Collision is a tech conference usually based in Toronto, Canada which recently held Collision from Home 2020 which included livestreaming talks from tech CEOs, international policymakers and global cultural figures; startup showcases and interactive workshops. I will be sharing some of the key learnings with you.
Although the world has been facing adversity due to Covid-19 in the last few months, it has proven to be quite an interesting time for these online retailers. For Ipsy, when people are home bound it means they are now enjoying more time for themselves and have more time for self-care routines so categories such as skincare are rising in popularity. This also means people have more time to try new products and submit feedback than ever before and this helps feed data into their machine learning to create personalisation and customisation packages for their customers. For Top Hatter, they see a higher demand in general because people are online a lot more and looking for affordable things to do. And since their app functions in real-time, every time people open the app they find different things to bid on and purchase.
Being data-oriented companies, their approach is by first collecting customer data in a structured way and synthesizing them which then helps to evolve the products. Using feedback loops, Top Hatter will share the information they receive to the sellers on their platform so they can make good decisions about what the buyer wants and connect them with the best buyers. Data helps keep the cost of experimentation low so when sellers find something that buyers really love, are competing and bidding on and getting fantastic feedback they want them to invest in that inventory and scale it up in an affordable way.
For Ipsy, the signals received from customers regarding a new trend or a particular ingredient helps them to create in-house or co-creation products with preferences that customers want but cannot find in the market. Their co-creation program supports working together with certain brands to identify white space and co-develop with them specific lines of products to meet new market demands that nobody is looking at because they do not have the ability to collect the signals. Partnering with brands help Ipsy create a unique assortment to help their customer discover more products.
Although both companies leverage on massive data sets, Top Hatter found that when they measure the decisions made from their sophisticated machine-learning algorithms against sellers’ decisions they found that sellers are making similarly good decisions without the complexity of machine learning. However, there are other aspects and a lot of draw of going into machine-learning approach that they continue to consider it. Ultimately it is the combination of personalisation and customisation that is backed by data that makes the experience special for the customers of these retailers.
