Marketers need access to the tools and technical know-how to use different types of data in order to implement effective hyper-personalised campaigns.
According to Ascend2, the biggest hurdle for marketers trying to rollout hyper-personalised campaigns is making data-driven decisions, with just over half of marketing professionals reporting this issue. The second most frequent challenge was using more artificial intelligence in their hyper-personalisation campaigns. Both issues go back to a central problem for executing hyper-personalised marketing: data use.
Hyper-personalisation campaigns use targeted advertising to better engage customers. Simple personalisation campaigns can take the form of sending consumers an email addressing them by their name or using purchase history to suggest other items to purchase. Hyper-personlisation takes this to the next level, using ever more granular data sets or a combination of data sets to suggest products for consumers and retain customers based on information like the weather and individual spending patterns. Data become more important—and challenging—as the campaigns get more specific, separating an effective hyper-personalised campaign from an ineffective one.
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