus and learn to how to emulate her innovative approach to branding building. So why don’t we all turn our attention to Professor Ahrendts, and let her teach us how to embrace the principles and practices that brought her phenomenal success at Burberry and got Apple’s attention.

Focus, focus, focus

When Ahrendts became CEO of Burberry, she inherited a brand in turmoil. The venerable 150-year old name was no longer synonymous with luxury; instead it had become the label of choice for British hooligans — so much, in fact, that some pubs refused to allow patrons inside if they were wearing Burberry. Outside the UK, the situation was even worse. Burberry had forged licensing agreements with more than a dozen international companies, and those companies were creating their own inferior products, then stamping them with the Burberry label. While Burberry was foundering, the luxury market as a whole was growing. Ahrendts found herself competing against well-established brands in a competitive market where her company had lost all advantage. Her response? Focus.

The brand

She began by finding what she refers to in interviews as her “white space” — the niche in the market that only Burberry could fill. And she found Burberry had two things that made it completely unique: it was British, and it had a history that spanned 150 years back to a single overcoat. These two features have been the touchstones guiding Burberry ever since in every piece of marketing, every fashion show and every story the brand has told — British models on the runway, British music on the website and in stores, and those classic trenchcoats always on prominent display.

The market

With a clear focus for the brand established, Ahrendts moved into market research to find the white space among consumers. Research told her something interesting – something that competing brands had either completely missed or ignored. The demographic group spending the most money on luxury consumer goods, especially in emerging markets, was the Millennial generation. So she landed upon the concept of “democratic luxury” as a way to bring the Burberry brand to a younger generation, avoiding the stuffy image many luxury brands promoted and making Burberry young, exciting and friendly.

The vision

Her final area of concern was all those licenses that were diluting the brand. Burberry bought back the licenses and established tight control over every single item that carried the Burberry label, from products to marketing campaigns. The new rule was simple: anything visible to the consumer passed through the hands of Chief Creative Officer Chris Bailey, the keeper of Burberry’s brand vision.

Digital first

Ahrendts has said that she views digital technology as a force for driving change rather than a marketing tool – a philosophy that is front and center in all of Burberry’s online outreach efforts. Take, for example, the Art of the Trench and the Burberry Kisses campaigns. Neither is designed as a direct-sell campaign but rather as a way to connect with, engage with and delight consumers. Then there’s Acoustic Burberry — a showcase of up-and-coming British musicians featured online and in Burberry stores. This integration of online and physical worlds is another of Ahrendts’ trademarks, and it’s embodied in Burberry’s flagship store, opened under her direction in 2012. Her stated goal was to make walking through a store exactly like browsing the Burberry website, and that goal is more than met. The store leverages cutting-edge technology to create a truly unique experience for customers. One great example is the use of chips embedded into selected products to activate interactive screens showcasing the story behind each item. Even runway shows blur the line between online and physical reality. Burberry now live-streams their fashion shows and allows online viewers to purchase items they see on the runway — well before they’re actually available in stores. It’s this seamless integration of worlds that has made the Burberry brand unique among its peers.

And always personal

Angela Ahrendts has also pioneered the use of technology to truly personalize the Burberry brand experience. In the stores, associates carry iPads with access to an international database of customers that provide purchase histories and personal preferences in order to allow them to provide their clients with a higher level of service. And online, customers are given the opportunity to customize items with nameplates and personalized technology. Orders placed online are even confirmed by a personal call from a Burberry rep.

Your takeaway

Angela Ahrendts has established herself as a branding genius — and Apple stands to benefit immeasurably under her guidance. Take her philosophy as an example, and reap some of the same benefits in your own market and on your own scale: Keep a clear focus on your brand story. Maintain control over your brand. Find your audience and learn to speak their language, which Ahrendts would say is digital. And discover ways to differentiate your product through presentation and personal service.

---

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.

---