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rajia sultana
May 08, 2022
In Welcome to the Forum
Any marketer will tell you that applying AI to marketing is a current trend and has the potential to disrupt the industry. Just last week, Oracle announced it was providing artificial intelligence in its customer experience cloud. Oracle's announcement follows a long Phone Number List series of press releases from major marketing clouds such as Salesforce, IBM and Adobe. Space is so hot, in fact, that there seems to be a contest to have the best name for your AI, with the likes of Einstein, Watson and Sensei regularly vying for top billing. If you look past the cute names, however, you'll find there's real technology at play. AI Marketing solutions aren't just a new way to describe the collaborative Phone Number List filtering recommendation engines that have gained popularity the decade (for example,. Nor are they the A/B/N or multi-variate testing tools that emerged earlier in this decade. They are, sure, taking advantage of all this and more, advances in AI technologies such as image/facial recognition, natural language processing/generation, and Phone Number List machine learning to fundamentally change the way marketing modern is carried out. Current marketing clouds are insufficient The challenge with traditional marketing automation solutions is that they're only as scalable or as smart as the marketers running them. While such solutions have shown the Phone Number List ability to automate marketing execution for specific “if/then” scenarios, these solutions fall woefully short when trying to apply them at scale in a large B2C company for three reasons: main: 1. To achieve the desired targeting granularity, marketers must write and manage dozens of “if/then” rules across hundreds or even thousands of campaigns. All targeting rules must be defined before launching the Phone Number List campaign. Initial success therefore depends solely on the experience and “best guess” capabilities of the marketing manager setting up the campaign. 3. Running A/B/N tests to optimize campaign effectiveness remains a very manual and labor-intensive process, often requiring a data scientist to be involved and spend weeks to do lift or propensity modeling, to come up with a recommendation for improvement that, while helpful, only positively affects a small portion of the total marketing audience. Because of Phone Number List these challenges, marketers spend their time programming campaign rules, managing exclusion groups, and analyzing test results instead of
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