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All language subtitles for 03 - Campaign management marketing applications

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,005 --> 00:00:03,000 - Campaign management is another area 2 00:00:03,000 --> 00:00:06,009 where agent AI is perfect and can help the campaign manager. 3 00:00:06,009 --> 00:00:09,000 The campaign can be an email campaign 4 00:00:09,000 --> 00:00:10,004 or social media campaign, 5 00:00:10,004 --> 00:00:13,005 or a B2B campaign using events or a workshop. 6 00:00:13,005 --> 00:00:17,002 In any campaign, traditionally, the campaign is set up 7 00:00:17,002 --> 00:00:19,004 with a set of outreach to customers 8 00:00:19,004 --> 00:00:21,002 using some creatives and copy. 9 00:00:21,002 --> 00:00:24,000 The customer segment to target for the campaign is selected 10 00:00:24,000 --> 00:00:27,000 using traditional AI to build out customer segments, 11 00:00:27,000 --> 00:00:29,000 and the next best click is predicted 12 00:00:29,000 --> 00:00:32,001 and a channel is selected to send out the campaign. 13 00:00:32,001 --> 00:00:34,003 The campaign response data is studied 14 00:00:34,003 --> 00:00:36,006 to decide on the next step of engagement 15 00:00:36,006 --> 00:00:38,004 or a new segment of customers. 16 00:00:38,004 --> 00:00:40,008 Can you see how agent AI will break down 17 00:00:40,008 --> 00:00:42,003 this task into steps? 18 00:00:42,003 --> 00:00:45,008 Well, agent AI can do each of these steps 19 00:00:45,008 --> 00:00:48,009 and reason to decide on the success of each campaign 20 00:00:48,009 --> 00:00:51,005 and optimize the copy, creative, and channel 21 00:00:51,005 --> 00:00:54,003 by iterative testing for customer response 22 00:00:54,003 --> 00:00:56,004 to arrive at the optimal combination 23 00:00:56,004 --> 00:00:58,001 that meets the campaign goals 24 00:00:58,001 --> 00:01:00,002 in the most cost-effective way. 25 00:01:00,002 --> 00:01:03,000 The third example I want to share in marketing is 26 00:01:03,000 --> 00:01:04,009 about personalization. 27 00:01:04,009 --> 00:01:07,008 Personalization is based on customer data, 28 00:01:07,008 --> 00:01:10,002 and in traditional AI, this data is used 29 00:01:10,002 --> 00:01:12,005 to build recommender systems with collaborative filtering. 30 00:01:12,005 --> 00:01:16,007 Can you imagine how personalization can be done by agent AI? 31 00:01:16,007 --> 00:01:19,006 Post a video and write your ideas in the notepad. 32 00:01:19,006 --> 00:01:23,008 Personalization is about collecting and analyzing user data 33 00:01:23,008 --> 00:01:27,005 and using the insights to drive the choice of what selection 34 00:01:27,005 --> 00:01:31,004 of products or experiences a user is presented on a website. 35 00:01:31,004 --> 00:01:34,006 So an agent AI can be tasked to achieve this. 36 00:01:34,006 --> 00:01:36,009 It will break it down to multiple steps 37 00:01:36,009 --> 00:01:39,009 and use tools to collect and analyze data, 38 00:01:39,009 --> 00:01:42,004 then use reasoning to make decisions 39 00:01:42,004 --> 00:01:44,001 on what is the optimal set 40 00:01:44,001 --> 00:01:46,006 of product recommendations for the user. 41 00:01:46,006 --> 00:01:49,001 Can you imagine what kind of agent AI can be used 42 00:01:49,001 --> 00:01:51,004 to achieve results with this use case? 43 00:01:51,004 --> 00:01:54,007 Also, the agent AI can tap into other services 44 00:01:54,007 --> 00:01:58,006 and data sets as tools to optimize its decisions. 45 00:01:58,006 --> 00:02:02,000 Multiple agents can come together to run a task 46 00:02:02,000 --> 00:02:05,008 and debate with each other using the reasoning power of LLM 47 00:02:05,008 --> 00:02:09,003 to arrive at the optimal personalization for the user. 48 00:02:09,003 --> 00:02:11,004 Fascinating, isn't it? 49 00:02:11,004 --> 00:02:15,009 We have gone from a single agent doing a set of single tasks 50 00:02:15,009 --> 00:02:19,006 to multiple agents negotiating towards a common goal. 51 00:02:19,006 --> 00:02:21,006 This comes with ethical responsibilities 52 00:02:21,006 --> 00:02:24,007 and the amazing power of agent AI. 53 00:02:24,007 --> 00:02:27,000 We learn about the different reasoning approaches 54 00:02:27,000 --> 00:02:30,000 and ethical considerations in future lessons. 4433