What is Machine Learning Going to Do – Artificial Intelligence is Here in Digital Marketing!

While computer scientists have been advertising Artificial Intelligence for more than half a century, the technology is just beginning to uncover its true potential. Despite all the hype, machine learning, deep learning, computer vision and natural language processing have, silently, become entrenched in many people’s daily routines. These innovations have brought with them new abilities to automate tasks, analyze data and connect dots in our lives.

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Without even understanding it, people have become habituated to interacting with the help of Artificial Intelligence.

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing.” — Larry Page, Co-Founder of Google

Several industry specialists have reached an agreement that AI in marketing and sales will eventually transform its work. A survey in July 2017 commissioned by Emarsys and conducted by Forrester Research found that roughly 8 out of 10 retail marketers worldwide believed that AI could revolutionize the marketer’s role, and had the potential to advance efficiency and usefulness, make marketing more tactical and enable staff to focus on value-generating tasks.

A survey of June 2017 entitled “Attitudes Toward the Effect of Artificial Intelligence (AI) on Marketing” found that about 86% of respondents voted for that AI “Makes marketing teams more efficient”, whilst, 79% of respondents voted for “changes the role of marketing toward more strategic work”.


The Advantages of Using Artificial Intelligence in Digital Marketing

    • Enhanced efficiency in learning the underlying relationships between disparate datasets over traditional methods, which require complex modeling and coding.
    • Upgraded accuracy for evidently defined processes that, otherwise, involve a lot of manual processing.
    • Ability to handle a huge amount of data with several attributes, for instance, customer behavior data, multichannel and multi-device data, complex product data and fraud detection.
    • More granularity in orchestration such as customer segmentation, sentiment analysis and personalization.
    • Frequent algorithm refreshes such as more than a few times a day to capture the transient fluctuations in customer and market behavior.
    • Brings new insights and better data analysis. Agencies and other consultants are stepping up to the plate, beefing up their technical resources and forging technology partnerships to help their clients navigate the dizzying array of AI and marketing solutions.
    • Makes an organization more creative best practices for marketers include: clearly defining business goals, thoroughly understanding the technology, planning, having the right data and using Artificial Intelligence ethically.
    • Help our organization make better management decisions A robust ecosystem of prepackaged APIs, open-source software and cloud-based platforms is helping accelerate Artificial Intelligence adoption, bringing new capabilities to speed-up, personalize marketing campaigns and scale in more economical ways.


How Are Marketers Using AI Today?

Marketers are using the systems in a variety of ways. Here, are various ways that eMarketers can benefit from it:

  • Marketing intelligence: Artificial Intelligence systems outshine at analyzing and crunching massive volumes of data from dissimilar sources, including data management platforms (DMPs), data warehouses, data lakes, and other repositories of structured and unstructured data. They can gather information from several inputs, find relationships, connect the dots and make predictions in ways that are not humanly possible. Marketers are using these competencies for the improvement of business intelligence, marketing study and predicting precision
  • Lead generation and customer attainment: Artificial Intelligence powered solutions are helping the marketers to generate and score sales leads, along with the goal of attaining more customers. Many of these systems take in the advantage of machine knowledge and predictive analytics
  • Marketing optimization: This application of Artificial Intelligence technology supports marketers to make optimal media buying and content placement selections. Applications include: programmatic advertising, and campaign optimization and measurement
  • Customer experience management: Artificial intelligence and several related technologies are used to boost the client experience and assists companies for better understanding and managing relationships with their clientele. Applications include: Artificial Intelligence-enhanced call center technology, bots and virtual digital assistants, smarter search interfaces and recommender systems that can help with many different types of customer support
  • Content creation and dynamic creative: Companies in a variety of industries are turning to Artificial Intelligence-powered content generators to create on-demand advertisements, articles, summaries, promotional material, websites, and other published content constructed on data inputs and further analytics. These include: robotic writing, image or video production tools that generate definite content for targeted audiences based on data and learning algorithms
  • Brand building: Artificial Intelligence technologies are being used in a variety of modified campaigns to strengthen and emphasize brand messaging. This usually involves data analysis that uncovers insights related to a brand positioning. It is generally viewed as an emerging area for Artificial Intelligence in marketing
  • Product recommendation. It frequently tests and updates the recommendation model to best match the customer interest — with higher accuracy and more granularity, by including more customer attributes such as behavior, demographics, preferences, and merchandise interest such as purchases, and browsing history
  • Product search. Artificial Intelligence incorporates multiple data sources to identify customer behavior, interest, and intent and is more sophisticated than the existing search framework using collaborative, or content-based filtering. Artificial Intelligence also opens the doors for new types of functionality such as image and natural language search
  • Customer Segmentation — This is often a labor-intensive task where many customer attributes are manually tagged and tend to be the broad-brush approach. Artificial Intelligence increases the granularity of attributes and the accuracy of assigning those to customers and make the process more automated. Additionally, Artificial Intelligence suggests clusters of attributes thereby creates new segments that are not clear to humans reviewing the same customer data
  • Product Categorization — Artificial Intelligence automatically categorizes products based on attributes and natural-language description and includes images and video. This improves the efficiency and accuracy of the task and makes a highly granular image/video categorization possible; thus, the content search is easier for business users and product search is easier for customers
  • Fraud Detection– Artificial Intelligence learns the transaction attributes and activity patterns associated with fraud in a much shorter time than rule engines and recognizes high-risk factors and combination. Such insight helps fraud managers to rapidly configure operative fraud detection and prevention models that are relevant to their business.

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