The use of Artificial Intelligence in marketing has revolutionized consumers’ shopping experience. Based on the history of customer preferences and behavior, it is possible to personalize contact and the offers sent, which directly impact sales and strengthen the relationship between the company and the public.
When associated with marketing automation, Artificial Intelligence carries out all stages of the process without involving manual actions, which contributes to time optimization. An example of a solution is Send Frequency, developed by E-goi Digital Solutions, a company specializing in omnichannel marketing automation and tailored solutions. The technology can identify the best day and time when each subscriber is most likely to interact with the email campaign, based on their history of past interactions.
In one of the implementation cases, Send Frequency increased the ticket average sales in the first two months. The solution was implemented by E-goi Digital Solutions in the communication strategy of a fashion brand in Portugal. Email campaigns sent on slots personalized products had a minimum opening rate of 56%, without taking up more time from the brand’s marketing team.
The technology analyzes each subscriber’s behavior history based on their interactions, from opening to click, as well as similar actions based on the subscribers’ profile. The model is then trained, defining the best day and time to send the hyper-personalized communication over the next seven days. With implementation, we ensure that communications will be delivered when customers are most likely to open and interact with them. This means more opens, more clicks and more conversions for your businessexplains the head of Innovation and Research at E-goi, Daniel Alves de Oliveira, responsible for managing the project.
The Send Frequency model is part of a series of algorithms designed by E-goi’s Research & Development Center, to be constantly trained and adapted to any business model, with the aim of maximizing conversion. All techniques and approaches implemented were investigated and improved, always with a scientific basis.
With each campaign sent, the technology stores the individual behavior of subscribers in the model itself, reinforces learning and improves the recommendation of future campaigns, which increases efficiency, concludes the executive.
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