How much are we willing to sacrifice for convenience?
Have you ever jumped online to buy a simple black office chair, only to be bombarded with a host of related furniture products before checkout? Before you know it your cart’s overflowing with framed art prints, decorative bronze side tables and synthetic parlour palms… And you’ve dropped nearly $1500.
In an online retail world increasingly ruled by learning algorithms, shopping is becoming more and more about the power of suggestion. Personal data is a powerful commodity for modern retailers, allowing their algorithms to target us with ever more customised offers. The upshot? How we’re consuming online is progressively being controlled by the way things are suggested to us.
Unfortunately, retailers’ learning algorithms often don’t understand us well enough. Humans are complex, nuanced beings, and the products we want and need stem from complex, nuanced desires. Yet the recommendations we receive online are largely two-dimensional.
Say we buy a country album as a gift for our Mum. We’re not fans of the genre ourselves; those bronzed crooners irritate us. But suddenly our screens are being assaulted with similar country albums – products we find frustratingly irrelevant.
While this scenario is all too common, we continue to pledge allegiance to the booming ecommerce market. Why? It all comes down to convenience.
Since Amazon revolutionised online shopping with its patented one-click ordering system in 1999, purchase and delivery has become rapidly faster and cheaper. This celebrated convenience, however, requires that we trade precious personal information for the instant reward of a quick online purchase. We accept pop-ups and user agreements; we populate forms with detailed behavioural data. And we read and write user reviews.
User reviews in particular offer ecommerce retailers a significant strategic advantage. While market leaders like Amazon and eBay possess vast stores of these, even smaller online retailers incorporate the learning gained from aggregated reviews to offer their customers tailored purchase suggestions.
Given their influence, the credibility of these user reviews is an important issue. Amazon continues to battle accusations of untrustworthy customer feedback, with a scathing article published in The Guardian earlier this month criticising the online behemoth’s policy of bundling together reviews for different products. With everything from butchered translations of Jane Austen’s Emma to critically panned remakes of classic films receiving over a thousand 4.5 star reviews each, it’s clear the world’s largest online retailer isn’t too worried about the misleading consequences of blithely consolidating reviews based on product name alone.
Which all gives rise to some uncomfortable questions. With the rapid growth of predictive artificial intelligence in online retail, have we given over too much responsibility to these learning algorithms and their profit-driven owners? How can we wrench back some autonomy so the experience is more like physical shopping?
Precision marketing – the ability of marketers to hyper-target ads to prospective customers with greater accuracy – is an exciting new concept in the world of curated commerce. Given that American consumers receive a massive 3000 marketing messages daily, it makes sense that shoppers gloss over anonymous communication in favour of targeted personal messages that resonate on a deeper level. Crucially, precision marketing also allows retailers to gauge the ROI of individual ads, permitting them to target customers with increasingly specific and personalised offers.
One thing’s for sure: If we put too much trust in the system, it could lead to us snuggled up on holiday with a lousy version of Jane Austen’s Persuasion because it popped into our browser and ‘rated’ well. An inconvenience, surely.