Today’s standard retail search and discovery experiences are falling further and further behind customer expectations, primarily being driven by non-retail technology advancements. Take, for instance, the next-generation semantic search capabilities being launched by Google, Apple and Walmart, the highly engaging visual shopping and personal content curation provided by Pinterest, as well as voice “search” capabilities powered by technologies like Siri. These are the new standards for engaging with consumers and retailers are faced with some steep challenges in evolving to meet them.
Limited Shared Vocabulary Between Shoppers And Merchandisers
Shopping is a decision-making process at its core. And dealing with the science of the human mind is complex. Doing so is not just a function of building technology, but about understanding who we are as people and how our mind functions. Not as a technologist, but as an interpreter of human behavior.
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Because consumers have been trained to think in short keywords, they often times guess at a translation between their preferences and needs and how they think the manufacturer and merchandiser may have categorized a product. At the heart of this limitation is a lack of “humanized” merchandising data or terminology.
For example, a shopper might describe a shirt as “salmon, modern” and the retailer might call it “pink and slim fit.” The shopper might also want the shirt to be appropriate for an outdoor wedding and a day full of meetings. Very few retailers have the manpower to look too far beyond manufacturer-provided information to determine the myriad of ways that their shoppers truly think about a product. The result is a sub-par set of product merchandising meta-data that can’t be used by a user to efficiently and confidently discover the products best for them.
Additionally, And Possibly Most Importantly, Is A Lack Of Ongoing Optimization Of Merchandising
Most merchandising involves a “set it once and forget it” process. Retailers are strained to understand what information has the most impact on buyer decisions, as well as what types of information and criteria are completely missing from the shopping discovery experience. Early detection of changing trends, styles and shopper vocabularies must be facilitated to help the merchandising team re-calibrate and prioritize optimizations.
Lack Of Tools That Support Natural Language Discovery And Navigation
Current search and discovery tools have been built to only interpret structured keywords rather than shopper “thoughts” and concepts. Even if more rich merchandising data was available, current tools would not be able to capture shopper needs and preferences in a natural, semantic way and translate them into highly relevant results. As with data gathering, the manpower required to update taxonomies and filter options fueling product discovery is heavily dependent on development support and roadmap prioritization.
Personalized Merchandising Based On Implicit Hypotheses
Great strides have been made over the past 10 years to create algorithms intended to help anticipate and point people to products and content they are likely to find interesting and useful. As important as this implicit data can be for retailers and brands, the gold standard is explicit data, such as preferences and needs directly expressed from shoppers, as it can play a more significant role in learning from each shopper’s likes and dislikes and buying path to improve the overall shopping experience. As TechCrunch recently reported, “While retailers are doing more with implicit data (i.e. reminding you when you left items in your shopping cart, etc.) no one has yet mastered the art of capturing that precious explicit data.”
The key to the next wave of online personalized discovery is not only in adapting the experience to the user, but also letting the user adapt the experience to his or her preferences. It is important for retailers to move beyond the idea of static experiences or static content, and instead embrace a philosophy that gives shoppers the tools they need to make decisions faster and more confidently.
Adaptive Commerce: A Game Changer For Humanized Discovery
In the quest for better product discovery and decision-making — and a vastly improved online shopping experience — it’s important to remember a simple principle: technology and algorithms are not a replacement for thought. As human beings, we have the world’s most powerful processor literally at the top of our head. This piece of equipment has been honed over thousands of millennia to bring us to where we are today, and yet we as a technologically driven society fight against its use. We assume that an algorithm knows best, or that a machine knows better what I want than myself. We build layers of protection against users “making mistakes” or “misusing software,” assuming that a machine will do it better.
The future of simple and fulfilling discovery experiences lies not in richer, better-crafted algorithms but rather a reinvention of the human computer interaction altogether — one that radically simplifies the decision journey. Buying online is no longer just about exciting shoppers, but about being a part of a holistic experience and simplifying their decision. How well you compete is a reflection of how well you understand the mind of the shopper. Creating the opportunity for interplay between explicit user inputs and thoughtful machine learning to create an evolving discovery experience should be the goal. This concept is called Adaptive Commerce.
Adaptive Commerce is the culmination of all the elements that shoppers want in an experience: Relevance, control and simplicity. Retailers will face challenges in evolving their approach to content creation and on-site interactivity to be more human and shopper-driven, but the rewards will be high for those who innovate first and best.
Garrett Eastham, CEO of Compare Metrics, is a marketing technologist with a knack for helping leading brands create, capture, and deliver value from their digital consumer experiences. He played a key role in developing the big data and analytics initiatives at Bazaarvoice as the Data & Analytics Product Manager. As the founder and CEO at Compare Metrics, he drives overall business and product strategy while also leading the day-to-day execution of the company’s vision.
