Print on Demand continues to grow
Over the last few years, shoppers have grown to expect some degree of personalisation on the retail sites they visit. Brick technology have been developing Print-on-Demand and personalised solutions since 1995.
Personalised recommendations that display products similar or related to the ones online visitors are viewing were once considered a “nice to have” by many retailers - and now they are seen as a necessary means to simplify the shopping journey. In sectors such as Beauty and Fashion, sixty percent of shoppers say they are more likely to buy from sites that provide relevant product recommendations.
Today, we are seeing an increasing number of retailers take personalisation further. Many brands now display tailored content across multiple pages of their websites to appeal to each unique visitor’s interests and brand affinities in order to drive more engagement and create a more relevant experience.
So, what’s next? What more can personalisation technology do to help retailers drive sales and retain customers? Ongoing research and development are fuelling innovation in this area - and the shift to ecommerce resulting from the global pandemic has played a key part in these developments, so there is more to come.
Here are five emerging trends and developments in ecommerce personalisation that are likely to take off in 2021 and beyond.
Expect greater adoption of image recognition capabilities
Ecommerce personalisation technology will embrace image recognition capabilities, meaning it can recognise specific colours and visual patterns within images of products (such as clothes) in order to create richer personalised experiences.
In fashion and home furnishings, this technology will enable even more granular “shop the look” recommendations, also making it even easier for shoppers to create their own look by searching for similar or matching products.
For stores, image recognition capabilities will foster more sophisticated online experiences by presenting products as shoppable content within images and videos. Shoppers could pick out a sofa they like out of a photo showing a living room or a purse carried by a model in a video of a street scene. This will have a greater impact on product discovery and drive shoppers to add items to their shopping cart at higher rates.
Get set for more transparency on how AI-powered personalisation works
Consumers can sometimes be a little intimidated by how AI and data is being used to create personalised experiences. Recognising this, ecommerce brands and technology providers will increasingly find ways to make AI decisions more transparent and less puzzling to shoppers, building a stronger sense of trust between retailer and consumer.
For example, this will mean that when recommendation engines identify and display relevant product recommendations, those recommendations will automatically be accompanied by simple explanations that make it crystal clear to the shopper why and what data was used to select those products.
If a shopper is shown a blouse that’s “recommended for you,” they might be presented with a list of explanations such as, “it’s in your size,” “it’s a similar style to what you’ve previously purchased,” “shoppers who also purchased (x product) also went on to buy this”, etc.
Psychographics will create more possibilities for personalisation
Most ecommerce personalisation and segmentation has, up to now, relied on demographics (such as age, gender, and location) as well as shoppers’ purchasing histories and online behaviour to create relevant shopping experiences.
The next step could well be the growth of new types of personalisation that rely on psychographic profiling.
For example, there’s a good case for using algorithms to improve personalisation by analysing visitors’ personality types: are they trusting, assertive, or adventurous? Psychographic personalisation could learn to identify and showcase the types of products, content, and online experiences that would most appeal to shoppers that fit each of these profiles.
Personalisation will begin to take target longer shopping cycles
Retailers predominantly use personalisation today to enhance the shopping experience in the current visit, or to personalise content or offers they’re shown if they return within the next month or so.
With personalisation technologies only becoming more sophisticated, we’ll start to see a new focus on longer shopping horizons. How do people’s behaviour in the present point to their purchasing preferences in the long-term future? Can we predict someone’s preferences and brand affinities in their forties by analysing what their purchasing habits will be like in their twenties?
As you would expect, this kind of high-level personalisation will mostly focus on big ticket items, such as cars or luxury goods. How does the brand of toy car purchased for a child, translate to the brand of car they might like to buy when they reach adulthood?
Algorithms will automate opportunity mining:
There could be hundreds (or even thousands) of customer segments that a retailer can target with online personalisation, and a huge amount of time and resources are ploughed into “mining” and researching the most lucrative and profitable ones.
Machine-learning algorithms will help to automate this process - creating a faster, more effective way to identify and nurture profitable shopper segments. In addition to telling retailers which segments to target, this technology will be able to advise on specific actions to optimise and personalize the experiences for them.
Over time we’re seeing many aspects of online personalisation become a standard requirement on ecommerce sites. As retailers compete to differentiate themselves (especially in the current ‘online first’ climate), this is driving further initiatives to enable richer, more sophisticated personalisation capabilities and offer the most relevant and enticing shopping experience.
Thanks to the author:
James White, general manager, UK at NOSTO