Display advertising ( or any business) is leaving money on the table unless appropriate measurement tools are in place and the campaign ( or product) is optimized for customers. Analytics tools not only help measure standard metrics but provide key insights for optimizing ad ( or product).
As we learned in Statistics 101, hypothesis testing provides a way to help make a decision about the parameter in question based on sample data. Similarly, deciding the most effective type of ad (including combination of multiple creative elements such as imagery, copy etc ), we need to create multiple scenarios(or hypothesis tests) to find out which ad most resonates with the customer. To start the process, we need to establish the business context, set objective of the campaign ( CTR or macro conversion), design concepts, perform tests ( for statistically significant results) and continuously introduce challenger concepts to improve KPIs.
As part of any digital campaign, creative team (UI/UX) needs to come up with various combinations of key images, text or any other rich media format to show the complete value proposition of the product. As it's extremely difficult to understand all customers, generating multiple ads help target different set of customers. Google's unified display network give enough flexibility to target display ads based on the customer segmentation. After running campaign for long enough period to generate statistically significant results, we can find out what ad works for different type of customers and further tailor ad or campaign accordingly.
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