Agencies Grow Intellectual Property Assets With Tech

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Register for this webinar to learn how to manage and support an employee advocacy program from planning through success. On top of making ads for marketers, agencies have long created products of their own, from Eos lip balm by Anomaly to the HurryCane walking stick from Minneapolis direct-response shop Marketing Architects. Now agencies are pouring resources into developing a very different kind of intellectual property: marketing technology. As brands spend more money to reach the right consumers with the right messages in digital media, agencies are looking to go after those dollars by creating the tech to help, such as tools to gauge consumer sentiment or optimize campaigns while they're still running. For marketers that buy in, shops often charge a monthly, subscription-like service or tech fee, a departure from the common per-head compensation model that agencies use to charge clients for traditional services.


Publicis and Microsoft Link Up Products for Data Push

WSJ.com: WSJD - Technology

Publicis Groupe and Microsoft Corp. have decided to expand a pilot initiative that combined the ad giant's Cosmos data product with Microsoft's Azure cloud service. The partnership highlights Publicis's push to generate more revenue from intellectual property and compete with consulting companies, as well as Microsoft's ambitions to expand its cloud-based offerings. Cosmos gleans people's behavioral data for marketers and constantly updates individual customer identities that are stored in the Microsoft cloud. The marketer uses that customer information to create and send targeted emails and digital ads to individuals. "How customers behave in real time changes how we market to them," said Shannon Denton, chief strategy officer of Publicis digital agency group SapientRazorfish.


Brands toe the AI line for better consumer experience

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Strategising marketing, advertising and sales efforts is no longer a Herculean task for brands and retailers, with many elevating consumer experiences with the help of artificial intelligence, machine learning and big data. Since customers in today's digital world demand hyper-personalised content and tailored experiences from their favourite brands, several e-tailers are perfecting their moves. "Advanced AI capabilities, that we have only known through science-fiction movies, such as chatbots and virtual shopping assistants, are quite real now, and many online retailers are already using them to serve customers in a better, more interactive manner," Sudeshna Datta, co-founder and Executive Vice-President, AbsolutData, an analytics firm, told BusinessLine. While Alibaba is using AI to create personalised shopping experiences for its consumers, Amazon has introduced and perfected some of the key use-cases for AI, machine learning, and analytics in e-commerce marketing, in addition to developing innovative communication strategies. Flipkart, too, has stepped on the gas, and is keen to utilise its e-commerce data on consumer behaviour that is has collated over the past 10 years.


Understanding artificial intelligence for retail customers

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Retailers are utilizing AI systems to develop better personalization, grow their audiences and analyze unstructured data. For example, the outdoor and equipment apparel company, The North Face, is using an interactive online shopping experience powered by IBM Watson cognitive computing technology.


Artificial Intelligence: Shaping the future of marketing - The Financial Express

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Don't you just love how Siri, Alexa or Google Assistant answer all of your questions? Isn't it interesting when you receive prompt responses on some website's chat box no matter what the time is? Yes, marketers know this interests you and this is why AI holds fascinating possibilities in the future for marketing. Using AI, brands are studying user needs, behaviours and predicting their future actions. AI's competence to handle a large volume of data, and finding reasons and patterns among them has given marketers exceptional ability to make data-driven predictions and decisions.