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Data is vital to implement personalisation. However, according to a report published by WBR and OneMarket, 64% of retailers said they were not able to identify the majority of their website’s visitors, while 57% said they did not use CRM platforms. This means, in most cases, their customer segmentation is not accurate.
We have already said it on numerous occasions. Each user is unique and, therefore, wants a relevant experience. As a result, when online personalization is inaccurate, consumers get frustrated and leave the website abruptly. In conclusion, this represents the loss of potential customers and, even, potential brand ambassadors.
According to the same report by WBR and OneMarket, about 50% of retailers are prioritizing the adoption of deep learning to improve their own marketing methods. The use of artificial intelligence and machine learning offers a richer understanding of the needs and patterns of visitors to your website and your potential customers.
There are general notions for the implementation of personalization. For example, to understand the interaction from person to person instead of B2B or B2C. Or that of modifying the “sale” argument by one about responding to the client’s interests. The data is what shows the way forward. These notions require technology, data analysis and decision making to be implemented correctly.
For instance, using static records of your customers is not the best strategy. By using the purchase history only to implement personalization or update your marketing strategy, it’s to have a biased and incomplete view of the customer. In other words, purchases made during sales or seasonally, only offer an aspect of your visitors’ profile.
Consequently, the most effective real-time personalization initiatives are those that can take advantage of all types of data that make the client’s profile. The combination of all allows a business to deliver relevant messages to its customers at the right time.