Home > Latest News > ‘Afterpay IQ’ Brings Behavioural Science To The BNPL Sector

‘Afterpay IQ’ Brings Behavioural Science To The BNPL Sector

Australia’s brightest young tech-hopeful Afterpay has analysed the last year of consumer purchasing behaviour — totalling a whopping 156 million transactions — to create Afterpay IQ, an exhaustive study of shoppers, which it is offering to its retail partners in a bid to better understand its customers.

According to Afterpay IQ, humans can be divided into ‘four customer tribes’ and further sliced into various ‘personas’

The company revealed this at last week’s AGM, with chair Elana Rubin describing Afterpay IQ as a “sophisticated unsupervised machine-learning algorithm”.

“We’ve leveraged merchant and product level data in order to create personas and it gives insight into customer motivations,” she said.

This may sound a little nefarious, given the recent outcry about data privacy, but Rubin insists this was front of mind for Afterpay.

“This portal is designed to help merchants understand their performance with Afterpay,” she said.

“But consistent with all the privacy segments, as such the only data merchant can see in the portal is their own, which has been de-identified and is only shown on an aggregated persona basis.”

Chief strategy officer Mark Teperson stressed this was developed “off the back of conversations we had with merchants about how we can drive more value for them.”

“Many of our merchants were asking us to help them dive deeper into how they could leverage the strength of the Afterpay brand and network.

“We created Afterpay IQ in-house with our behavioural science team … then working with our engineering team to bring it to life.

“We’re looking to deliver much more insightful information than just customer segmentation. The IQ platform can provide a deeper understanding of the customer, ideas for new channels to target that customer and even new product ranges or adjacencies.

“Our personas paint a clear data-driven profile of who that customer is, their spend, frequency, AOV (average order value) as well as helping merchants understand which other verticals their core customer is purchasing.

“Specifically, merchants can look at their top in-store personas and use this to help inform their marketing strategy to drive online customers in-store.”

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