Marketers should rely on short-term data over data aggregated over long periods to adapt to short-term consumer shifts.
With online conversions at only 3% pre-pandemic, converting casual browsers to buyers has been a challenging task for online retailers. That complexity has been further compounded by changing consumer behaviour patterns, rising operating costs and decrease in margins.
Applying in-session machine learning (ML) can help overcome such challenges by quickly recognising short-term consumer behaviour by leveraging limited data. AI-based company ZineOne has developed a way to model short-term behaviours from using only clickstream data – which contains consumer activity on a site – as an ordered sequence.
Such a model can help brands identify visitors who plan to make transactions from the first few clicks of their site visit. Retailers can then use ML algorithms to personalise pricing and promotions to increase conversions.
[9 minute read]