Since most consumers want to be recognized as individuals today, who generally expect personalization in marketing campaigns, Artificial Intelligence (AI) gets amazingly to work amazingly by ensuring the delivery of an effective brand experience.
Because brands have significantly more information at their disposal, it is possible to engage each lead with relevant and specific messaging or content and ‘predictive personalization’, which means detailed and refined personalization driven by Machine Learning (ML), come into play.
If an online shop visitor sees a product that exactly meets his/her requirements and buys it, data-based personalization made it happen thanks to large amount of customer data available: for example, based on previous purchases and search queries as well as age and gender. Pity that there is often still a lack of know-how and time to evaluate this data in a targeted manner.
Systems with artificial intelligence (AI) that automatically analyze large amounts of data provide a remedy. Based on the findings, a profile of the visitor gets created and suitable content is proposed: in the fashion area, for example, trendy outfits, combination options and styling tips.
The visitor profile is also considered in search queries: for example, if a customer is searching online for boots, the AI personalizes the order of the models shown. If the customer prefers comfort rather than elegance, he/she receives pictures and information about sporty boots first, while boots with heels are only shown later. In this way, the customer experience can be improved, and the purchase probability is significantly higher.
Artificial intelligence can even help if there is no purchase or surfing history, since the technology is able to evaluate a visit in real-time: if the visitor has just viewed a product, items are suggested that are similar, and if the visitor looks at these suggestions, further data is produced that can be used in real-time. The system simply learns what works, and what doesn't, and optimizes its measures accordingly.
AI can also be used to measure more precisely which content formats - such as text, images, video - work for which customers and how certain content is received by the online audience. So, the systems also learn e.g. whether the assignment of offers to a profile was successful. Accordingly, they can make decisions about the right time and communication channel for a communication measure.
AI-based personalization strategies not only ensure increasing conversion rates and satisfied customers, but more efficiency. Marketing managers save time-consuming activities such as dividing buyer groups into segments and the corresponding content assignment. Furthermore, they receive help with incentive decision-making, which avoids wastage and can even turn inactive users into active customers.
The use of AI can even open up completely new paths for personalization: AI-based visual search solutions, such as e.g. StyleSnap from Amazon or Slyce, enables a potential customer to take pictures of items (e.g. from the fashion industry) and the AI then analyzes the photo and suggests similar products in the online shop. This technology is based on Google's Cloud Vision API, which can be easily integrated into an app or the mobile version of the online shop.
But trend scouts also benefit from AI-based image recognition. In the past, photos had to be viewed to identify trends at an early stage, which is an extremely complex and error-prone process. With the new image recognition models, millions of images can be analyzed in a very short time. The so-called ‘AI trend and demand forecasting’ is used by the sports manufacturer Nike, among others.
Anyway, whether real-time analysis or visual search, the use of AI makes the personalization of communication more efficient and effective - and online providers and their customers benefit equally from this.
By Daniela La Marca