Amazon’s latest earnings announcement shouldn’t have come as a surprise. But it was. A week or so ago, when it announced its fiscal fourth quarter, the results were so good, it caught Wall Street off guard. This was because everything is up more than expected – Prime Membership, AWS revenue, and product sales, largely linked to a strong holiday period. If there was ever any doubt, Amazon has once again cemented its status as the most influential player in the e-commerce space. What can other retailers and brands learn from the superbrand – and how can you fight for your place in the industry?

The Changing Nature of Search

The digital age has revolutionized how we shop, but the ways we search and find products online dates back to the 1930s with a handful of optimizations made over the last 60 years. The two major components to our “modern” search experience are faceted or filtered search and static keywords. Each with their own impact on how consumers navigate digital search experiences.

The latest data suggests that 79% of all product-related searches start with Amazon. But, with the abundance of choice online becoming more overwhelming and confusing – consumers are demanding new ways to search. We expect to be guided to products rather than working independently. After all – how are consumers supposed to make sense of thousands of products, specs, features, and keywords, especially when most products within a category look the same?

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Time to Transform the Search Experience

For Amazon (or any other retailer or brand) to continue to grow, fixing the search problem will become one of the most urgent business issues for its e-commerce leaders to address – putting an end to the outdated and frequently ineffective expectation that consumers will navigate and find products themselves. Keyword-based search is no longer helping shoppers find the perfect products, it is actually ineffective, out-dated and in need of an overhaul.

This is because keyword-based search offers a shopper tens of thousands of product options but only a fraction of results will truly fit their needs. Retailers and brands alike need to find a way to help consumers make sense of the specs, product features and model numbers…especially when all companies look the same. Using AI, Machine Learning and NLP (Natural Language Processing) is the only way to translate SKU data into a language that is needs-based, easily understood and actually helps consumers find the products they’re looking for.

Voice Search Starting to Be Heard

For this, look no further than the rise of Voice assistants like the popularity of Alexa (another successful Amazon product). The rise of Voice assistants is connected to the way people’s lives are changing and research shows that the rise of Voice search will overtake traditional keyword searches in a short number of years. Adobe’s State of Voice Assistants report that found 37 percent of men and 27 percent of women own a smart speaker already.

More and more consumers will begin turning to Voice assistants as a means to shop and search for products which brings with it a huge challenge – how do brands and retailers simultaneously have millions of conversations that help shoppers find the perfect products and increase purchase confidence – all without even looking at the product in question?

By its very nature, Voice conversations are unstructured and fluid, just like a human conversation. This new dialogue requires companies to translate vast amounts of product catalog data and features into truly humanized language which requires Natural Language Processing, Sentiment Analysis, and Artificial Intelligence. Consumers demand convenience in every part of their life, adopting technology that fundamentally makes their lives simpler – and that includes searching for products and services.

Success Will Mean Becoming Data-Driven – Like Never Before

Most retailers and brands alike use the Sales performance of individual products to inform stock rotation, product development and retargeting – but sales performance doesn’t actually say very much. If a particular model of camera sells more – do retailers know why? Surely it is better for retailers to know the answer to this, than not?

Uncovering the data blindspots that power product preferences and sales performance will quickly separate the front runners from the rest of the market. Questions like –  who is buying, why, and what matters to a particular cohort of consumers, not only informs distribution and stock optimizations but can also be injected into retargeting.

The best retailers will learn to predict consumers’ wants and needs, creating the shortest and most frictionless path to purchase and unparalleled consumer confidence – creating the ultimate competitive advantage to take on the e-commerce giants.

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