optimize experiences<\/a><\/li><li>AI-powered conversations lift the potential of automated interactions to new levels<\/li><\/ul>\n\n\n","post_title":"The Future of Mobile Messaging","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"the-future-of-mobile-messaging","to_ping":"","pinged":"","post_modified":"2024-01-08 15:00:20","post_modified_gmt":"2024-01-08 21:00:20","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=22488","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":20289,"post_author":"31733","post_date":"2019-08-19 20:32:41","post_date_gmt":"2019-08-19 20:32:41","post_content":"\nIt\u2019s the golden age of artificial intelligence (AI). No longer are machine learning algorithms implemented in covert server rooms with advanced code scripted exclusively by research scientists. Instead, AI-driven insights are readily available with cloud-based software solutions (like <a href=https://www.ama.org/"https:////www.pardot.com//blog//spring-into-pardots-new-feature-set///">Pardot Einstein<\/a>!) which are flexible, simple to integrate, and tremendously powerful.<\/p>\n\n\n\nStill, some marketers remain skeptical of AI\u2019s vast promises. But organizations today have every reason to embrace artificial intelligence in the Fourth Industrial Revolution. Here\u2019s why. <\/p>\n\n\n\n<h2>WHAT CAN ARTIFICIAL INTELLIGENCE (AI) DO FOR MARKETING?<\/strong><\/h2>\n\n\n\nAI fundamentally addresses two types of questions.<\/p>\n\n\n\n<ul><li>Is X similar to Y?<\/li><li>What influence does A have on B?<\/li><\/ul>\n\n\n\nThe first question is foundational to marketing segmentation. After all, a segment is useful only if there are some similarities among its records. (Otherwise, it\u2019s a random population, and not particularly actionable for content-specific marketing campaigns).<\/p>\n\n\n\nAI can help marketers with segmentation because machine learning algorithms can identify similarities between records. Marketers can specify an audience of interest \u2013 like historically high-converting leads \u2013 and then use similarities in this population to create new segments to target. An application of this would be the generation of <a href=https://www.ama.org/"https:////www.facebook.com//login//?next=https%3A%2F%2Fwww.facebook.com%2Fbusiness%2Fhelp%2F164749007013531\%22>Lookalike Audiences in Facebook<\/a> \u2013 something especially valuable in B2C marketing where markets are routinely wide and varied. <\/p>\n\n\n\nThe second question, that of identifying causal influences in datasets, speaks to the heart of all B2B analytics. At its core, a prediction is a hypothesis about variables. An example is that the mailing state within a contact record influences whether it also has associated opportunity. With large enough datasets, machine learning can not only forecast whether certain variables have a causal influence on a target result, but also how much each value within that variable influences a target. So not only can it determine whether mailing state is an influencing variable, but it can also identify whether a value of \u201cCA\u201d is more likely to have an associated opportunity than a value of \u201cNY.\u201d What\u2019s more, that increased likelihood of conversion can be codified into a quantitative score, which rates a contact from California with a higher score than one from New York.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=https://www.ama.org/"https:////lh4.googleusercontent.com//bjBv-4kdOKdSoERseupwleshCf3wUZ-lajRazRyWDRcI2fcJRaTBs-f1v1QC5MkQU5I5tYJyHBrZWf59dNC3AnPTUGLDGnO8KWvGdY-bOkbGo9yau7CJXLxf8nJND-orv61UkSzL/" alt=\"\"\/><\/figure>\n\n\n\nUse cases like these should pique the interest of all B2B marketers. With large datasets, and potentially thousands of data points, there\u2019s a tremendous appeal to technology that can identify which variables are most likely to influence a conversion rate, and, likewise, which kinds of campaigns are more effective in attracting business than others.<\/p>\n\n\n\n<h2>WHAT STANDS IN THE WAY OF LEVERAGING AI?<\/strong><\/h2>\n\n\n\nDespite the attractions of AI, many companies aren\u2019t positioned to hire a data science team. After all, data scientists are expensive and often leverage a tech stack that doesn\u2019t integrate into existing business systems.<\/p>\n\n\n\nMoreover, even with an all-star data science team, there\u2019s the risk that AI-driven predictions won\u2019t be relevant. If their prediction engine isn\u2019t integrated with the dynamic<\/em> data sources in a CRM or marketing platform, they won\u2019t be able to leverage these insights to make a business impact. How useful are predictions leveraging click behavior if they only get updated once per month?<\/p>\n\n\n\n<h2>THE SOLUTION: CRM-INTEGRATED AI<\/strong><\/h2>\n\n\n\nB2B marketers have extraordinarily rich datasets at their disposal. Thus, if they\u2019re interested in the maximizing the return on an investment in AI, the need for an integrated solution is clear. In other words, predictions ought to leverage all available contact and campaign data, and that\u2019s only possible with an AI solution that with the CRM and marketing systems that house them. Cue <a href=https://www.ama.org/"https:////www.pardot.com//blog//predict-buying-intent-with-pardot-einstein-behavior-score///">Pardot Einstein<\/a>, the plug-and-play AI tool for B2B marketers. In contrast with proprietary data science solutions, Pardot Einstein natively integrates with both Salesforce and Pardot, so there\u2019s no lengthy configuration and testing process. Once Pardot Einstein is activated, data from both systems flow into the Einstein prediction engine. At that point, predictions get generated in hours, not years, and they get updated dynamically as prospects engage with marketing assets in Pardot.<\/p>\n\n\n\n Because Pardot captures an incredible diversity of prospect data, these predictions are sensitive to a myriad of marketing behaviors. What\u2019s the conversion coefficient for a new landing page? While a data scientist might struggle to build a system responsive to new variables, a CRM-integrated AI engine, like Pardot Einstein, adapts naturally. Moreover, the predictions are better because they\u2019re leveraging more variables \u2014 <\/em>and, thus, more complete data \u2014 <\/em>from both Pardot and Sales Cloud.<\/p>\n\n\n\n<h2>YOUR COMPETITORS ARE DOING IT<\/strong><\/h2>\n\n\n\nIt\u2019s an exciting time to be a B2B marketer. Marketing and CRM platforms are in a mature state, and cloud-based storage and computation seems virtually unlimited. But there\u2019s one thing missing: how to leverage the data in a system to build better audiences and make better predictions.<\/p>\n\n\n\nThat\u2019s where AI fits in. In our estimation, nothing else has the possibility of driving business results than this area of digital transformation. <a href=https://www.ama.org/"https:////www.pardot.com//whitepapers//b2b-marketing-trends///">In 2018, 30% of B2B marketers were using AI, a 23% increase from the year before<\/a>. Do you want to be at the vanguard of marketing technology? Today marks an opportunity to be a leader. What are you waiting for? <\/p>\n","post_title":"No Lead Left Behind: Why Marketers Must Invest in Artificial Intelligence","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"no-lead-left-behind-why-marketers-must-invest-in-artificial-intelligence","to_ping":"","pinged":"","post_modified":"2024-01-08 15:02:16","post_modified_gmt":"2024-01-08 21:02:16","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=20289","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":19653,"post_author":"23862","post_date":"2019-08-02 14:52:07","post_date_gmt":"2019-08-02 14:52:07","post_content":"\nImplement Effective Marketing Automation Software and Processes for Unrivaled Success<\/p>\n\n\n\nMarketing automation activates one-to-one conversations with customers at scale. This eBook will help you:<\/p>\n\n\n\n<ul><li> Understand key features and benefits of marketing automation <\/li><li> Build the business case for marketing automation <\/li><li> Implement and utilize marketing automation <\/li><li> Leverage marketing automation to encourage sales and marketing alignment <\/li><li> Read what marketing automation experts have to say about how to make marketing automation a reality. <\/li><\/ul>\n\n\n","post_title":"Making Marketing Automation a Reality","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"making-marketing-automation-a-reality","to_ping":"","pinged":"","post_modified":"2024-01-08 15:02:49","post_modified_gmt":"2024-01-08 21:02:49","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=19653","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":13964,"post_author":"61","post_date":"2019-05-08 18:52:02","post_date_gmt":"2019-05-08 18:52:02","post_content":"\nOver one in three B-to-B marketers are using artificial\nintelligence (AI), according to a new study by Salesforce Pardot, but the\nmassive amounts of data generated by this new technology make it a challenge\nfor marketers to realize its true business impact. We caught up with Nate\nSkinner, vice president of product marketing at Salesforce, to discuss how\nB-to-B marketers can best put AI to work for them, how marketers can make the\nmost of AI in their account-based marketing programs, and what the future might\nhold for these technologies. <\/p>\n\n\n\nQ: How is AI impacting\nB-to-B marketers\u2019 jobs on a daily basis, changing how they gather and segment\ncustomer data, personalize customer experiences, and other tasks?<\/strong><\/p>\n\n\n\nA: AI is actually a\ndistraction for marketers, because there\u2019s a lot of noise about what AI\nactually is and how people get value from it. Marketers need to figure it out\nand don\u2019t know where to start. We found that 30% of marketers are using AI in\nsome capacity, 24% more than last year. It\u2019s clearly not a fad. But for AI to\nbe done properly, you have to have all of your data in one place. Salesforce\u2019s\nAI platform, Einstein, can generate automated insights on campaign performance\nand lead scoring. That\u2019s the power of AI done properly \u2013 it works on your\nbehalf to provide information. For key accounts, it assigns a higher-rated lead\nscore, and shows you next-step actions and suggestions on how to engage the\nprospect. <\/p>\n\n\n\nQ: Despite the\ngrowing popularity of AI, no dominant use case for the technology has emerged.\nWhy do you think this is? <\/strong><\/p>\n\n\n\nA: So much of AI is in our everyday lives. Amazon implements\nit, and it\u2019s in Gmail, which autocompletes email text. Salesforce Einstein does\nthat, too. For B-to-B marketers, there are so many opportunities to apply it,\nbut the power of AI comes from data. If you don\u2019t have data, AI doesn\u2019t work.\nFor many customers, data is all over the place: in your CRM, in your ads\nplatform, in your data management platform. If you don\u2019t have a single place\nfor all the data, aggregated, then you can\u2019t use machine learning to tell you\nthings you didn\u2019t already know. If you have siloed implementations of AI, you\nwon\u2019t get the biggest bang for your buck. Our customers are seeing specific use\ncases that are impactful because their data is on the same platform, where\nmachine learning models can run against all the data. Einstein shows you\ncampaigns that are highest performing and makes suggestions about running\ncampaigns again. Those are the insights and better use cases you get when you\nrun all the data in the same place. <\/p>\n\n\n\nQ: What are the\nbiggest challenges of implementing AI so that it has real business impact, and\nhow can B-to-B marketers address those challenges?<\/strong><\/p>\n\n\n\nA:<\/strong> All marketers\ncan implement AI, but actually getting it to move the needle for your business\nis hard because there are so many niche vendors who aren\u2019t using marketing\nmuscle to understand buyer need. Sales data doesn\u2019t match marketing data, or\nvice versa. For most B-to-B companies, the sales team is selling to an account,\nnot to one person. Most companies aren\u2019t aligned around a committee of buyers,\nthey\u2019re aligned around getting one person to do something. That\u2019s why it\u2019s so\nchallenging. If you connect all of those things and have a clear line of sight across\nsales and marketing, accounts you\u2019re going after and people you need to engage,\nyou could start to see massive business impact. We\u2019re seeing game-changing\nresults in ABM when you put martech, adtech and sales tech together, with AI at\nthe center. The challenge is bringing it all together and finding truth around\ndata that powers the AI initiative. You can address it by stepping back and\nlooking at whether your martech stack works for you, or vice versa. Many\ncustomers keep buying technology to solve individual problems, but they need to\nmake sure their process connects to that technology to drive business results. <\/p>\n\n\n\nQ: Account-based\nmarketing (ABM) now accounts for 28% of total user marketing budgets, and 45%\nof ABM users are seeing at least double ROI compared to other marketing\nmethods, according to Salesforce Pardot. Do you expect these trends to\ncontinue, and why? <\/strong><\/p>\n\n\n\nA:<\/strong> ABM isn\u2019t new,\nbut the reason it\u2019s gotten so much traction is because it\u2019s now scalable. Marketing\ncan now take an account-first approach and have it be economically viable, and\nit\u2019s easier to prove ROI. With the value in increased conversion rates and deal\nsizes getting bigger across every market, ABM strategies are going to continue,\nbecause the results speak for themselves. Most customers we speak to are getting\nstarted on an account-first marketing approach and are thinking about the technology\nstack that will allow them to do that, so we\u2019re nowhere near the tipping point.\n<\/p>\n\n\n\nQ: How is AI helping supercharge\nABM, and how can B-to-B marketers make the most of AI in their ABM programs? <\/strong><\/p>\n\n\n\nA:<\/strong> AI helps\nsupercharge ABM in the same way that it helps supercharge broad-based marketing\ncampaigns \u2013 if you have your arms around your data. If you don\u2019t, if you\u2019re tracking\nsales data separate from marketing data, you\u2019ll run into trouble. AI is only as\npowerful as the data that\u2019s generating the recommendations, so you can\u2019t\nexecute your strategy if the data is in different places. ABM starts with accounts,\nso to make the most of AI in B-to-B marketing programs, your content and data\nshould be in the same place, so you have access to it in the machine layer. For\naccount and context selection, and engagement orchestration, you need to\nmeasure it based on the same source of truth, otherwise, your marketing team is\nmeasuring the impact of ABM whiles sales is looking at whole different set of\nmetrics. \n \nQ: Salesforce Pardot reported that\nhigh-performing B-to-B sales teams are 2.2 times more likely than\nunderperformers to execute ABM programs jointly with marketing teams. What\u2019s\nyour advice for achieving better sales\/marketing alignment to optimize ABM? <\/strong><\/p>\n\n\n\nA:<\/strong> Tracy Eiler, the\nCMO of InsideView Technology, wrote a book that says that data is the ultimate\nequalizer when it comes to sales\/marketing alignment. If both departments are talking\nabout the same data sets and metrics, if they have the same view of the world,\nthey\u2019ll have alignment. When you add people to the mix, and give them a single\ndashboard of the same metrics, that takes it to the next level. Imagine the\nopposite of that, which most companies have: marketing is measuring its\nperformance, and sales is measuring its performance, and nobody\u2019s measuring\nthem together. If sales and marketing can\u2019t agree on metrics that matter, you\ncan\u2019t build an ABM program. Sit in a room together and agree on three metrics\nthat are most important for both of you, build an ABM strategy together, and\nmeasure your efforts based on those three metrics. Bring the teams together and\nrealize a common mission. \n \nQ: What are the biggest challenges\nB-to-B marketers face when it comes to ABM?<\/strong><\/p>\n\n\n\nA:<\/strong> For most organizations,\nmarketing and sales are divided. Processes and tools are separate. Sales teams\nidentify accounts and develop account-specific content and campaigns, then\nmeasure and optimize efforts without touching marketing at all. When it comes\nto ABM, we\u2019re using different words to describe the same thing. When the customer\ndoesn\u2019t think you\u2019re speaking to them in one voice, your AMB strategy is\nfailing. Breaking down siloes is the biggest challenge, and bringing common\nnomenclature to both sales and marketing is the next-biggest challenge. Marketing\nis ironically a little behind in terms of account-first thinking: marketers have\nbeen thinking about ABM for the first time maybe in the last five years, but\nsales teams and B-to-B companies have been thinking account-first forever. If\nmarketers come in hot with an account-based strategy but don\u2019t include the sales\npoint of view, they\u2019ll be out of sync with reality, because the sales team is\nalready thinking that way. <\/p>\n\n\n\nQ: What do you think\nthe future holds for AI and ABM, and how can marketers prepare? <\/strong><\/p>\n\n\n\nA:<\/strong> We\u2019ll continue\nto see B-to-C marketing and B-to-B marketing converge as it relates to best\npractices for both AI and ABM. B-to-C marketers are reaching customers via\nLinkedIn, Instagram and Google, and B-to-B marketers are starting to do that,\ntoo. A lot of these trends are powered by AI. Imagine the kind of technology\nwhere AI tells you, based on all the campaigns you\u2019ve run, here\u2019s one to try,\nand one you\u2019ve never thought of. For ABM, it\u2019s the same: AI-powered ABM campaigns\nwill recommend new account types to consider. For instance, in financial\nservices, there are several sub-groups, like wealth management and brokerage\nfirms, and AI might tell you to run a campaign that targets a whole new\nsub-group, high-net-worth individuals, by taking everything you\u2019ve already learned\nabout marketing to wealth mangers and brokers and marketing to high-net-worth\nindividuals. There are a lot of things that I can\u2019t imagine possible in the\nnext three to five years, but those that are out front of the AI and ABM trends\nwill change their business. <\/p>\n","post_title":"How AI and Account-Based Marketing are Changing the Game for B-to-B Marketers","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"how-ai-and-account-based-marketing-are-changing-the-game-for-b-to-b-marketers","to_ping":"","pinged":"","post_modified":"2024-01-08 15:05:36","post_modified_gmt":"2024-01-08 21:05:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=13964","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":13684,"post_author":"24791","post_date":"2019-05-02 17:02:13","post_date_gmt":"2019-05-02 17:02:13","post_content":"\n\n\nThere's been a lot of hype about the impact of artificial intelligence in marketing, but how are those on the front line of business really preparing for the AI revolution?\n\n<\/p>\n\n\n\n\n\nThe Drum surveyed over 200 marketers to find out how they view AI technology, how they expect it to change their jobs, and what their organizations are doing to take advantage of the opportunities of artificial intelligence.<\/p>\n\n\n\nYou can find the full results of the survey in this exclusive report, published in partnership with Sysomos and featuring expert comment from our Chief Strategy Officer, David Berkowitz.\n\n<\/p>\n\n\n","post_title":"Artificial Intelligence in Marketing","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"artificial-intelligence-in-marketing","to_ping":"","pinged":"","post_modified":"2024-01-08 15:05:42","post_modified_gmt":"2024-01-08 21:05:42","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=13684","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":26370,"post_author":"13094","post_date":"2019-12-16 16:19:35","post_date_gmt":"2019-12-16 16:19:35","post_content":"\n<h2>There's a lot to look forward to in the new year\u2014including all the ways artificial intelligence will affect your job and your business<\/h2>\n\n\n\n<div class=\"wp-block-columns\">\n<div class=\"wp-block-column\" style=\"flex-basis:19%\">\n<div class=\"wp-block-image\"><figure class=\"alignright is-resized\"><a href=https://www.ama.org/"https:////www.ama.org//looking-ahead-to-2020-and-a-review-of-2019///"> $\"\"$ The Year Ahead 2020<\/a> <\/em><\/strong>special web issue<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2019//12//year-ahead_INFOGRAPHIC_TEASE.png?fit=1024%2C668\%22 alt=\"\" class=\"wp-image-26409\"\/><\/figure><\/div>\n\n\n ","post_title":"What Marketers Should Know About AI in 2020","post_excerpt":"There's a lot to look forward to in the new year\u2014including all the ways artificial intelligence will affect your job and your business","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"what-marketers-should-know-about-ai-in-2020","to_ping":"","pinged":"","post_modified":"2024-01-22 14:14:53","post_modified_gmt":"2024-01-22 20:14:53","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?post_type=ama_marketing_news&p=26370","menu_order":0,"post_type":"ama_marketing_news","post_mime_type":"","comment_count":"0","filter":"raw"}]" />

Artificial Intelligence

Artificial intelligence is an area of computer science concerned with designing smart computer systems. AI systems exhibit the characteristics generally associated with intelligence in human learning, reasoning, and solving problems.

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