{"id":38866,"date":"2017-03-23T15:00:10","date_gmt":"2017-03-23T14:00:10","guid":{"rendered":"https:\/\/blog.buzzoole.com\/?p=38866"},"modified":"2018-05-28T15:34:05","modified_gmt":"2018-05-28T13:34:05","slug":"big-data-artificial-intelligence-marketing-strategy","status":"publish","type":"post","link":"https:\/\/blog.buzzoole.com\/en\/natural-language-processing\/big-data-artificial-intelligence-marketing-strategy\/","title":{"rendered":"How to Apply Big Data and Artificial Intelligence to Your Marketing Strategy"},"content":{"rendered":"

The marketing industry is rapidly evolving. It’s at a new phase where Big Data and AI (Artificial Intelligence) are driving strategy development and decision making. Because conventional analytics scale at a gradual pace, marketers are beginning to utilise AI to analyse the large volume of unstructured data to improve marketing research, forecasting accuracy and campaign experiences.<\/span><\/p>\n

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A research<\/span><\/a> of CMOs from the US, UK, and China from companies with more than $500 million in revenue revealed that around two thirds of these think <\/span>Artificial Intelligence will play a significant role<\/b> in their future marketing operations. But only a third claimed to have good knowledge of how it is actually implemented. <\/span><\/p>\n

Another study of B2B marketers at managerial or higher level positions at organisations with 250+ employees <\/span>revealed the main benefits<\/span><\/a> of AI implementation were in marketing. <\/span><\/p>\n

\"Benefits<\/a><\/p>\n

Today, AI tracks and records every <\/span>interaction<\/b> a consumer has with a website, email, or social media platform, while <\/span>machine learning<\/b> can gather the incoming data in real time, and deliver personalised experiences to each person in immediately.<\/span><\/p>\n

Here’s a breakdown of how this process works (courtesy of <\/span>Genii Analytics<\/span><\/a>):<\/span><\/p>\n

\"Artificial<\/a><\/p>\n

What you see is a simplified chain that makes sense of Big Data when several independent agents interact with one another. The AI-powered funnel doesn’t work in a vacuum; it responds to the feedback received in its environment from both AI agents and humans. <\/span><\/p>\n

With greater insight into socio-economic data, geographic patterns, and customer demographics, marketers will be able to develop proactive campaigns that optimise output. And the good news is that you don’t have to learn about AI and machine learning; off-the-shelf algorithms are available for companies to use. <\/span><\/p>\n

Range of AI & Big Data Possibilities for Marketers<\/h2>\n

Several companies are already leveraging Artificial Intelligence and Big Data to improve campaign reach. The aggregated information is already being used in the following scenarios:<\/span><\/p>\n

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  1. Customer Service<\/b><\/li>\n<\/ol>\n

    The key application of Artificial Intelligence and Big Data is combining the technologies of natural language processing and machine learning to build chatbots (intelligent assistants). These are interactive customer service reps that communicate with customers naturally and assist them in solving problems and accessing specific information. An example is <\/span>IBM’s Virtual Agent Watson<\/span><\/a>. It allows marketers to engage with customers by tackling usual questions and interacting in a personal manner when responding to requests on any channel. It’s programmed to learn about a company and its customers.<\/span><\/p>\n

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    1. Consumers’ Feelings<\/b><\/li>\n<\/ol>\n

      As user-generated content and social media platforms started producing their own Big Data, marketers began to analyse these vast data set for insights but they faced a new challenge – knowing if people are talking about you is less useful than knowing their sentiment towards you. That’s where semantic analysis comes into the picture. It provides context to certain behaviors and actions, enabling you to cluster data elements. <\/span><\/p>\n

      For instance, an AI tool will know that:<\/span><\/p>\n