Ajouter un commentaire
by Ray Grady
Welcome the anonymous flood of traffic that arrives at an online store. Then dynamically customize each individual user’s experience, depending on how they found their way to the site, how they identify themselves, and what their past history is.
This has long been the holy grail for ecommerce executives.
Personalization was all the rage towards the end of the Web 1.0 phase of ecommerce and became a buzzword of sorts in the late 1990s at the height of the first dot.com boom. Today it is an absolutely essential expectation by customers that if they establish a one-to-one relationship with a brand that they will be remembered in the future and given an experience relevant to their needs, not a generic shopping experience that is ignorant of their individual history and preferences. Static one-site-for-all experiences are being challenged by online stores that not only remember the past, but prompt customers to make future purchases.
Serving up a personalized user experience would seem eminently possible, but in practice it has proven difficult for most businesses because of the cumbersome constraints of their ecommerce infrastructure, back-end IT systems and customer relationship management tools. Rather than invest in expensive integration of third-party customer relationship management tools and content management systems into their existing transaction/catalogue engine (e.g. ATG, IBM Websphere, Hybris, GCI, Magento), ecommerce managers are building tailored user experiences outside of those systems on nimble, point solutions. Enter the era of the “bolt-on” store, a new layer of highly dynamic quality content that sits atop a robust transaction processing engine.
Traditional Personalization Triggers
Designing and delivering an online commerce experience that isn’t flat and monolithic but which dynamically responds to its users begins with how receptive the brand is to customer signals, signals which may be interpreted from a number of traditional digital sources, including the obvious ones which have been standard operating procedure for the past ten years for most successful online stores. More advanced signals -- such as those that enable an astute marketer to know when an anonymous browser is pregnant or in the market for a new car -- will be treated later in this series. For now, the classic identifiers for ecommerce have been:
- Cookies - If the user has previously shopped on the site or opened an account, and if their browser will accept a site cookie, then the site will automatically remember the user on subsequent visits. Cookies are an accepted fact of life for most web browsers, yet are imperfect and constantly come under scrutiny as invasions of privacy. User registration is generally a given function of any ecommerce operation which needs to remember multiple personal payment options, ship-to-addresses, and order histories to keep up with customer expectations. Remembering a customer and welcoming them on subsequent visits establishes the best opportunity for the site to deliver a customized, relevant experience keyed to the customer’s history of browsing, reviews, and past purchases.
- Referrals – The proper tagging of digital advertising and sources of traffic is essential to accurately give credit to the specific creative and media that succeeds in sending traffic to the destination store. The cadence of tagging digital advertising and driving traffic from that creative to a specific microsite was an early and effective tactic for many ecommerce product managers who wanted to optimize their in-market spend by giving credit to the right creative message and media buy. From single-use coupons to affiliate programs, ecommerce demand generation depends on accurate attribution to drive the optimization of outbound marketing and media buys.
- Detection, Dayparting and Geo-location – This is where the site experience adjusts according to the time of day, the country where the visit originated, and the IP address of the user – think of Amazon.com delivering a German language experience to an American tourist traveling in Germany. Changing the site experience according to the time of day can permit a single commerce site to anticipate a global market without translation and adopting a multi-site country/language model. Domain triggers such as detecting an “edu” or “gov” user and giving segment specific promotions such as special campus pricing for qualified students or government employees is another classic tactic. Detection also uses standard web logging information such as screen size and client operating system to determine if a user is on a desktop PC, a tablet or a smart phone and automatically deliver an experience optimized for that device. Whether the site adapts to that device is a function of how flexible the site is and whether the ecommerce team follows a “mobile-first” cadence in site development.
- Self-declaration – The site can try to get the user to guide themselves down the right path by giving them the option to declare their identity and area of interest by selecting a segment-menu option. A classic example would be a PC manufacturer’s online store that would offer a separate catalogue of products, prices and services for students/educators, small businesses, or government accounts. The issue with overt segmentation is it builds a subtle suspicion in users that by selecting such options they will risk losing exposure to all available options. While the complexity of too many choices can stymie a customer seeking a quick and relevant purchase, some online stores have tried to ease their decision making by offering them wizard-based shopping tools that help winnow down complex catalogues through a question-driven model that asks about price, intended use, and other variables to narrow down the catalogue to a few relevant options.
It has been an article of faith among ecommerce operators that enabling a catalogue with a “share this” function will improve audience development by using buyers to promote their own purchases within their own social graphs. The concept of “social commerce” was all the rage several years ago, but has best manifested itself in the default addition of user ratings and reviews to the catalogue. Coupons are being distributed through Facebook and Twitter with good effect, but for most customers the socialization of their shopping experience is focused on the sharing of SKUs with more expert friends who can advise them on a purchase, an example being the configuration of a new car with options, or a new laptop, and then sharing the build with a friend who can offer an expert opinion. Other examples of catalogue sharing is the effect of Pinterest on fashion sites, where customers can compile and “pin” a selection of outfits and accessories for friends to rate and comment on.