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by Ray Grady
The sudden shift away from desktop browser experiences to the mobile web has been both a huge challenge and opportunity for ecommerce user experience management. While having a mobile version of the store was a priority in the five years, the new user expectation is that the site will respond to their circumstances intuitively down to an awareness of whether or not they are in a moving vehicle or inside the brand’s actual store. From Apple’s iBeacon technology for in-store retail experiences to intelligent technologies that deduce if a customer is driving, riding a subway, or waiting at an airport, location awareness is a bold horizon for ecommerce content management.
Harnessing this flood of new, significant signals being sent by consumers to brands adept enough to receive and interpret them in real-time is filled with hype and very difficult to implement in practice. The new model of customer lifecycle management is built on top of a sophisticated Big Data platform, one that collates incoming data and signals from customers and interprets those signals into action. Where web logs and registration databases were the traditional sources of data for delivering a custom user experience, new sources are arriving that range from social media detection and analysis to the data thrown off by wearable devices and the “Internet of Things.”
Assuming the organization has a plan around advanced data collection and analytics, the question then is how to harness the insights promised by such a data-driven model into agile triggers that can delivered relevant content and offers on the fly to customers operating with a new sense of entitlement and expectations driven by digital experiences presented to them elsewhere.
From the first registration attempts at personalization in the late 1990s, through the craze for “behavioral targeting” in the Web 2.0 era a decade ago, the confluence of commerce and content into a dynamic, highly personalized user experience is finally coming into focus and fruition as the technology components required to enable the experience come into affordable range. Where ecommerce 1.0 was about transaction processing, credit card security, and catalogue management (usually via the uploading of spreadsheet-based data into a rigid and proprietary structure), the new models are moving towards a lighter, more open, more standardized structure that is taking online sales out of the highest levels of online retailing – e.g. Dell, Walmart, Amazon, iTunes – and down to even the smallest business thanks to the proliferation of merchant platforms ranging from eBay to Amazon to “open” platforms such as Magento and Shopify.
Enabling online sales ten years ago was usually a multi-million dollar project involving massive software licenses, systems integrators, and inflexible structures. The new commerce model is far more nimble, open and drag-and-drop focused, with a second and even third wave of commerce models emerging which look and behave entirely different than the first pioneering stores. Barriers are dropping which kept medium and small enterprises from doing business online in the past.
The biggest change by far is being driven by the realization that ecommerce is a media experience unto itself, one that is far more complicated than the old model of “search - add to cart – register – and checkout.” This is giving rise to content experiences that are about the niche first and the transaction second.
Let’s examine some obvious and time tested examples of enhancing catalogues with content tailored to a specific user by first looking at the leading innovator in ecommerce experiences, Amazon:
Recommendation engines: Amazon was one of the first online services to harness the power of recommendations and by displaying on every SKU a list of other products that other users viewed or purchased. Today recommendation engines are de rigeur on any online store.
Reviews: Amazon was also first to the game of enhancing SKUs with customer generated reviews of the product. Offering a combined “star” rating system with a simple review submission platform that over time developed into not only an entertaining medium of its own, but perhaps the most significant driver of customer preference and selection as users discounted marketing copy in favor of actual user testimonials.
Content Targeting: Some ecommerce operators have presented users with different content defined “paths” through their sites. A top level menu offering of the past might have asked a consumer to declare if they were a student, a government employee or a small business owner. Today content can be served more subtly and automatically based on the traditional triggers described earlier. The challenge is not so much the intelligence that triggers the delivery of the content as the creation and management of the content assets themselves. Where past catalogues were relatively flat, matrix-driven models that followed a strict page structure of storefront-category-sku, newer models are less linear and more adaptative to the user’s triggers. Such a dynamic content model drives the need for more variants on copy and other media assets as well as a smooth system of templates and relevance served on the fly. Amazon targets content subtly but effectively by remembering a user’s preferences and delivering a relevant offer on the first page view.
Retargeting: Retargeting consumers after they’ve paid a visit is a classic optimization technique also known as “follow-along” marketing. To understand the model, think of a spy movie where someone sticks a tracking device under the bumper of a car to follow its movements. In ecommerce that device is a cookie that the site deposits on the user’s browser which in turn triggers the serving of a “reminder” ad as they cruise around elsewhere on the internet. The technique has been demonstrated to have some impact, but if misused can alienate customers who become aware of being followed blindly with no consideration of why they visited the ecommerce site in the first place. e.g. pushing display ads with aggressive discounts for a product the customer has already purchased is a waste of the impression as well as an indication of “deafness” on the part of the brand. However retargeting a customer with an accessory or upgrade to an original purchase can be very effective in improving accessory attach rates. Amazon remembers browsing history and retargets within its own pages, using its own pageview inventory to reinforce past browsing sessions.
Segmentation: User segmentation is difficult to do correctly and disastrous when done poorly. Customers may resent the feeling that they have been typecast or pigeon-holed into a segment that isn’t germane to their actual needs. Tailoring a catalogue to a specific segment may lead the customer to wonder what they aren’t being shown, or whether they are seeing a different set of prices than another segment is.
Push marketing: Amazon is subtle in asking customers to review recent purchases via email as well as occasionally pushing an offer to them. Email continues to be an effective direct marketing medium, especially when it managed wisely, isn’t overused and presents relevant value to the recipient. Some brands generate thousands of variations of emails to inform customers of upcoming sales that match their profile preferences. Buy a baseball book on Amazon, and eventually you’ll receive a baseball book notice in email.
Sharing: Amazon’s Wish Lists and sharing tools permit users to share SKUs with their social graph are important innovations that drive sharing and increase the utility of the site when customers seek the input of their friends on purchase or wish to make a recommendation directly to a prospective buyer.
Account management: Amazon’s account management model is extremely rich and offers a customer a record of all past purchases back to the very earliest days of the company’s existence.