4th December, 2019
Ai Editorial: Personalisation models in e-commerce are not only looking at aspects like predicting a visitor's likelihood of conversion, but machine learning deployed to match different offer variations to each visitor also adapt to changes in visitor behaviour, writes Ai's Ritesh Gupta
There is greater clarity on the journey personalisation today.
Be it for being adept with data (it has to be captured, pooled, processed and analyzed) or personalisation being an ongoing journey, an organization has to prepare and remain diligent.
For instance, in case of delivering targeted content to digital visitors, there is a need to create a personalisation rule (meeting conditions such as mapped behaviour, a certain score etc.), personalize the content (content based on the conditions in the personalisation rule), the layout etc. For all of this to work, the quality of data counts. As it turns out in case of personalized content, it is served on the basis of all the behavioural data about the visitor, plus machine learning deployed to match different offer variations to each visitor also adapt to changes in visitor behaviour.
The much-talked about individualized/ hyper-personalized offer is all about an individual offer based on one's preferences, activities and behaviour. In the ideal conditions, each individualized offer is unique, as shared by Comarch in a recent blog post. The objective of personalisation is to interact with a visitor in ways that optimize for a desired result featuring that individual, such as higher spend, continued loyalty, etc. It is about influencing a customer’s behaviour.
As for the progress being made in e-commerce, specialists already talk about automated personalisation. It not only looks at areas like predicting a visitor's likelihood of conversion, but the model also is trained to focus on a section of traffic to sustain learning throughout the life of the activity, and evaluate evolving preferences of the visitors.
There is provision for using offline data, propensity scores or other custom data to build personalisation models.
No magic bullet
It is fascinating to assess how machine learning helps in assessing the most effective content when targeting diverse visitors. It happens over a period of time as the algorithm learns to predict.
Algorithmic optimization isn’t an overnight phenomena; it entails a layered development of ever-increasing complexity. Plus, machine learning isn’t a magic bullet, capitalizing on its prowess demands diligence and a methodical approach. It one of the many building blocks that lays foundation for an astute loyalty initiative. In the initial stages, don’t target BMW of machine learning, recommends airline loyalty and big data expert, Mark Ross-Smith. Rather than thinking of AI, focus on simple things. For example, first target simple areas like identifying and addressing a traveller. “How to address someone in the right way at the right time,” he points out. A connection between a brand and its customers will build over time, deepening with each gratifying interaction.
Few key takeaways:
Keen on exploring topics related to stepping up the conversion rate? Ai has planned AncillaryRevenue Conferences in Berlin, Bangkok and San Antonio in 2020: