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The Forrester Wave\u2122: Cross-Channel Campaign Management (Independent Platforms), Q3 2021 report<\/em><\/a> are an indication of what\u2019s new and what\u2019s to come, one thing\u2019s for sure: AI has officially moved past the hype and is here to stay.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>What is Cross Channel Campaign Management (CCCM)?<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Before we talk about the latest and greatest in CCCM, it\u2019s important to know what exactly CCCM is, and how platforms can help. Customer experience has the power to make or break a brand. Today\u2019s consumers demand personalization across channels, with content in-context. Every engagement is an opportunity to demonstrate that you know who they are, what they care about, and how you can help them. As more companies continue to realize it\u2019s of the utmost importance to improve digital experiences through personalization, CCCM platforms allow brands to do just that by making it possible to connect with their customers across multiple channels to stay top-of-mind. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Forrester defines a Cross-Channel Campaign Management (CCCM) platform as \u201cEnterprise marketing technology that supports customer data management, analytics, segmentation, and workflow tools for designing, executing, and measuring campaigns for digital and offline channels\u201d. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Essentially, CCCM platforms provide unified customer views across online and offline channels, making it much easier for marketing and product managers to build and deliver personalized experiences. With this platform, you can create and execute campaigns across online and offline channels without the hassle of switching through multiple tools and databases. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>It\u2019s important to know that CCCM platforms are not the same as a customer data platform (CDP) or a marketing automation platform, which offer a single view of the customer without campaign orchestration capabilities, or offer lead generation capabilities without a single customer view overtime. Rather, CCCM platforms may include these capabilities either through integration or natively. <strong>When it comes down to it, CCCM platforms are the new go-to choice for brands looking to build deeper relationships with their customers. <\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>(To better understand the CCCM landscape, get your complimentary copy of \u201cThe Forrester Wave\u2122: Cross-Channel Campaign Management, Q3 2021\u201d <a href=https://www.ama.org/"https:////www.moengage.com//exp//strong-performer-forrester-wave-cccm-2021//?utm_source=ama-n&utm_medium=paid_social&utm_campaign=forrestercccm2021&utm_content=forrestercccm2021report&utm_term=ama-feature\%22>report here<\/a>.)<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>The Future is AI-Driven<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Things look a lot different this year for CCCM. Based on the latest Forrester Wave\u2122 for Cross-Channel Campaign Management, MoEngage was positioned as a Strong Performer, receiving recognition for its AI-driven insights and personalization capabilities. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong><em>While others are still stuck on enabling cohorts and audiences, a platform like MoEngage brings marketers into the future, today. <\/em><\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Teams that are armed with AI-driven insights and the right engagement tools are finally able to create meaningful customer experiences at scale across channels like web, mobile, email, social, and more. <strong>These powerful capabilities empower marketing and product managers to identify the best customers to target with individualized messaging at unprecedented speeds. <\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><em>Without<\/em> the help of AI-powered automation, it is almost impossible to achieve personalized experiences at scale. Today, brands use automation to optimize user interactions and enhance the overall customer experience to deliver the right content, on the right channels, based on user behaviors and preferences. For example - for retail and ecommerce brands, when a user performs a predefined action like abandons a cart or installs an app,  cross-channel marketing platforms like MoEngage can automatically trigger alerts via push, SMS or email notifications bringing the user back into the active purchase funnel. (For more examples of how AI helps improve marketing campaign performance, <a href=https://www.ama.org/"https:////www.moengage.com//blog//ai-in-marketing-3-ways-to-improve-marketing-campaign-performance//?utm_source=ama-n&utm_medium=paid_social&utm_campaign=forrestercccm2021&utm_content=forrestercccm2021report&utm_term=ama-feature\%22>check out this article.<\/a>)<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Teams that leverage AI in their marketing and product experiences are immediately seeing improvements in performance, too. Take the marketing and product team at Empiricus, for example. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Marketing and product teams at Empiricus wanted to re-evaluate their customer onboarding journey. They decided to leverage AI and automation capabilities, and immediately spotted an area to improve<strong> <\/strong><a href=https://www.ama.org/"https:////www.moengage.com//blog//empiricus-increases-conversions-45-percent-user-path-analysis//?utm_source=ama-n&utm_medium=paid_social&utm_campaign=forrestercccm2021&utm_content=forrestercccm2021report&utm_term=ama-feature\%22>that ended up increasing conversions by 45%. <\/strong><\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>With the help of AI-driven insights, the team was able to achieve this in a fraction of the time it would have taken to do using their typical manual process. Today, the team at Empiricus are continuing to leverage AI to turn insights into action, instantly. A win for their team, and for their customers. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>With MoEngage\u2019s appearance on the CCCM wave, we\u2019re anticipating future disruptions to occur in the market as other vendors will be forced to keep up. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>Interested in seeing how MoEngage empowers marketing and product owners with AI-driven insights? <\/strong><a href=https://www.ama.org/"https:////www.moengage.com//request-demo//?utm_source=ama-n&utm_medium=paid_social&utm_campaign=forrestercccm2021&utm_content=forrestercccm2021report&utm_term=ama-feature\%22>Click here to request a product demo<\/strong><\/a><strong> and speak with an expert. <\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong><br><\/strong><strong>About MoEngage<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><a href=https://www.ama.org/"https:////www.moengage.com//?utm_source=ama-n&utm_medium=paid_social&utm_campaign=forrestercccm2021&utm_content=forrestercccm2021report&utm_term=ama-feature\%22>MoEngage<\/a> is an insights-led customer engagement platform, trusted by more than 1,200+ global consumer brands such as Ally Financial, McAfee, Flipkart, Nestle, T-Mobile, Travelodge, and more. MoEngage empowers marketers and product owners with insights into customer behavior and the ability to act on those insights to engage customers across the web, mobile, email, social, and messaging channels. Consumer brands across 35 countries use MoEngage to send more than 50 billion messages to engage 900 million users every month. With offices in nine countries, MoEngage is backed by Multiples Private Equity, Eight Roads, <strong>F-Prime Capital, Matrix Partners, Ventureast, and Helion Ventures.<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>To stay updated on the latest CCCM landscape, download a complimentary copy of the \u201cThe Forrester Wave\u2122: Cross-Channel Campaign Management, Q3 2021\u201d report <a href=https://www.ama.org/"https:////www.moengage.com//exp//strong-performer-forrester-wave-cccm-2021//?utm_source=ama-n&utm_medium=paid_social&utm_campaign=forrestercccm2021&utm_content=forrestercccm2021report&utm_term=ama-feature\%22>by going here.<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"53px\"} -->\n<div style=\"height:53px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"id\":85083,\"sizeSlug\":\"full\",\"linkDestination\":\"none\"} -->\n<figure class=\"wp-block-image size-full\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2021//08//MoEngage-Transparent-Bluelogo-270.png/" alt=\"\" class=\"wp-image-85083\"\/><\/figure>\n<!-- \/wp:image -->","post_title":"The Future of Cross Channel Campaign Management is Here: AI","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"the-future-of-cross-channel-campaign-management-is-here-ai","to_ping":"","pinged":"","post_modified":"2024-01-04 12:51:35","post_modified_gmt":"2024-01-04 18:51:35","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=85000","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":70523,"post_author":"91557","post_date":"2020-11-24 16:59:32","post_date_gmt":"2020-11-24 16:59:32","post_content":"<!-- wp:paragraph -->\n<p>Amazon recently launched a Personal Shopper service that uses a combination of AI technology and human stylists to curate clothing for Prime members. Based on a style profile generated by the customer, a human stylist chooses pieces from an AI-mined database of thousands of brands. Subscription services like Stitch Fix and Thread are taking similar approaches to personalized styling, using AI to assist human recommendations.<br> <br>More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide recommendations to consumers. As these companies evaluate AI-based assistance, one critical question must be asked: When do consumers trust the \u201cword of machine,\u201d and when do they resist it?<br> <br>A new <em><a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\">Journal of Marketing<\/a> <\/em>study explores reasons behind the preference of recommendation source (AI vs. human). The key factor in deciding how to incorporate AI recommenders is whether consumers are focused on the functional and practical aspects of a product (its utilitarian value) or on the experiential and sensory aspects of a product (its hedonic value).<br> <br>Relying on data from over 3,000 study participants, our research team provides evidence supporting a word-of-machine effect. We define this effect as the phenomenon by which the trade-offs between utilitarian and hedonic aspects of a product determine the preference for, or resistance to, AI recommenders. The word-of-machine effect stems from a widespread belief that AI systems are more competent than humans at dispensing advice when functional and practical qualities (utilitarian) are desired and less competent when the desired qualities are experiential and sensory-based (hedonic). Consequently, the importance of utilitarian attributes determine preference for AI recommenders over human ones, while the importance of hedonic attributes determine resistance to AI recommenders over human ones. <br> <br>In actuality, humans are not necessarily more competent at evaluating hedonic products and AI is not necessarily more competent at evaluating utilitarian products. In fact, we can find examples in the marketplace today that challenge this belief. For instance, AI selects flower arrangements for 1-800-Flowers and creates new flavors for food companies such as McCormick, Starbucks, and Coca-Cola. Still, by and large, this widespread belief exists. <br> <br>We first tested the word-of-machine effect using experiments designed to assess people\u2019s tendency to choose products based on consumption experiences and recommendation source. We found that when presented with instructions to choose products based solely on utilitarian\/functional attributes, more participants chose AI-recommended products. When asked to only consider hedonic\/experiential attributes, a higher percentage of participants chose human recommenders. The word-of-machine effect also extended to product consumption and taste perception. In one study, we presented participants with two chocolate cakes, one created with the ingredients selected by an AI chocolatier and one created with ingredients selected by a human chocolatier. Participants ate one of the two cakes and rated it on two hedonic\/experiential features (indulgent taste and aromas, pleasantness to the senses) and two utilitarian\/functional attributes (beneficial chemical properties, healthiness). The AI-recommended cake was rated as less tasty but healthier than the cake recommended by the human, even though they were identical in appearance and actual ingredients. <br> <br>When utilitarian features are most important, we found that the word-of-machine effect was more distinct. In one study, participants were asked to imagine buying a winter coat and rate how important utilitarian\/functional attributes (e.g., breathability) and hedonic\/experiential attributes (e.g., fabric type) were in their decision making. The more utilitarian\/functional features were highly rated, the greater the preference for AI over human assistance, and the more hedonic\/experiential features were highly rated, the greater the preference for human over AI assistance. <br> <br>Another study indicated that when consumers wanted recommendations matched to their unique preferences, they resisted AI recommenders and preferred human recommenders regardless of hedonic or utilitarian preferences. These results suggest that companies whose customers are known to be satisfied with \u201cone size fits all\u201d recommendations (i.e., not in need of a high level of customization) may rely on AI-systems. However, companies whose customers are known to desire personalized recommendations should rely on humans. <br> <br>Although there is a clear correlation between utilitarian attributes and consumer trust in AI recommenders, companies selling products that promise more sensorial experiences (e.g., fragrances, food, wine) may still use AI to engage customers. In fact, we found that people embrace AI\u2019s recommendations as long as AI works in partnership with humans. When AI plays an assistive role, \u201caugmenting\u201d human intelligence rather than replacing it, the AI-human hybrid recommender performs as well as a human-only assistant.<br>  <br>Overall, the word-of-machine effect has important implications as the development and adoption of AI, machine learning, and natural language processing challenges managers and policy-makers to harness these transformative technologies. The digital marketplace is crowded and consumer attention span is short. Understanding the conditions under which consumers trust, and do not trust, AI advice will give companies a competitive advantage in this space.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><a href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920957347/" target=\"_blank\" rel=\"noreferrer noopener\">Read the full article<\/a>.  <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>From: <\/strong>Chiara Longoni and Luca Cian, \u201c<a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920957347/" target=\"_blank\">Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The 'Word-of-Machine' Effect<\/a>,\u201d <em><a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/a><\/em>, 84.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Go to the <em><a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/a><\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:block {\"ref\":56764} \/-->\n\n<!-- wp:acf\/ama-curated-posts {\"id\":\"block_5fbd300d8f63c\",\"name\":\"acf\/ama-curated-posts\",\"data\":{\"title\":\"Related Articles\",\"_title\":\"field_5cf4b10fc4ef3\",\"picks\":[\"70312\",\"69029\",\"20842\"],\"_picks\":\"field_5cf4b131c4ef4\",\"columns\":\"1\",\"_columns\":\"field_5d65283c9b4d2\"},\"mode\":\"edit\"} \/-->","post_title":"When Consumers Trust AI Recommendations\u2014or Resist Them","post_excerpt":"AI-driven recommendations are all the rage in ecommerce. But do they work better for utilitarian purchases like wheelbarrows or hedonic purchases like designer shoes?","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"when-consumers-trust-ai-recommendations-or-resist-them","to_ping":"","pinged":"","post_modified":"2024-01-08 14:47:16","post_modified_gmt":"2024-01-08 20:47:16","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=70523","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":70312,"post_author":"63051","post_date":"2020-11-19 05:01:00","post_date_gmt":"2020-11-19 05:01:00","post_content":"<!-- wp:html -->\n<iframe width=\"100%\" height=\"450\" src=https://www.ama.org/"https:////www.youtube.com//embed//jC2_4tq_wpo/" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen=\"\"><\/iframe>\n<!-- \/wp:html -->\n\n<!-- wp:paragraph {\"align\":\"center\"} -->\n<p class=\"has-text-align-center\"><em>Listen to the authors present their findings (source: November 2020 <\/em><a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////www.ama.org//events//webinar//jm-webinar-series-insights-for-managers///" target=\"_blank\"><em>JM Webinar<\/em><\/a><em>)<\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Many companies now turn to artificial intelligence (AI) to provide sales agents with coaching services that were originally offered by human managers. AI coaches are computer software solutions that leverage deep learning algorithms and cognitive speech analytics to analyze sales agents\u2019 conversations with customers and provide feedback to improve their job skills. Due to their high computation power, scalability, and cost efficiencies, AI coaches are more capable of generating data-driven training feedback than human managers. MetLife, an insurance giant adopted an AI coach named Cogito to offer training feedback to its call center frontline employees to improve customer service skills. Similarly, Zoom uses its AI coach, Chorus, to offer on-the-job training to its sales force. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>A new <em><a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/a><\/em> study explores the growing use of AI to coach sales agents to determine if there are any caveats that inhibit the effective use of this technology. Precisely because of the big data analytics power of AI coaches, one concern is that feedback generated by the technology may be too comprehensive for agents to assimilate and learn, especially for bottom-ranked agents. Further, despite their superior \u201chard\u201d data computation skills, AI coaches lack the \u201csoft\u201d interpersonal skills to communicate the feedback to agents, which is a key advantage of human managers. The lack of soft skills may result in agents\u2019 aversion to receiving feedback from AI coaches, thus hampering their learning and performance improvement. Indeed, the design of AI coaches often focuses on information generation, but less on learning by agents who may differ in learning abilities. Therefore, it would be na\u00efve to expect a simple, linear impact of AI coaches, relative to human managers, across heterogeneous sales agents.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>With this background, our research team addresses several research questions:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:list -->\n<ul><li>Which types of sales agents, bottom-, middle-, or top-ranked, benefit the most or the least from AI vis-\u00e0-vis human coaches? Is the incremental impact of AI coaches on agent performance heterogeneous in a non-linear manner?<\/li><li>What is the underlying mechanism? Does learning from the training feedback account for the impact of AI coaches?<\/li><li>Can an assemblage of AI and human coach qualities circumvent caveats and improve the sales performance of distinct types of agents?<\/li><\/ul>\n<!-- \/wp:list -->\n\n<!-- wp:paragraph -->\n<p>These questions are answered by a series of randomized field experiments with two fintech companies. In the first experiment, a total of 429 agents were randomly assigned to undergo on-the-job sales training with an AI or human coach. Results show that the incremental impact of the AI coach over human coach is heterogeneous in an inverted-U shape. While middle-ranked agents improve the most, both bottom- and top-ranked agents show limited incremental gains. Results suggest that this pattern is driven by a learning-based underlying mechanism. Bottom-ranked agents encounter the most severe information-overload with the AI coach. By contrast, top-ranked agents display the strongest AI aversion problem, which obstructs their incremental learning and performance.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>The slim improvement in bottom-ranked agents is an obstacle for AI coach adoption because they have the largest room and most acute needs to sharpen their job skills. Thus, we re-designed the AI coach by restricting the amount of feedback provided to bottom-ranked agents. With a separate sample of 100 bottom-ranked agents, the second experiment affirmed a substantial improvement in agent performance with a restricted AI coach. A third experiment tackled the limitations of either AI or human coaches alone by examining an AI\u2013human coach assemblage, wherein human managers communicate the feedback generated by the AI coach to the agents. A new sample of 451 bottom- and top-ranked agents were randomly assigned to the AI coach, human coach, and AI\u2013human coach assemblage conditions. The results suggest that both bottom- and top-ranked agents in the AI\u2013human coach assemblage condition enjoy higher performance than their counterparts in the AI coach alone or the human coach alone condition. In addition, bottom-ranked agents gain more performance improvement than top-ranked agents with the hybrid of AI and human coaching. Thus, this assemblage that harnesses the soft communication skills of human managers and hard data analytics power of AI coaches can effectively solve both problems faced by bottom- and top-ranked agents.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Managerially speaking, our research empowers companies to tackle the challenges they may encounter when investing in AI coaches to train distinct types of agents. We show that instead of simply applying an AI coach to the entire workforce, managers ought to prudently design it for targeted agents. Moreover, companies should be aware that AI and human coaches are not dichotomous choices. Instead, an assemblage between AI and human coaches engenders higher workforce productivity, thus allowing companies to reap substantially more value from their AI investments.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><a href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920956676/" target=\"_blank\" rel=\"noreferrer noopener\">Read the full article<\/a>. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>From:<\/strong> Xueming Luo, Shaojun Qin, Zheng Fang, and Zhe Qu, \u201c<a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920956676/" target=\"_blank\">Artificial Intelligence (AI) Coaches for Sales Agents: Caveats and Solutions<\/a>,\u201d <em><a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\">Journal of Marketing<\/a><\/em>.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Go to the <em><a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/a><\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:block {\"ref\":56764} \/-->\n\n<!-- wp:acf\/ama-curated-posts {\"id\":\"block_5fb5a0befda4f\",\"name\":\"acf\/ama-curated-posts\",\"data\":{\"title\":\"Related Articles\",\"_title\":\"field_5cf4b10fc4ef3\",\"picks\":[\"69029\",\"55601\",\"20842\"],\"_picks\":\"field_5cf4b131c4ef4\",\"columns\":\"1\",\"_columns\":\"field_5d65283c9b4d2\"},\"mode\":\"edit\"} \/-->","post_title":"Coaching Sales Agents? Use AI and Human Coaches Jointly","post_excerpt":"Deploying AI coaches for sales training? Read this study to learn which types of performers they work well with.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"coaching-sales-agents-use-ai-and-human-coaches-jointly","to_ping":"","pinged":"","post_modified":"2024-01-08 14:47:37","post_modified_gmt":"2024-01-08 20:47:37","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=70312","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":69029,"post_author":"88576","post_date":"2020-10-22 16:31:40","post_date_gmt":"2020-10-22 16:31:40","post_content":"<!-- wp:paragraph -->\n<p>Not long ago, artificial intelligence (AI) was the stuff of science fiction. Now it is changing how consumers eat, sleep, work, play, and even date. Consumers can interact with AI throughout the day, from Fitbit\u2019s fitness tracker and Alibaba\u2019s Tmall Genie smart speaker to Google Photo\u2019s editing suggestions and Spotify\u2019s music playlists. Given the growing ubiquity of AI in consumers\u2019 lives, marketers operate in organizations with a culture increasingly shaped by computer science. Software developers\u2019 objective of creating technical excellence, however, may not naturally align with marketers\u2019 objective of creating valued consumer experiences. For example, computer scientists often characterize algorithms as neutral tools evaluated on efficiency and accuracy, an approach that may overlook the social and individual complexities of the contexts in which AI is increasingly deployed. Thus, whereas AI can improve consumers\u2019 lives in very concrete and relevant ways, a failure to incorporate behavioral insight into technological developments may undermine consumers\u2019 experiences with AI.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>A new <em><a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/a> <\/em>article seeks to bridge these two perspectives. On one hand, our research team acknowledges the benefits that AI can provide to consumers. On the other hand, we build on and integrate sociological and psychological scholarship to examine the costs consumers can experience in their interactions with AI. After exposing the tension between these benefits and costs, we offer recommendations to guide managers and scholars investigating these challenges. In so doing, we respond to the call from the Marketing Science Institute to examine \u201cthe role of the human\/tech interface in marketing strategy\u201d and to offer more scholarly attention to situations where \u201ccustomers face an array of new devices with which to interact with firms, fundamentally altering the purchase experience\u201d (Marketing Science Institute 2018).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>We begin by presenting a framework that conceptualizes AI as an ecosystem with four capabilities: data capture, classification, delegation, and social. We focus on the consumer experience of these capabilities, including the tensions felt. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:image {\"align\":\"center\",\"id\":69031,\"width\":574,\"height\":475,\"sizeSlug\":\"large\"} -->\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large is-resized\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//10//AI-figure.png?w=815\%22 alt=\"\" class=\"wp-image-69031\" width=\"574\" height=\"475\"\/><\/figure><\/div>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph -->\n<p>We then discuss the experience of these tensions at a macro level, by exposing relevant and often explosive narratives in the sociological context, and at the micro level, by illustrating them with real-life examples grounded in relevant psychological literature. Using these insights, we provide marketers with recommendations regarding how to learn about and manage the tensions. Paralleling the joint emphasis on social and individual responses, we make recommendations outlining both the organizational learning in which firms should engage to lead the deployment of consumer AI and the concrete steps they should take to design improved consumer AI experiences. We close with a research agenda that cuts across the four consumer experiences and suggest ideas for how researchers might contribute new knowledge on this important topic. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><a href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920953847/" target=\"_blank\" rel=\"noreferrer noopener\">Read the full article<\/a>.  <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>From:<\/strong> Stefano Puntoni, Rebecca Walker Reczek, Markus Giesler, and Simona Botti, \u201c<a href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920953847/" target=\"_blank\" rel=\"noreferrer noopener\">Consumers and Artificial Intelligence: An Experiential Perspective<\/a>,\u201d <em><a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/a><\/em>.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Go to the <em><a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\">Journal of Marketing<\/a><\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:block {\"ref\":56764} \/-->\n\n<!-- wp:acf\/ama-curated-posts {\"id\":\"block_5f91adff105a0\",\"name\":\"acf\/ama-curated-posts\",\"data\":{\"title\":\"Related Articles\",\"_title\":\"field_5cf4b10fc4ef3\",\"picks\":[\"20842\",\"55601\",\"22954\"],\"_picks\":\"field_5cf4b131c4ef4\",\"columns\":\"1\",\"_columns\":\"field_5d65283c9b4d2\"},\"mode\":\"edit\"} \/-->","post_title":"[AI Insights] Understanding Consumer Reactions to AI","post_excerpt":"AI can create serious problems for consumers and societies. Here\u2019s how to fix that. ","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"consumers-and-artificial-intelligence-an-experiential-perspective","to_ping":"","pinged":"","post_modified":"2024-07-10 06:56:19","post_modified_gmt":"2024-07-10 11:56:19","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=69029","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":55601,"post_author":"133","post_date":"2020-03-18 17:34:37","post_date_gmt":"2020-03-18 17:34:37","post_content":"<!-- wp:html -->\n<iframe width=\"100%\" height=\"450\" src=https://www.ama.org/"https:////www.youtube.com//embed//L_jNkPp8kaw/" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe>\n<!-- \/wp:html -->\n\n<!-- wp:paragraph {\"align\":\"center\"} -->\n<p class=\"has-text-align-center\"><em>Listen to the authors present their findings (source: March 2020 <\/em><a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////www.ama.org//events//webinar//jm-webinar-series-insights-for-managers///" target=\"_blank\"><em>JM Webinar<\/em><\/a><em>)<\/em> <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>In 2018, over 1.7 million new cases of cancer were diagnosed in the United States and the cost of cancer care surpassed $147 billion. Many of these cases could have been prevented through regular cancer screening tests that open the door for early detection, more cost-effective treatment options, and better recovery prognosis. For example, regular screening reduces mortality rates for lung cancer by 28%, breast cancer by 24%, and liver cancer by 37%. Moreover, cancer screening can reduce the annual treatment cost for a patient by nearly $5,000.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Healthcare institutions rely on marketing interventions\u2014or direct-to-patient outreach\u2014to increase screening completion among at-risk patients. As an example, Johns Hopkins Hospital\u2019s cancer center uses emails, letters, seminars, and community events to encourage screening completion among patients. Yet, according to a recent article in the <em>LA Times<\/em>, \"just 4.2% of patients in the United States who are at high risk for lung cancer get screened for it \u2014 a figure seen as alarmingly low by those who work in the area of prevention.\" Could it be that the 1.7 million outreach interventions launched in 2015 and $123 million spent on prevention and education efforts go to waste?<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>A new study in the <em>Journal of Marketing<\/em> explores the efficacy of patient marketing. Our research team used a multi-period randomized field experiment conducted at University of Texas Southwestern Medical Center in Dallas with at-risk patients for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients were randomly assigned (1:1:1) to three different conditions\u2014usual care, outreach alone, or outreach with patient navigation. Usual care is the baseline condition where physicians offer preventive care recommendations at their discretion during a patient\u2019s usual care visits. Outreach alone and outreach with patient navigation provide two different levels of direct marketing efforts based on outreach mails, outreach calls, and customized motivational education by trained patient navigators. The focal outcome is the patient\u2019s screening completion status within 6 months (Period 1), 6-12 months (Period 2), and 12-18 months (Period 3) of the initial randomization. To incorporate patient heterogeneity, we included patients\u2019 demographics, health status, visit history, health system accessibility, neighborhood socioeconomic status, and prior screening compliance.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>We apply causal forests, a state-of-the-art development in machine learning, to discover that the effectiveness of outreach programs varies widely over time and across patients. For example, outreach programs in general are more effective for patients who are female, minority, in better health status, have a more frequent visit history, covered by medical-assistance insurance, reside in closer proximity to clinics, and reside in a more populated neighborhood. Outreach alone is more effective for patients who are younger, commute faster, and reside in a neighborhood with more public insurance coverage. In contrast, outreach with patient navigation is more effective for patients who are older and reside in a higher-income neighborhood.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Incorporating these patient-level differences in their responsiveness to outreach interventions and a well-established scheme of cost-benefit calculation that quantifies health benefits and financial costs associated with outreach interventions, we show that a targeted outreach program (that matches each patient to the optimal outreach type) improves the return on the randomized outreach program (the current state of practice) by 74%-96%, resulting in a gain of $1.6 million to $2 million. Collectively, we help healthcare practitioners in the following ways: <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:list -->\n<ul><li>We offer an understanding of how outreach effectiveness varies over time and by patient characteristics that are theoretically relevant and easily accessible to practitioners in their patient database. <br> <\/li><li>We offer a cost-benefit analysis approach for assessing individual-level return on marketing investments in the context of liver cancer screening outreach. <br><\/li><li>We contribute to the practice of individually and dynamically personalized healthcare marketing by providing a tool that can recommend the most suitable intervention for each patient.<\/li><\/ul>\n<!-- \/wp:list -->\n\n<!-- wp:paragraph -->\n<p>In sum, we combine the well-known marketing principle that all customers are different with advanced machine learning to show that personalized cancer outreach can save both lives and money. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><a href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920913025/" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Read the full article (opens in a new tab)\">Read the full article<\/a>. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>From: <\/strong>Yixing Chen, Ju-Yeon Lee, Shrihari Sridhar, Vikas Mittal, and Amit G. Singal, \u201c<a href=https://www.ama.org/"https:////doi.org//10.1177%2F0022242920913025/" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment (opens in a new tab)\">Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment<\/a>,\u201d<em> <a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Journal of Marketing (opens in a new tab)\">Journal of Marketing<\/a><\/em>. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Go to the <em><a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">Journal of Marketing<\/a><\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:acf\/ama-curated-posts {\"id\":\"block_5e72565bbf71e\",\"name\":\"acf\/ama-curated-posts\",\"data\":{\"title\":\"Related Articles\",\"_title\":\"field_5cf4b10fc4ef3\",\"picks\":[\"2647\",\"8634\",\"2576\"],\"_picks\":\"field_5cf4b131c4ef4\",\"columns\":\"1\",\"_columns\":\"field_5d65283c9b4d2\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:block {\"ref\":26858} \/-->","post_title":"Applying Machine Learning to the Challenge of Cancer Patient Outreach","post_excerpt":"A new JM study demonstrates that using machine learning to personalize marketing to cancer patients can improve ROI by 74%-96%. ","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"applying-machine-learning-to-the-challenge-of-cancer-patient-outreach","to_ping":"","pinged":"","post_modified":"2024-01-08 14:57:06","post_modified_gmt":"2024-01-08 20:57:06","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=55601","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":114355,"post_author":"90177","post_date":"2023-01-31 20:28:36","post_date_gmt":"2023-01-31 20:28:36","post_content":"<!-- wp:paragraph -->\n<p>For years, I\u2019ve been interested in the ever-blurring line between humans and technology in the market research industry, maintaining that there is a deft balance to be found. However, I\u2019m also cognizant of the fact just how (for the lack of a better word) time-poor people are, which is often the excuse or reason for failed technology implementations. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>I\u2019ll start out with a bold statement: if you\u2019re working in <a href=https://www.ama.org/"https:////www.ama.org//data-and-analytics-for-marketers///">market research and insights<\/a> and you're NOT willing to embrace and use the power of technology, you may not have a <a href=https://www.ama.org/"https:////www.ama.org//marketing-news//8-effective-ways-to-get-a-digital-marketing-promotion///">job in 10 years time. This is probably true for nearly any industry at this point in time. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>But it isn\u2019t that simple. And it\u2019s not just using <a href=https://www.ama.org/"https:////www.ama.org//marketing-news//empathy-and-technology-shaping-our-connection-in-the-future///">technology for the sake of it. We all know that the industry is growing and is evolving extremely quickly, but we still need the human aspect of market research to succeed: our ideas, our intuitions, our feelings and those uniquely human activities that no technology can duplicate (<a href=https://www.ama.org/"https:////www.ama.org//2020//10//22//consumers-and-artificial-intelligence-an-experiential-perspective///">not even AI<\/a>, yet). And most importantly, our curiosity.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>I\u2019m a researcher at heart, and I can honestly say that we (at Infotools) want to provide the kind of insights that are foundational to successful business decisions. We\u2019ve been doing this for over 30 years and it\u2019s truly remarkable when it happens. Interestingly, one common thread that connects these projects that I\u2019ve noticed over the years is \u201chuman interference\u201d. Technology, for all its potential, still needs people pulling certain strings, providing oversight to make sure ideal outcomes are met. Even in the age of \u201cAI\u201d people claim we are in, it\u2019s humans that bring the smarts, technology brings the muscle.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>I often use music, something I\u2019m very passionate about, to illustrate what this looks like. I play my own music (check out this<a href=https://www.ama.org/"https:////www.infotools.com//our-resources//this-one-is-for-the-research-and-insights-industry///" target=\"_blank\" rel=\"noreferrer noopener\"> medley to the market research<\/a> industry that I wrote in the midst of the pandemic), I write quirky lyrics, and I listen to a lot of music. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Today, there is software out there that can help musicians write and produce music, without even picking up an instrument. But we lose something in that process, don\u2019t we? Can technology really capture the authentic human element - the feelings, the pain, the joy - that we have when composing and writing music? <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>There\u2019s still no substitute for playing and grooving and jamming when it comes to bringing emotions into music and making it a reflection of the human soul. Yes, there are many things that technology can facilitate. Musicians don\u2019t need a big record label, contract, or lots of money, in order to produce and distribute their music. But for the best results, there is a blend of the very human aspect of music and the technology to take it to the next level.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>This is so similar to the insights industry. As market researchers, we are called upon to bring our views, expertise, and sometimes even our \u201cgut feelings\u201d to the insights we uncover. But the truth is that we are facing new, increased pressures for speed and efficiency that humans are ill-equipped to meet on our own. We need technology to do the things it is really good at, so that we can be allowed to do what we\u2019re good at. When the right technology speeds up our processes, we have more time to <em>really<\/em> examine the data and bring our skills to the table. After all, I believe most of us are in market research because we are curious people. Technology gives us back the extra time we need to feed that curiosity.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>There is a delicate balancing point where technology can help us manage the repetitive, mundane parts of our jobs. Smart technology can even point us in the right direction by telling us where to dig further, where more insights might lie within the data itself. But how can companies adopt solutions with both technology and people in mind?<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li><strong>Involve all stakeholders in the process.<\/strong><br>I\u2019m a big proponent of involving the people who will be on-the-ground using the technology from the beginning \u2013 even in the consideration stage. Rather than an IT team making a unilateral decision of what type of platform to use across departments, input from those who will be using it to improve their workflow is key. They must identify exactly where the roadblocks and inefficiencies are in current processes, and then identify how the technology can improve those sticking points. There\u2019s nothing more demoralizing than suddenly asking people to start using a solution that\u2019s been dumped on them, without their input.<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item -->\n<li><strong>Minimize workflow disruption.<\/strong><br>This is a huge consideration for companies looking to adopt new technology. Everyone clearly wants to minimize upheaval so deadlines can continue to be met and client expectations can continue to be delivered upon during technology implementation and onboarding. I say start small. Use the new solution for a single project, take those learnings and then build upon that, gracefully and in stages. I\u2019ve seen how taking a staged approach can streamline technology implementation, gain more buy-in from entire teams and, ultimately, deliver better results.<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item -->\n<li><strong>Leadership and project support.<\/strong><br>You will definitely need a senior leadership sponsor and a dedicated project manager for the implementation of any new technology. Not only does this nurture a sense of responsibility, but senior level buy-in can help the whole company become more receptive to change. Find someone who understands that you need the efficiencies that technology brings, but that also knows change doesn\u2019t happen overnight. A project manager can help set expectations and integrate the tool into the business, rather than just presenting a tool to users with no guidance. Over-communication among all parties is key. Once you\u2019re sick of communicating a message, it\u2019s only then that it\u2019s starting to resonate with people.<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item -->\n<li><strong>Answer the right questions for your business.<\/strong><br>Where are the sticking points in my current workflow when it comes to delivering quality insights, faster and at a better price? Where can the technology implementation impact these key deliverables? What level of disruption am I comfortable with? Do my current team members have the right skills and appetite for a new solution? Can I motivate and upskill them if needed?<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list -->\n\n<!-- wp:paragraph -->\n<p>Technology can give us back the \u201ccool\u201d part of market research \u2013 giving us the space to do things that feed our curiosity and find out what\u2019s going on behind the data. Insights are exciting. They help businesses make better decisions and move forward in a landscape that is undergoing constant, fast-paced change. The data we uncover can, in many ways, contribute to the greater good of everyone. Far from replacing our uniquely human contributions to the insights ecosystem, technology can support us in making our jobs faster, better and easier. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Technology implemented and adopted well in your organization won\u2019t just help you do more of what you do best, but also do more of what you really want to be doing. Think it over. <\/p>\n<!-- \/wp:paragraph -->","post_title":"Think It Over: Technology Adoption Done Well Allows Humans to Do What We Do Best","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"think-it-over-technology-adoption-done-well-allows-humans-to-do-what-we-do-best","to_ping":"","pinged":"","post_modified":"2024-01-22 13:40:16","post_modified_gmt":"2024-01-22 19:40:16","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?post_type=ama_marketing_news&p=114355","menu_order":0,"post_type":"ama_marketing_news","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":62562,"post_author":"13094","post_date":"2020-07-06 17:39:17","post_date_gmt":"2020-07-06 17:39:17","post_content":"<!-- wp:heading -->\n<h2>The worldwide market size for chatbots is expected to reach more than $1.3 billion by 2024, an annual growth rate of 24.3%<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":50} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:media-text {\"mediaId\":62563,\"mediaLink\":\"https:\/\/www.ama.org\/?attachment_id=62563\",\"mediaType\":\"image\"} -->\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//06//image-7.png?w=500\%22 alt=\"robot in speech bubble\" class=\"wp-image-62563\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph {\"placeholder\":\"Content\u2026\",\"textColor\":\"black\",\"customFontSize\":23} -->\n<p style=\"font-size:23px\" class=\"has-text-color has-black-color\"><strong>88% <\/strong>of self-identified high-performing marketers say they lead customer experience initiatives across their organizations, compared to <strong>68%<\/strong> of under-performers. Innovation and real-time customer engagement are the top two priorities for marketers in 2020.<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:spacer {\"height\":50} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:media-text {\"mediaPosition\":\"right\",\"mediaId\":62564,\"mediaLink\":\"https:\/\/www.ama.org\/?attachment_id=62564\",\"mediaType\":\"image\"} -->\n<div class=\"wp-block-media-text alignwide has-media-on-the-right is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//06//image-8.png?w=473\%22 alt=\"baby robot in stroller\" class=\"wp-image-62564\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph {\"placeholder\":\"Content\u2026\",\"customFontSize\":60} -->\n<p style=\"font-size:60px\"><strong>186%<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"placeholder\":\"Content\u2026\",\"customFontSize\":23} -->\n<p style=\"font-size:23px\">Increase in general AI adoption since 2018<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:spacer {\"height\":50} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"align\":\"center\",\"textColor\":\"blue\",\"customFontSize\":40} -->\n<p style=\"font-size:40px\" class=\"has-text-color has-text-align-center has-blue-color\"><strong>78% of marketers describe their <\/strong><br><strong>customer engagement as data-driven<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":50} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:columns -->\n<div class=\"wp-block-columns\"><!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:paragraph {\"align\":\"center\",\"customTextColor\":\"#9fdbf0\",\"customFontSize\":50} -->\n<p style=\"color:#9fdbf0;font-size:50px\" class=\"has-text-color has-text-align-center\"><strong>67%<\/strong><\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column -->\n\n<!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:paragraph {\"customFontSize\":25} -->\n<p style=\"font-size:25px\">U.S. millennials that said they are likely to purchase products and services from brands using a chatbot.<\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column --><\/div>\n<!-- \/wp:columns -->\n\n<!-- wp:columns -->\n<div class=\"wp-block-columns\"><!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:paragraph {\"align\":\"center\",\"customTextColor\":\"#2c629a\",\"customFontSize\":50} -->\n<p style=\"color:#2c629a;font-size:50px\" class=\"has-text-color has-text-align-center\"><strong>68%<\/strong><\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column -->\n\n<!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:paragraph {\"customFontSize\":25} -->\n<p style=\"font-size:25px\">Percentage of customers that say bot-driven messaging is the most convenient way to contact a business.<\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column --><\/div>\n<!-- \/wp:columns -->\n\n<!-- wp:columns -->\n<div class=\"wp-block-columns\"><!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:paragraph {\"align\":\"center\",\"customFontSize\":50} -->\n<p style=\"font-size:50px\" class=\"has-text-align-center\"><strong>69%<\/strong><\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column -->\n\n<!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:paragraph {\"customFontSize\":25} -->\n<p style=\"font-size:25px\">Consumers that say they feel more confident about brands that they can message.<\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column --><\/div>\n<!-- \/wp:columns -->\n\n<!-- wp:spacer {\"height\":75} -->\n<div style=\"height:75px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:media-text {\"mediaPosition\":\"right\",\"mediaId\":62567,\"mediaLink\":\"https:\/\/www.ama.org\/?attachment_id=62567\",\"mediaType\":\"image\"} -->\n<div class=\"wp-block-media-text alignwide has-media-on-the-right is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//06//image-10.png?w=440\%22 alt=\"\" class=\"wp-image-62567\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:heading {\"level\":1} -->\n<h1>Marketers planning to increase use over the next year<\/h1>\n<!-- \/wp:heading -->\n\n<!-- wp:paragraph -->\n<p><\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:spacer {\"height\":75} -->\n<div style=\"height:75px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:columns -->\n<div class=\"wp-block-columns\"><!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:image {\"align\":\"center\",\"id\":62568,\"sizeSlug\":\"large\"} -->\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//06//image-11.png?w=517\%22 alt=\"47%\" class=\"wp-image-62568\"\/><\/figure><\/div>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph {\"align\":\"center\"} -->\n<p class=\"has-text-align-center\">Shoppers who are open to purchasing items <br>through a bot<\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column -->\n\n<!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:image {\"align\":\"center\",\"id\":62569,\"sizeSlug\":\"large\"} -->\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//06//image-12.png?w=532\%22 alt=\"37%\" class=\"wp-image-62569\"\/><\/figure><\/div>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph {\"align\":\"center\"} -->\n<p class=\"has-text-align-center\">Customers who would consider buying items on a social network instead of a company's website<\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column -->\n\n<!-- wp:column -->\n<div class=\"wp-block-column\"><!-- wp:image {\"align\":\"center\",\"id\":62570,\"sizeSlug\":\"large\"} -->\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img src=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//06//image-13.png?w=536\%22 alt=\"57%\" class=\"wp-image-62570\"\/><\/figure><\/div>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph {\"align\":\"center\"} -->\n<p class=\"has-text-align-center\">Customers who are interested in getting information from <br>bots when browsing a business' website<\/p>\n<!-- \/wp:paragraph --><\/div>\n<!-- \/wp:column --><\/div>\n<!-- \/wp:columns -->\n\n<!-- wp:spacer {\"height\":50} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"align\":\"center\",\"id\":62565,\"sizeSlug\":\"large\",\"linkDestination\":\"media\"} -->\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><a href=https://www.ama.org/"https:////www.ama.org//wp-content//uploads//2020//06//image-9.png/">\"\"

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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